diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 00000000..0f4298b7 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1 @@ +* @temporalio/sdk diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ff07e2af..bfb1353e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,31 +12,50 @@ jobs: strategy: fail-fast: true matrix: - python: ["3.8", "3.12"] - os: [ubuntu-latest, macos-latest, windows-latest] - runs-on: ${{ matrix.os }} + python: ["3.9", "3.12"] + os: [ubuntu-latest, macos-intel, macos-arm, windows-latest] + include: + - os: macos-intel + runsOn: macos-13 + - os: macos-arm + runsOn: macos-14 + runs-on: ${{ matrix.runsOn || matrix.os }} steps: + - uses: astral-sh/setup-uv@v5 - name: Print build information run: "echo head_ref: ${{ github.head_ref }}, ref: ${{ github.ref }}, os: ${{ matrix.os }}, python: ${{ matrix.python }}" - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 with: submodules: recursive - - uses: actions/setup-python@v1 + - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python }} - # Using fixed Poetry version until - # https://github.com/python-poetry/poetry/pull/7694 is fixed - - run: python -m pip install --upgrade wheel "poetry==1.4.0" poethepoet - - run: poetry install --with pydantic --with dsl + - run: uv tool install poethepoet + - run: uv sync --group=dsl --group=encryption --group=trio-async - run: poe lint - - run: poe test -s -o log_cli_level=DEBUG - - run: poe test -s -o log_cli_level=DEBUG --workflow-environment time-skipping + - run: mkdir junit-xml + - run: poe test -s --junit-xml=junit-xml/${{ matrix.python }}--${{ matrix.os }}.xml + - run: poe test -s --workflow-environment time-skipping --junit-xml=junit-xml/${{ matrix.python }}--${{ matrix.os }}--time-skipping.xml + # This must remain the last step since it downgrades pydantic + - name: Uninstall pydantic + shell: bash + run: | + echo y | uv run pip uninstall pydantic + echo y | uv run pip uninstall pydantic-core + uv run pip install pydantic==1.10 + poe test -s --junit-xml=junit-xml/${{ matrix.python }}--${{ matrix.os }}--pydantic-v1.xml tests/pydantic_converter_v1/workflow_test.py # On latest, run gevent test - name: Gevent test if: ${{ matrix.python == '3.12' }} run: | - poetry install --with gevent - poetry run python gevent_async/test/run_combined.py - + uv sync --group gevent + uv run gevent_async/test/run_combined.py + - name: Upload junit-xml artifacts + uses: actions/upload-artifact@v4 + if: always() + with: + name: junit-xml--${{github.run_id}}--${{github.run_attempt}}--${{ matrix.python }}--${{ matrix.os }} + path: junit-xml + retention-days: 14 diff --git a/.gitignore b/.gitignore index 033df5fb..41afe5f8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,5 @@ .venv +.idea __pycache__ +.vscode +.DS_Store diff --git a/README.md b/README.md index df72efc7..5c1941d1 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,26 @@ # Temporal Python SDK Samples -This is the set of Python samples for the [Python SDK](https://github.com/temporalio/sdk-python). +This is a collection of samples showing how to use the [Python SDK](https://github.com/temporalio/sdk-python). ## Usage Prerequisites: -* Python >= 3.8 -* [Poetry](https://python-poetry.org) -* [Local Temporal server running](https://docs.temporal.io/application-development/foundations#run-a-development-cluster) +* [uv](https://docs.astral.sh/uv/) +* [Temporal CLI installed](https://docs.temporal.io/cli#install) +* [Local Temporal server running](https://docs.temporal.io/cli/server#start-dev) + +The SDK requires Python >= 3.9. You can install Python using uv. For example, + + uv python install 3.13 With this repository cloned, run the following at the root of the directory: - poetry install + uv sync -That loads all required dependencies. Then to run a sample, usually you just run it in Python. For example: +That loads all required dependencies. Then to run a sample, usually you just run it under uv. For example: - poetry run python hello/hello_activity.py + uv run hello/hello_activity.py Some examples require extra dependencies. See each sample's directory for specific instructions. @@ -52,11 +56,19 @@ Some examples require extra dependencies. See each sample's directory for specif * [hello_signal](hello/hello_signal.py) - Send signals to a workflow. * [activity_worker](activity_worker) - Use Python activities from a workflow in another language. +* [bedrock](bedrock) - Orchestrate a chatbot with Amazon Bedrock. +* [cloud_export_to_parquet](cloud_export_to_parquet) - Set up schedule workflow to process exported files on an hourly basis +* [context_propagation](context_propagation) - Context propagation through workflows/activities via interceptor. * [custom_converter](custom_converter) - Use a custom payload converter to handle custom types. * [custom_decorator](custom_decorator) - Custom decorator to auto-heartbeat a long-running activity. +* [custom_metric](custom_metric) - Custom metric to record the workflow type in the activity schedule to start latency. * [dsl](dsl) - DSL workflow that executes steps defined in a YAML file. * [encryption](encryption) - Apply end-to-end encryption for all input/output. * [gevent_async](gevent_async) - Combine gevent and Temporal. +* [langchain](langchain) - Orchestrate workflows for LangChain. +* [message_passing/introduction](message_passing/introduction/) - Introduction to queries, signals, and updates. +* [message_passing/safe_message_handlers](message_passing/safe_message_handlers/) - Safely handling updates and signals. +* [message_passing/update_with_start/lazy_initialization](message_passing/update_with_start/lazy_initialization/) - Use update-with-start to update a Shopping Cart, starting it if it does not exist. * [open_telemetry](open_telemetry) - Trace workflows with OpenTelemetry. * [patching](patching) - Alter workflows safely with `patch` and `deprecate_patch`. * [polling](polling) - Recommended implementation of an activity that needs to periodically poll an external resource waiting its successful completion. @@ -64,15 +76,16 @@ Some examples require extra dependencies. See each sample's directory for specif * [pydantic_converter](pydantic_converter) - Data converter for using Pydantic models. * [schedules](schedules) - Demonstrates a Workflow Execution that occurs according to a schedule. * [sentry](sentry) - Report errors to Sentry. +* [trio_async](trio_async) - Use asyncio Temporal in Trio-based environments. +* [updatable_timer](updatable_timer) - A timer that can be updated while sleeping. * [worker_specific_task_queues](worker_specific_task_queues) - Use unique task queues to ensure activities run on specific workers. * [worker_versioning](worker_versioning) - Use the Worker Versioning feature to more easily version your workflows & other code. - ## Test Running the tests requires `poe` to be installed. - python -m pip install poethepoet + uv tool install poethepoet Once you have `poe` installed you can run: diff --git a/activity_worker/README.md b/activity_worker/README.md index 8b821281..0d904313 100644 --- a/activity_worker/README.md +++ b/activity_worker/README.md @@ -8,6 +8,6 @@ First run the Go workflow worker by running this in the `go_workflow` directory Then in another terminal, run the sample from this directory: - poetry run python activity_worker.py + uv run activity_worker.py The Python code will invoke the Go workflow which will execute the Python activity and return. \ No newline at end of file diff --git a/bedrock/README.md b/bedrock/README.md new file mode 100644 index 00000000..42a1f4d5 --- /dev/null +++ b/bedrock/README.md @@ -0,0 +1,25 @@ +# AI Chatbot example using Amazon Bedrock + +Demonstrates how Temporal and Amazon Bedrock can be used to quickly build bulletproof AI applications. + +## Samples + +* [basic](basic) - A basic Bedrock workflow to process a single prompt. +* [signals_and_queries](signals_and_queries) - Extension to the basic workflow to allow multiple prompts through signals & queries. +* [entity](entity) - Full multi-Turn chat using an entity workflow.. + +## Pre-requisites + +1. An AWS account with Bedrock enabled. +2. A machine that has access to Bedrock. +3. A local Temporal server running on the same machine. See [Temporal's dev server docs](https://docs.temporal.io/cli#start-dev-server) for more information. + +These examples use Amazon's Python SDK (Boto3). To configure Boto3 to use your AWS credentials, follow the instructions in [the Boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html). + +## Running the samples + +For these sample, the optional `bedrock` dependency group must be included. To include, run: + + uv sync --group bedrock + +There are 3 Bedrock samples, see the README.md in each sub-directory for instructions on running each. \ No newline at end of file diff --git a/bedrock/__init__.py b/bedrock/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/bedrock/basic/README.md b/bedrock/basic/README.md new file mode 100644 index 00000000..88cae861 --- /dev/null +++ b/bedrock/basic/README.md @@ -0,0 +1,10 @@ +# Basic Amazon Bedrock workflow + +A basic Bedrock workflow. Starts a workflow with a prompt, generates a response and ends the workflow. + +To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory: + +1. Run the worker: `uv run run_worker.py` +2. In another terminal run the client with a prompt: + + e.g. `uv run send_message.py 'What animals are marsupials?'` \ No newline at end of file diff --git a/bedrock/basic/__init__.py b/bedrock/basic/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/bedrock/basic/run_worker.py b/bedrock/basic/run_worker.py new file mode 100644 index 00000000..fee8aa5d --- /dev/null +++ b/bedrock/basic/run_worker.py @@ -0,0 +1,35 @@ +import asyncio +import concurrent.futures +import logging + +from temporalio.client import Client +from temporalio.worker import Worker +from workflows import BasicBedrockWorkflow + +from bedrock.shared.activities import BedrockActivities + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + activities = BedrockActivities() + + # Run the worker + with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor: + worker = Worker( + client, + task_queue="bedrock-task-queue", + workflows=[BasicBedrockWorkflow], + activities=[activities.prompt_bedrock], + activity_executor=activity_executor, + ) + await worker.run() + + +if __name__ == "__main__": + print("Starting worker") + print("Then run 'python send_message.py \"\"'") + + logging.basicConfig(level=logging.INFO) + + asyncio.run(main()) diff --git a/bedrock/basic/send_message.py b/bedrock/basic/send_message.py new file mode 100644 index 00000000..1b4cc995 --- /dev/null +++ b/bedrock/basic/send_message.py @@ -0,0 +1,29 @@ +import asyncio +import sys + +from temporalio.client import Client +from workflows import BasicBedrockWorkflow + + +async def main(prompt: str) -> str: + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + + # Start the workflow + workflow_id = "basic-bedrock-workflow" + handle = await client.start_workflow( + BasicBedrockWorkflow.run, + prompt, # Initial prompt + id=workflow_id, + task_queue="bedrock-task-queue", + ) + return await handle.result() + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python send_message.py ''") + print("Example: python send_message.py 'What animals are marsupials?'") + else: + result = asyncio.run(main(sys.argv[1])) + print(result) diff --git a/bedrock/basic/workflows.py b/bedrock/basic/workflows.py new file mode 100644 index 00000000..9968704d --- /dev/null +++ b/bedrock/basic/workflows.py @@ -0,0 +1,24 @@ +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from bedrock.shared.activities import BedrockActivities + + +@workflow.defn +class BasicBedrockWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + + workflow.logger.info("Prompt: %s" % prompt) + + response = await workflow.execute_activity_method( + BedrockActivities.prompt_bedrock, + prompt, + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info("Response: %s" % response) + + return response diff --git a/bedrock/entity/README.md b/bedrock/entity/README.md new file mode 100644 index 00000000..5f53840d --- /dev/null +++ b/bedrock/entity/README.md @@ -0,0 +1,19 @@ +# Multi-turn chat with Amazon Bedrock Entity Workflow + +Multi-Turn Chat using an Entity Workflow. The workflow runs forever unless explicitly ended. The workflow continues as new after a configurable number of chat turns to keep the prompt size small and the Temporal event history small. Each continued-as-new workflow receives a summary of the conversation history so far for context. + +To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory: + +1. Run the worker: `uv run run_worker.py` +2. In another terminal run the client with a prompt. + + Example: `uv run send_message.py 'What animals are marsupials?'` + +3. View the worker's output for the response. +4. Give followup prompts by signaling the workflow. + + Example: `uv run send_message.py 'Do they lay eggs?'` +5. Get the conversation history summary by querying the workflow. + + Example: `uv run get_history.py` +6. To end the chat session, run `uv run end_chat.py` diff --git a/bedrock/entity/__init__.py b/bedrock/entity/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/bedrock/entity/end_chat.py b/bedrock/entity/end_chat.py new file mode 100644 index 00000000..49125306 --- /dev/null +++ b/bedrock/entity/end_chat.py @@ -0,0 +1,22 @@ +import asyncio +import sys + +from temporalio.client import Client +from workflows import EntityBedrockWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + + workflow_id = "entity-bedrock-workflow" + + handle = client.get_workflow_handle_for(EntityBedrockWorkflow.run, workflow_id) + + # Sends a signal to the workflow + await handle.signal(EntityBedrockWorkflow.end_chat) + + +if __name__ == "__main__": + print("Sending signal to end chat.") + asyncio.run(main()) diff --git a/bedrock/entity/get_history.py b/bedrock/entity/get_history.py new file mode 100644 index 00000000..1600886e --- /dev/null +++ b/bedrock/entity/get_history.py @@ -0,0 +1,31 @@ +import asyncio + +from temporalio.client import Client +from workflows import EntityBedrockWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + workflow_id = "entity-bedrock-workflow" + + handle = client.get_workflow_handle(workflow_id) + + # Queries the workflow for the conversation history + history = await handle.query(EntityBedrockWorkflow.get_conversation_history) + + print("Conversation History") + print( + *(f"{speaker.title()}: {message}\n" for speaker, message in history), sep="\n" + ) + + # Queries the workflow for the conversation summary + summary = await handle.query(EntityBedrockWorkflow.get_summary_from_history) + + if summary is not None: + print("Conversation Summary:") + print(summary) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/bedrock/entity/run_worker.py b/bedrock/entity/run_worker.py new file mode 100644 index 00000000..3e3b1e64 --- /dev/null +++ b/bedrock/entity/run_worker.py @@ -0,0 +1,35 @@ +import asyncio +import concurrent.futures +import logging + +from temporalio.client import Client +from temporalio.worker import Worker +from workflows import EntityBedrockWorkflow + +from bedrock.shared.activities import BedrockActivities + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + activities = BedrockActivities() + + # Run the worker + with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor: + worker = Worker( + client, + task_queue="bedrock-task-queue", + workflows=[EntityBedrockWorkflow], + activities=[activities.prompt_bedrock], + activity_executor=activity_executor, + ) + await worker.run() + + +if __name__ == "__main__": + print("Starting worker") + print("Then run 'python send_message.py \"\"'") + + logging.basicConfig(level=logging.INFO) + + asyncio.run(main()) diff --git a/bedrock/entity/send_message.py b/bedrock/entity/send_message.py new file mode 100644 index 00000000..177b4b69 --- /dev/null +++ b/bedrock/entity/send_message.py @@ -0,0 +1,30 @@ +import asyncio +import sys + +from temporalio.client import Client +from workflows import BedrockParams, EntityBedrockWorkflow + + +async def main(prompt): + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + + workflow_id = "entity-bedrock-workflow" + + # Sends a signal to the workflow (and starts it if needed) + await client.start_workflow( + EntityBedrockWorkflow.run, + BedrockParams(None, None), + id=workflow_id, + task_queue="bedrock-task-queue", + start_signal="user_prompt", + start_signal_args=[prompt], + ) + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python send_message.py ''") + print("Example: python send_message.py 'What animals are marsupials?'") + else: + asyncio.run(main(sys.argv[1])) diff --git a/bedrock/entity/workflows.py b/bedrock/entity/workflows.py new file mode 100644 index 00000000..3dfdf530 --- /dev/null +++ b/bedrock/entity/workflows.py @@ -0,0 +1,165 @@ +import asyncio +from collections import deque +from dataclasses import dataclass +from datetime import timedelta +from typing import Deque, List, Optional, Tuple + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from bedrock.shared.activities import BedrockActivities + + +@dataclass +class BedrockParams: + conversation_summary: Optional[str] = None + prompt_queue: Optional[Deque[str]] = None + + +@workflow.defn +class EntityBedrockWorkflow: + def __init__(self) -> None: + # List to store prompt history + self.conversation_history: List[Tuple[str, str]] = [] + self.prompt_queue: Deque[str] = deque() + self.conversation_summary: Optional[str] = None + self.continue_as_new_per_turns: int = 6 + self.chat_ended: bool = False + + @workflow.run + async def run( + self, + params: BedrockParams, + ) -> str: + + if params and params.conversation_summary: + self.conversation_history.append( + ("conversation_summary", params.conversation_summary) + ) + + self.conversation_summary = params.conversation_summary + + if params and params.prompt_queue: + self.prompt_queue.extend(params.prompt_queue) + + while True: + workflow.logger.info("Waiting for prompts...") + + # Wait for a chat message (signal) or timeout + await workflow.wait_condition( + lambda: bool(self.prompt_queue) or self.chat_ended + ) + + if self.prompt_queue: + # Fetch next user prompt and add to conversation history + prompt = self.prompt_queue.popleft() + self.conversation_history.append(("user", prompt)) + + workflow.logger.info("Prompt: " + prompt) + + # Send prompt to Amazon Bedrock + response = await workflow.execute_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_with_history(prompt), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info(f"{response}") + + # Append the response to the conversation history + self.conversation_history.append(("response", response)) + + # Continue as new every x conversational turns to avoid event + # history size getting too large. This is also to avoid the + # prompt (with conversational history) getting too large for + # AWS Bedrock. + + # We summarize the chat to date and use that as input to the + # new workflow + if len(self.conversation_history) >= self.continue_as_new_per_turns: + # Summarize the conversation to date using Amazon Bedrock + self.conversation_summary = await workflow.start_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_summary_from_history(), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info( + "Continuing as new due to %i conversational turns." + % self.continue_as_new_per_turns, + ) + + workflow.continue_as_new( + args=[ + BedrockParams( + self.conversation_summary, + self.prompt_queue, + ) + ] + ) + + continue + + # If end chat signal was sent + if self.chat_ended: + # The workflow might be continued as new without any + # chat to summarize, so only call Bedrock if there + # is more than the previous summary in the history. + if len(self.conversation_history) > 1: + # Summarize the conversation to date using Amazon Bedrock + self.conversation_summary = await workflow.start_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_summary_from_history(), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info( + "Chat ended. Conversation summary:\n" + + f"{self.conversation_summary}" + ) + + return f"{self.conversation_history}" + + @workflow.signal + async def user_prompt(self, prompt: str) -> None: + # Chat ended but the workflow is waiting for a chat summary to be generated + if self.chat_ended: + workflow.logger.warn(f"Message dropped due to chat closed: {prompt}") + return + + self.prompt_queue.append(prompt) + + @workflow.signal + async def end_chat(self) -> None: + self.chat_ended = True + + @workflow.query + def get_conversation_history(self) -> List[Tuple[str, str]]: + return self.conversation_history + + @workflow.query + def get_summary_from_history(self) -> Optional[str]: + return self.conversation_summary + + # Helper method used in prompts to Amazon Bedrock + def format_history(self) -> str: + return " ".join(f"{text}" for _, text in self.conversation_history) + + # Create the prompt given to Amazon Bedrock for each conversational turn + def prompt_with_history(self, prompt: str) -> str: + history_string = self.format_history() + return ( + f"Here is the conversation history: {history_string} Please add " + + "a few sentence response to the prompt in plain text sentences. " + + "Don't editorialize or add metadata like response. Keep the " + + f"text a plain explanation based on the history. Prompt: {prompt}" + ) + + # Create the prompt to Amazon Bedrock to summarize the conversation history + def prompt_summary_from_history(self) -> str: + history_string = self.format_history() + return ( + "Here is the conversation history between a user and a chatbot: " + + f"{history_string} -- Please produce a two sentence summary of " + + "this conversation." + ) diff --git a/bedrock/shared/__init__.py b/bedrock/shared/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/bedrock/shared/activities.py b/bedrock/shared/activities.py new file mode 100644 index 00000000..98c8d055 --- /dev/null +++ b/bedrock/shared/activities.py @@ -0,0 +1,39 @@ +import json + +import boto3 +from botocore.config import Config +from temporalio import activity + +config = Config(region_name="us-west-2") + + +class BedrockActivities: + def __init__(self) -> None: + self.bedrock = boto3.client(service_name="bedrock-runtime", config=config) + + @activity.defn + def prompt_bedrock(self, prompt: str) -> str: + # Model params + modelId = "meta.llama2-70b-chat-v1" + accept = "application/json" + contentType = "application/json" + max_gen_len = 512 + temperature = 0.1 + top_p = 0.2 + + body = json.dumps( + { + "prompt": prompt, + "max_gen_len": max_gen_len, + "temperature": temperature, + "top_p": top_p, + } + ) + + response = self.bedrock.invoke_model( + body=body, modelId=modelId, accept=accept, contentType=contentType + ) + + response_body = json.loads(response.get("body").read()) + + return response_body.get("generation") diff --git a/bedrock/signals_and_queries/README.md b/bedrock/signals_and_queries/README.md new file mode 100644 index 00000000..edbca795 --- /dev/null +++ b/bedrock/signals_and_queries/README.md @@ -0,0 +1,19 @@ +# Amazon Bedrock workflow using Signals and Queries + +Adding signals & queries to the [basic Bedrock sample](../1_basic). Starts a workflow with a prompt, allows follow-up prompts to be given using Temporal signals, and allows the conversation history to be queried using Temporal queries. + +To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory: + +1. Run the worker: `uv run run_worker.py` +2. In another terminal run the client with a prompt. + + Example: `uv run send_message.py 'What animals are marsupials?'` + +3. View the worker's output for the response. +4. Give followup prompts by signaling the workflow. + + Example: `uv run send_message.py 'Do they lay eggs?'` +5. Get the conversation history by querying the workflow. + + Example: `uv run get_history.py` +6. The workflow will timeout after inactivity. diff --git a/bedrock/signals_and_queries/__init__.py b/bedrock/signals_and_queries/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/bedrock/signals_and_queries/get_history.py b/bedrock/signals_and_queries/get_history.py new file mode 100644 index 00000000..0bdf0861 --- /dev/null +++ b/bedrock/signals_and_queries/get_history.py @@ -0,0 +1,31 @@ +import asyncio + +from temporalio.client import Client +from workflows import SignalQueryBedrockWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + workflow_id = "bedrock-workflow-with-signals" + + handle = client.get_workflow_handle(workflow_id) + + # Queries the workflow for the conversation history + history = await handle.query(SignalQueryBedrockWorkflow.get_conversation_history) + + print("Conversation History") + print( + *(f"{speaker.title()}: {message}\n" for speaker, message in history), sep="\n" + ) + + # Queries the workflow for the conversation summary + summary = await handle.query(SignalQueryBedrockWorkflow.get_summary_from_history) + + if summary is not None: + print("Conversation Summary:") + print(summary) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/bedrock/signals_and_queries/run_worker.py b/bedrock/signals_and_queries/run_worker.py new file mode 100644 index 00000000..b3e709a9 --- /dev/null +++ b/bedrock/signals_and_queries/run_worker.py @@ -0,0 +1,35 @@ +import asyncio +import concurrent.futures +import logging + +from temporalio.client import Client +from temporalio.worker import Worker +from workflows import SignalQueryBedrockWorkflow + +from bedrock.shared.activities import BedrockActivities + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + activities = BedrockActivities() + + # Run the worker + with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor: + worker = Worker( + client, + task_queue="bedrock-task-queue", + workflows=[SignalQueryBedrockWorkflow], + activities=[activities.prompt_bedrock], + activity_executor=activity_executor, + ) + await worker.run() + + +if __name__ == "__main__": + print("Starting worker") + print("Then run 'python send_message.py \"\"'") + + logging.basicConfig(level=logging.INFO) + + asyncio.run(main()) diff --git a/bedrock/signals_and_queries/send_message.py b/bedrock/signals_and_queries/send_message.py new file mode 100644 index 00000000..67b8b37e --- /dev/null +++ b/bedrock/signals_and_queries/send_message.py @@ -0,0 +1,31 @@ +import asyncio +import sys + +from temporalio.client import Client +from workflows import SignalQueryBedrockWorkflow + + +async def main(prompt): + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + + workflow_id = "bedrock-workflow-with-signals" + inactivity_timeout_minutes = 1 + + # Sends a signal to the workflow (and starts it if needed) + await client.start_workflow( + SignalQueryBedrockWorkflow.run, + inactivity_timeout_minutes, + id=workflow_id, + task_queue="bedrock-task-queue", + start_signal="user_prompt", + start_signal_args=[prompt], + ) + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python send_message.py ''") + print("Example: python send_message.py 'What animals are marsupials?'") + else: + asyncio.run(main(sys.argv[1])) diff --git a/bedrock/signals_and_queries/workflows.py b/bedrock/signals_and_queries/workflows.py new file mode 100644 index 00000000..0c9147d7 --- /dev/null +++ b/bedrock/signals_and_queries/workflows.py @@ -0,0 +1,110 @@ +import asyncio +from collections import deque +from datetime import timedelta +from typing import Deque, List, Optional, Tuple + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from bedrock.shared.activities import BedrockActivities + + +@workflow.defn +class SignalQueryBedrockWorkflow: + def __init__(self) -> None: + # List to store prompt history + self.conversation_history: List[Tuple[str, str]] = [] + self.prompt_queue: Deque[str] = deque() + self.conversation_summary = "" + self.chat_timeout: bool = False + + @workflow.run + async def run(self, inactivity_timeout_minutes: int) -> str: + while True: + workflow.logger.info( + "Waiting for prompts... or closing chat after " + + f"{inactivity_timeout_minutes} minute(s)" + ) + + # Wait for a chat message (signal) or timeout + try: + await workflow.wait_condition( + lambda: bool(self.prompt_queue), + timeout=timedelta(minutes=inactivity_timeout_minutes), + ) + # If timeout was reached + except asyncio.TimeoutError: + self.chat_timeout = True + workflow.logger.info("Chat closed due to inactivity") + # End the workflow + break + + while self.prompt_queue: + # Fetch next user prompt and add to conversation history + prompt = self.prompt_queue.popleft() + self.conversation_history.append(("user", prompt)) + + workflow.logger.info(f"Prompt: {prompt}") + + # Send the prompt to Amazon Bedrock + response = await workflow.execute_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_with_history(prompt), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info(f"{response}") + + # Append the response to the conversation history + self.conversation_history.append(("response", response)) + + # Generate a summary before ending the workflow + self.conversation_summary = await workflow.start_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_summary_from_history(), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info(f"Conversation summary:\n{self.conversation_summary}") + + return f"{self.conversation_history}" + + @workflow.signal + async def user_prompt(self, prompt: str) -> None: + # Chat timed out but the workflow is waiting for a chat summary to be generated + if self.chat_timeout: + workflow.logger.warn(f"Message dropped due to chat closed: {prompt}") + return + + self.prompt_queue.append(prompt) + + @workflow.query + def get_conversation_history(self) -> List[Tuple[str, str]]: + return self.conversation_history + + @workflow.query + def get_summary_from_history(self) -> str: + return self.conversation_summary + + # Helper method used in prompts to Amazon Bedrock + def format_history(self) -> str: + return " ".join(f"{text}" for _, text in self.conversation_history) + + # Create the prompt given to Amazon Bedrock for each conversational turn + def prompt_with_history(self, prompt: str) -> str: + history_string = self.format_history() + return ( + f"Here is the conversation history: {history_string} Please add " + + "a few sentence response to the prompt in plain text sentences. " + + "Don't editorialize or add metadata like response. Keep the " + + f"text a plain explanation based on the history. Prompt: {prompt}" + ) + + # Create the prompt to Amazon Bedrock to summarize the conversation history + def prompt_summary_from_history(self) -> str: + history_string = self.format_history() + return ( + "Here is the conversation history between a user and a chatbot: " + + f"{history_string} -- Please produce a two sentence summary of " + + "this conversation." + ) diff --git a/cloud_export_to_parquet/README.md b/cloud_export_to_parquet/README.md new file mode 100644 index 00000000..fe6b3db9 --- /dev/null +++ b/cloud_export_to_parquet/README.md @@ -0,0 +1,23 @@ +# Cloud Export to parquet sample + +This is an example workflow to convert exported file from proto to parquet file. The workflow is an hourly schedule. + +Please make sure your python is 3.9 above. For this sample, run: + + uv sync --group=cloud-export-to-parquet + +Before you start, please modify workflow input in `create_schedule.py` with your s3 bucket and namespace. Also make sure you've the right AWS permission set up in your environment to allow this workflow read and write to your s3 bucket. + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: + +```bash +uv run run_worker.py +``` + +This will start the worker. Then, in another terminal, run the following to execute the schedule: + +```bash +uv run create_schedule.py +``` + +The workflow should convert exported file in your input s3 bucket to parquet in your specified location. diff --git a/cloud_export_to_parquet/__init__.py b/cloud_export_to_parquet/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/cloud_export_to_parquet/create_schedule.py b/cloud_export_to_parquet/create_schedule.py new file mode 100644 index 00000000..f425d4c6 --- /dev/null +++ b/cloud_export_to_parquet/create_schedule.py @@ -0,0 +1,64 @@ +import asyncio +import traceback +from datetime import datetime, timedelta + +from temporalio.client import ( + Client, + Schedule, + ScheduleActionStartWorkflow, + ScheduleIntervalSpec, + ScheduleSpec, + WorkflowFailureError, +) + +from cloud_export_to_parquet.workflows import ( + ProtoToParquet, + ProtoToParquetWorkflowInput, +) + + +async def main() -> None: + """Main function to run temporal workflow.""" + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + # TODO: update s3_bucket and namespace to the actual usecase + wf_input = ProtoToParquetWorkflowInput( + num_delay_hour=2, + export_s3_bucket="test-input-bucket", + namespace="test.namespace", + output_s3_bucket="test-output-bucket", + ) + + # Run the workflow + # try: + # await client.start_workflow( + # ProtoToParquet.run, + # wf_input, + # id = f"proto-to-parquet-{datetime.now()}", + # task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + # ) + # except WorkflowFailureError: + # print("Got exception: ", traceback.format_exc()) + + # Create the schedule + try: + await client.create_schedule( + "hourly-proto-to-parquet-wf-schedule", + Schedule( + action=ScheduleActionStartWorkflow( + ProtoToParquet.run, + wf_input, + id=f"proto-to-parquet-{datetime.now()}", + task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + ), + spec=ScheduleSpec( + intervals=[ScheduleIntervalSpec(every=timedelta(hours=1))] + ), + ), + ) + except WorkflowFailureError: + print("Got exception: ", traceback.format_exc()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cloud_export_to_parquet/data_trans_activities.py b/cloud_export_to_parquet/data_trans_activities.py new file mode 100644 index 00000000..b91f6b1a --- /dev/null +++ b/cloud_export_to_parquet/data_trans_activities.py @@ -0,0 +1,123 @@ +import json +import uuid +from dataclasses import dataclass +from typing import List + +import boto3 +import pandas as pd +import temporalio.api.export.v1 as export +from google.protobuf.json_format import MessageToJson +from temporalio import activity + + +@dataclass +class GetObjectKeysActivityInput: + bucket: str + path: str + + +@dataclass +class DataTransAndLandActivityInput: + export_s3_bucket: str + object_key: str + output_s3_bucket: str + write_path: str + + +@activity.defn +def get_object_keys(activity_input: GetObjectKeysActivityInput) -> List[str]: + """Function that list objects by key.""" + object_keys = [] + s3 = boto3.client("s3") + response = s3.list_objects_v2( + Bucket=activity_input.bucket, Prefix=activity_input.path + ) + for obj in response.get("Contents", []): + object_keys.append(obj["Key"]) + if len(object_keys) == 0: + raise FileNotFoundError( + f"No files found in {activity_input.bucket}/{activity_input.path}" + ) + + return object_keys + + +@activity.defn +def data_trans_and_land(activity_input: DataTransAndLandActivityInput) -> str: + """Function that convert proto to parquet and save to S3.""" + key = activity_input.object_key + data = get_data_from_object_key(activity_input.export_s3_bucket, key) + activity.logger.info("Convert proto to parquet for file: %s", key) + parquet_data = convert_proto_to_parquet_flatten(data) + activity.logger.info("Finish transformation for file: %s", key) + return save_to_sink( + parquet_data, activity_input.output_s3_bucket, activity_input.write_path + ) + + +def get_data_from_object_key( + bucket_name: str, object_key: str +) -> export.WorkflowExecutions: + """Function that get object by key.""" + v = export.WorkflowExecutions() + + s3 = boto3.client("s3") + try: + data = s3.get_object(Bucket=bucket_name, Key=object_key)["Body"].read() + except Exception as e: + activity.logger.error(f"Error reading object: {e}") + raise e + v.ParseFromString(data) + return v + + +def convert_proto_to_parquet_flatten(wfs: export.WorkflowExecutions) -> pd.DataFrame: + """Function that convert flatten proto data to parquet.""" + dfs = [] + for wf in wfs.items: + start_attributes = wf.history.events[ + 0 + ].workflow_execution_started_event_attributes + histories = wf.history + json_str = MessageToJson(histories) + row = { + "WorkflowID": start_attributes.workflow_id, + "RunID": start_attributes.original_execution_run_id, + "Histories": json.loads(json_str), + } + dfs.append(pd.DataFrame([row])) + df = pd.concat(dfs, ignore_index=True) + rows_flatten = [] + for _, row in df.iterrows(): + wf_histories_raw = row["Histories"]["events"] + worfkow_id = row["WorkflowID"] + run_id = row["RunID"] + for history_event in wf_histories_raw: + row_flatten = pd.json_normalize(history_event, sep="_") + skip_name = ["payloads", "."] + columns_to_drop = [ + col for col in row_flatten.columns for skip in skip_name if skip in col + ] + row_flatten.drop(columns_to_drop, axis=1, inplace=True) + row_flatten.insert(0, "WorkflowId", worfkow_id) + row_flatten.insert(1, "RunId", run_id) + rows_flatten.append(row_flatten) + df_flatten = pd.concat(rows_flatten, ignore_index=True) + return df_flatten + + +def save_to_sink(data: pd.DataFrame, s3_bucket: str, write_path: str) -> str: + """Function that save object to s3 bucket.""" + write_bytes = data.to_parquet(None, compression="snappy", index=False) + uuid_name = uuid.uuid1() + file_name = f"{uuid_name}.parquet" + activity.logger.info("Writing to S3 bucket: %s", file_name) + + s3 = boto3.client("s3") + try: + key = f"{write_path}/{file_name}" + s3.put_object(Bucket=s3_bucket, Key=key, Body=write_bytes) + return key + except Exception as e: + activity.logger.error(f"Error saving to sink: {e}") + raise e diff --git a/cloud_export_to_parquet/run_worker.py b/cloud_export_to_parquet/run_worker.py new file mode 100644 index 00000000..df02de11 --- /dev/null +++ b/cloud_export_to_parquet/run_worker.py @@ -0,0 +1,38 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor + +from temporalio.client import Client +from temporalio.worker import Worker +from temporalio.worker.workflow_sandbox import ( + SandboxedWorkflowRunner, + SandboxRestrictions, +) + +from cloud_export_to_parquet.data_trans_activities import ( + data_trans_and_land, + get_object_keys, +) +from cloud_export_to_parquet.workflows import ProtoToParquet + + +async def main() -> None: + """Main worker function.""" + # Create client connected to server at the given address + client = await Client.connect("localhost:7233") + + # Run the worker + worker: Worker = Worker( + client, + task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + workflows=[ProtoToParquet], + activities=[get_object_keys, data_trans_and_land], + workflow_runner=SandboxedWorkflowRunner( + restrictions=SandboxRestrictions.default.with_passthrough_modules("boto3") + ), + activity_executor=ThreadPoolExecutor(100), + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cloud_export_to_parquet/workflows.py b/cloud_export_to_parquet/workflows.py new file mode 100644 index 00000000..2d3edd88 --- /dev/null +++ b/cloud_export_to_parquet/workflows.py @@ -0,0 +1,74 @@ +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy +from temporalio.exceptions import ActivityError + +with workflow.unsafe.imports_passed_through(): + from cloud_export_to_parquet.data_trans_activities import ( + DataTransAndLandActivityInput, + data_trans_and_land, + get_object_keys, + GetObjectKeysActivityInput, + ) +from dataclasses import dataclass + + +@dataclass +class ProtoToParquetWorkflowInput: + num_delay_hour: int + export_s3_bucket: str + namespace: str + output_s3_bucket: str + + +@workflow.defn +class ProtoToParquet: + """Proto to parquet workflow.""" + + @workflow.run + async def run(self, workflow_input: ProtoToParquetWorkflowInput) -> str: + """Run proto to parquet workflow.""" + retry_policy = RetryPolicy( + maximum_attempts=10, maximum_interval=timedelta(seconds=5) + ) + + # Read from export S3 bucket and given at least 2 hour delay to ensure the file has been uploaded + read_time = workflow.now() - timedelta(hours=workflow_input.num_delay_hour) + common_path = f"{workflow_input.namespace}/{read_time.year}/{read_time.month:02}/{read_time.day:02}/{read_time.hour:02}/00" + path = f"temporal-workflow-history/export/{common_path}" + get_object_keys_input = GetObjectKeysActivityInput( + workflow_input.export_s3_bucket, path + ) + + # Read Input File + object_keys_output = await workflow.execute_activity( + get_object_keys, + get_object_keys_input, + start_to_close_timeout=timedelta(minutes=5), + retry_policy=retry_policy, + ) + + write_path = f"temporal-workflow-history/parquet/{common_path}" + + try: + # Could create a list of corountine objects to process files in parallel + for key in object_keys_output: + data_trans_and_land_input = DataTransAndLandActivityInput( + workflow_input.export_s3_bucket, + key, + workflow_input.output_s3_bucket, + write_path, + ) + # Convert proto to parquet and save to S3 + await workflow.execute_activity( + data_trans_and_land, + data_trans_and_land_input, + start_to_close_timeout=timedelta(minutes=15), + retry_policy=retry_policy, + ) + except ActivityError as output_err: + workflow.logger.error(f"Data transformation failed: {output_err}") + raise output_err + + return write_path diff --git a/context_propagation/README.md b/context_propagation/README.md new file mode 100644 index 00000000..e1027292 --- /dev/null +++ b/context_propagation/README.md @@ -0,0 +1,16 @@ +# Context Propagation Interceptor Sample + +This sample shows how to use an interceptor to propagate contextual information through workflows and activities. For +this example, [contextvars](https://docs.python.org/3/library/contextvars.html) holds the contextual information. + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +worker: + + uv run worker.py + +This will start the worker. Then, in another terminal, run the following to execute the workflow: + + uv run starter.py + +The starter terminal should complete with the hello result and the worker terminal should show the logs with the +propagated user ID contextual information flowing through the workflows/activities. \ No newline at end of file diff --git a/context_propagation/__init__.py b/context_propagation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/context_propagation/activities.py b/context_propagation/activities.py new file mode 100644 index 00000000..77cde015 --- /dev/null +++ b/context_propagation/activities.py @@ -0,0 +1,9 @@ +from temporalio import activity + +from context_propagation import shared + + +@activity.defn +async def say_hello_activity(name: str) -> str: + activity.logger.info(f"Activity called by user {shared.user_id.get()}") + return f"Hello, {name}" diff --git a/context_propagation/interceptor.py b/context_propagation/interceptor.py new file mode 100644 index 00000000..f8dca8e5 --- /dev/null +++ b/context_propagation/interceptor.py @@ -0,0 +1,184 @@ +from __future__ import annotations + +from contextlib import contextmanager +from typing import Any, Mapping, Protocol, Type + +import temporalio.activity +import temporalio.api.common.v1 +import temporalio.client +import temporalio.converter +import temporalio.worker +import temporalio.workflow + +with temporalio.workflow.unsafe.imports_passed_through(): + from context_propagation.shared import HEADER_KEY, user_id + + +class _InputWithHeaders(Protocol): + headers: Mapping[str, temporalio.api.common.v1.Payload] + + +def set_header_from_context( + input: _InputWithHeaders, payload_converter: temporalio.converter.PayloadConverter +) -> None: + user_id_val = user_id.get() + if user_id_val: + input.headers = { + **input.headers, + HEADER_KEY: payload_converter.to_payload(user_id_val), + } + + +@contextmanager +def context_from_header( + input: _InputWithHeaders, payload_converter: temporalio.converter.PayloadConverter +): + payload = input.headers.get(HEADER_KEY) + token = ( + user_id.set(payload_converter.from_payload(payload, str)) if payload else None + ) + try: + yield + finally: + if token: + user_id.reset(token) + + +class ContextPropagationInterceptor( + temporalio.client.Interceptor, temporalio.worker.Interceptor +): + """Interceptor that can serialize/deserialize contexts.""" + + def __init__( + self, + payload_converter: temporalio.converter.PayloadConverter = temporalio.converter.default().payload_converter, + ) -> None: + self._payload_converter = payload_converter + + def intercept_client( + self, next: temporalio.client.OutboundInterceptor + ) -> temporalio.client.OutboundInterceptor: + return _ContextPropagationClientOutboundInterceptor( + next, self._payload_converter + ) + + def intercept_activity( + self, next: temporalio.worker.ActivityInboundInterceptor + ) -> temporalio.worker.ActivityInboundInterceptor: + return _ContextPropagationActivityInboundInterceptor(next) + + def workflow_interceptor_class( + self, input: temporalio.worker.WorkflowInterceptorClassInput + ) -> Type[_ContextPropagationWorkflowInboundInterceptor]: + return _ContextPropagationWorkflowInboundInterceptor + + +class _ContextPropagationClientOutboundInterceptor( + temporalio.client.OutboundInterceptor +): + def __init__( + self, + next: temporalio.client.OutboundInterceptor, + payload_converter: temporalio.converter.PayloadConverter, + ) -> None: + super().__init__(next) + self._payload_converter = payload_converter + + async def start_workflow( + self, input: temporalio.client.StartWorkflowInput + ) -> temporalio.client.WorkflowHandle[Any, Any]: + set_header_from_context(input, self._payload_converter) + return await super().start_workflow(input) + + async def query_workflow(self, input: temporalio.client.QueryWorkflowInput) -> Any: + set_header_from_context(input, self._payload_converter) + return await super().query_workflow(input) + + async def signal_workflow( + self, input: temporalio.client.SignalWorkflowInput + ) -> None: + set_header_from_context(input, self._payload_converter) + await super().signal_workflow(input) + + async def start_workflow_update( + self, input: temporalio.client.StartWorkflowUpdateInput + ) -> temporalio.client.WorkflowUpdateHandle[Any]: + set_header_from_context(input, self._payload_converter) + return await self.next.start_workflow_update(input) + + +class _ContextPropagationActivityInboundInterceptor( + temporalio.worker.ActivityInboundInterceptor +): + async def execute_activity( + self, input: temporalio.worker.ExecuteActivityInput + ) -> Any: + with context_from_header(input, temporalio.activity.payload_converter()): + return await self.next.execute_activity(input) + + +class _ContextPropagationWorkflowInboundInterceptor( + temporalio.worker.WorkflowInboundInterceptor +): + def init(self, outbound: temporalio.worker.WorkflowOutboundInterceptor) -> None: + self.next.init(_ContextPropagationWorkflowOutboundInterceptor(outbound)) + + async def execute_workflow( + self, input: temporalio.worker.ExecuteWorkflowInput + ) -> Any: + with context_from_header(input, temporalio.workflow.payload_converter()): + return await self.next.execute_workflow(input) + + async def handle_signal(self, input: temporalio.worker.HandleSignalInput) -> None: + with context_from_header(input, temporalio.workflow.payload_converter()): + return await self.next.handle_signal(input) + + async def handle_query(self, input: temporalio.worker.HandleQueryInput) -> Any: + with context_from_header(input, temporalio.workflow.payload_converter()): + return await self.next.handle_query(input) + + def handle_update_validator( + self, input: temporalio.worker.HandleUpdateInput + ) -> None: + with context_from_header(input, temporalio.workflow.payload_converter()): + self.next.handle_update_validator(input) + + async def handle_update_handler( + self, input: temporalio.worker.HandleUpdateInput + ) -> Any: + with context_from_header(input, temporalio.workflow.payload_converter()): + return await self.next.handle_update_handler(input) + + +class _ContextPropagationWorkflowOutboundInterceptor( + temporalio.worker.WorkflowOutboundInterceptor +): + async def signal_child_workflow( + self, input: temporalio.worker.SignalChildWorkflowInput + ) -> None: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return await self.next.signal_child_workflow(input) + + async def signal_external_workflow( + self, input: temporalio.worker.SignalExternalWorkflowInput + ) -> None: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return await self.next.signal_external_workflow(input) + + def start_activity( + self, input: temporalio.worker.StartActivityInput + ) -> temporalio.workflow.ActivityHandle: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return self.next.start_activity(input) + + async def start_child_workflow( + self, input: temporalio.worker.StartChildWorkflowInput + ) -> temporalio.workflow.ChildWorkflowHandle: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return await self.next.start_child_workflow(input) + + def start_local_activity( + self, input: temporalio.worker.StartLocalActivityInput + ) -> temporalio.workflow.ActivityHandle: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return self.next.start_local_activity(input) diff --git a/context_propagation/shared.py b/context_propagation/shared.py new file mode 100644 index 00000000..faae59d8 --- /dev/null +++ b/context_propagation/shared.py @@ -0,0 +1,6 @@ +from contextvars import ContextVar +from typing import Optional + +HEADER_KEY = "__my_user_id" + +user_id: ContextVar[Optional[str]] = ContextVar("user_id", default=None) diff --git a/context_propagation/starter.py b/context_propagation/starter.py new file mode 100644 index 00000000..2865eee2 --- /dev/null +++ b/context_propagation/starter.py @@ -0,0 +1,35 @@ +import asyncio +import logging + +from temporalio.client import Client + +from context_propagation import interceptor, shared, workflows + + +async def main(): + logging.basicConfig(level=logging.INFO) + + # Set the user ID + shared.user_id.set("some-user") + + # Connect client + client = await Client.connect( + "localhost:7233", + # Use our interceptor + interceptors=[interceptor.ContextPropagationInterceptor()], + ) + + # Start workflow, send signal, wait for completion, issue query + handle = await client.start_workflow( + workflows.SayHelloWorkflow.run, + "Temporal", + id=f"context-propagation-workflow-id", + task_queue="context-propagation-task-queue", + ) + await handle.signal(workflows.SayHelloWorkflow.signal_complete) + result = await handle.result() + logging.info(f"Workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/context_propagation/worker.py b/context_propagation/worker.py new file mode 100644 index 00000000..14d954da --- /dev/null +++ b/context_propagation/worker.py @@ -0,0 +1,41 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from context_propagation import activities, interceptor, workflows + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + # Connect client + client = await Client.connect( + "localhost:7233", + # Use our interceptor + interceptors=[interceptor.ContextPropagationInterceptor()], + ) + + # Run a worker for the workflow + async with Worker( + client, + task_queue="context-propagation-task-queue", + activities=[activities.say_hello_activity], + workflows=[workflows.SayHelloWorkflow], + ): + # Wait until interrupted + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/context_propagation/workflows.py b/context_propagation/workflows.py new file mode 100644 index 00000000..e9c120b3 --- /dev/null +++ b/context_propagation/workflows.py @@ -0,0 +1,28 @@ +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from context_propagation.activities import say_hello_activity + from context_propagation.shared import user_id + + +@workflow.defn +class SayHelloWorkflow: + def __init__(self) -> None: + self._complete = False + + @workflow.run + async def run(self, name: str) -> str: + workflow.logger.info(f"Workflow called by user {user_id.get()}") + + # Wait for signal then run activity + await workflow.wait_condition(lambda: self._complete) + return await workflow.execute_activity( + say_hello_activity, name, start_to_close_timeout=timedelta(minutes=5) + ) + + @workflow.signal + async def signal_complete(self) -> None: + workflow.logger.info(f"Signal called by user {user_id.get()}") + self._complete = True diff --git a/custom_converter/README.md b/custom_converter/README.md index d4c9f588..8e82e483 100644 --- a/custom_converter/README.md +++ b/custom_converter/README.md @@ -5,11 +5,11 @@ This sample shows how to make a custom payload converter for a type not natively To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run starter.py The workflow should complete with the hello result. If the custom converter was not set for the custom input and output classes, we would get an error on the client side and on the worker side. \ No newline at end of file diff --git a/custom_converter/shared.py b/custom_converter/shared.py index 7ac45495..c94b8dbb 100644 --- a/custom_converter/shared.py +++ b/custom_converter/shared.py @@ -54,8 +54,7 @@ def __init__(self) -> None: # Just add ours as first before the defaults super().__init__( GreetingEncodingPayloadConverter(), - # TODO(cretz): Make this list available without instantiation - https://github.com/temporalio/sdk-python/issues/139 - *DefaultPayloadConverter().converters.values(), + *DefaultPayloadConverter.default_encoding_payload_converters ) diff --git a/custom_decorator/README.md b/custom_decorator/README.md index 65f59482..cfc59111 100644 --- a/custom_decorator/README.md +++ b/custom_decorator/README.md @@ -6,11 +6,11 @@ decorator that automatically configures an activity to heartbeat twice as freque To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run starter.py The workflow will be started, and then after 5 seconds will be sent a signal to cancel its forever-running activity. The activity has a heartbeat timeout set to 2s, so since it has the `@auto_heartbeater` decorator set, it will heartbeat diff --git a/custom_metric/README.md b/custom_metric/README.md new file mode 100644 index 00000000..de1d51d5 --- /dev/null +++ b/custom_metric/README.md @@ -0,0 +1,36 @@ +# Custom Metric + +This sample deminstrates two things: (1) how to make a custom metric, and (2) how to use an interceptor. +The custom metric in this sample is an activity schedule-to-start-latency with a workflow type tag. + +Please see the top-level [README](../README.md) for prerequisites such as Python, uv, starting the local temporal development server, etc. + +1. Run the worker with `uv run custom_metric/worker.py` +2. Request execution of the workflow with `uv run custom_metric/starter.py` +3. Go to `http://127.0.0.1:9090/metrics` in your browser + +You'll get something like the following: + +```txt +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="100"} 1 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="500"} 1 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="1000"} 1 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="5000"} 2 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="10000"} 2 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="100000"} 2 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="1000000"} 2 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="+Inf"} 2 +custom_activity_schedule_to_start_latency_sum{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow"} 1010 +custom_activity_schedule_to_start_latency_count{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow"} 2 +... +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="100"} 1 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="500"} 1 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="1000"} 1 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="5000"} 2 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="10000"} 2 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="100000"} 2 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="1000000"} 2 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="+Inf"} 2 +temporal_activity_schedule_to_start_latency_sum{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue"} 1010 +temporal_activity_schedule_to_start_latency_count{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue"} 2 +``` diff --git a/custom_metric/__init__.py b/custom_metric/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/custom_metric/activity.py b/custom_metric/activity.py new file mode 100644 index 00000000..7f2ee116 --- /dev/null +++ b/custom_metric/activity.py @@ -0,0 +1,9 @@ +import time + +from temporalio import activity + + +@activity.defn +def print_and_sleep(): + print("In the activity.") + time.sleep(1) diff --git a/custom_metric/starter.py b/custom_metric/starter.py new file mode 100644 index 00000000..ded3a626 --- /dev/null +++ b/custom_metric/starter.py @@ -0,0 +1,23 @@ +import asyncio +import uuid + +from temporalio.client import Client + +from custom_metric.workflow import StartTwoActivitiesWorkflow + + +async def main(): + + client = await Client.connect( + "localhost:7233", + ) + + await client.start_workflow( + StartTwoActivitiesWorkflow.run, + id="execute-activity-workflow-" + str(uuid.uuid4()), + task_queue="custom-metric-task-queue", + ) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/custom_metric/worker.py b/custom_metric/worker.py new file mode 100644 index 00000000..9ffad207 --- /dev/null +++ b/custom_metric/worker.py @@ -0,0 +1,71 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor + +from temporalio import activity +from temporalio.client import Client +from temporalio.runtime import PrometheusConfig, Runtime, TelemetryConfig +from temporalio.worker import ( + ActivityInboundInterceptor, + ExecuteActivityInput, + Interceptor, + Worker, +) + +from custom_metric.activity import print_and_sleep +from custom_metric.workflow import StartTwoActivitiesWorkflow + + +class SimpleWorkerInterceptor(Interceptor): + def intercept_activity( + self, next: ActivityInboundInterceptor + ) -> ActivityInboundInterceptor: + return CustomScheduleToStartInterceptor(next) + + +class CustomScheduleToStartInterceptor(ActivityInboundInterceptor): + async def execute_activity(self, input: ExecuteActivityInput): + + schedule_to_start = ( + activity.info().started_time + - activity.info().current_attempt_scheduled_time + ) + # Could do the original schedule time instead of current attempt + # schedule_to_start_second_option = activity.info().started_time - activity.info().scheduled_time + + meter = activity.metric_meter() + histogram = meter.create_histogram_timedelta( + "custom_activity_schedule_to_start_latency", + description="Time between activity scheduling and start", + unit="duration", + ) + histogram.record( + schedule_to_start, {"workflow_type": activity.info().workflow_type} + ) + return await self.next.execute_activity(input) + + +async def main(): + runtime = Runtime( + telemetry=TelemetryConfig(metrics=PrometheusConfig(bind_address="0.0.0.0:9090")) + ) + client = await Client.connect( + "localhost:7233", + runtime=runtime, + ) + worker = Worker( + client, + task_queue="custom-metric-task-queue", + interceptors=[SimpleWorkerInterceptor()], + workflows=[StartTwoActivitiesWorkflow], + activities=[print_and_sleep], + # only one activity executor with two concurrently scheduled activities + # to force a nontrivial schedule to start times + activity_executor=ThreadPoolExecutor(1), + max_concurrent_activities=1, + ) + + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/custom_metric/workflow.py b/custom_metric/workflow.py new file mode 100644 index 00000000..cf37823b --- /dev/null +++ b/custom_metric/workflow.py @@ -0,0 +1,25 @@ +import asyncio +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from custom_metric.activity import print_and_sleep + + +@workflow.defn +class StartTwoActivitiesWorkflow: + @workflow.run + async def run(self): + # Request two concurrent activities with only one task slot so + # we can see nontrivial schedule to start times. + activity1 = workflow.execute_activity( + print_and_sleep, + start_to_close_timeout=timedelta(seconds=5), + ) + activity2 = workflow.execute_activity( + print_and_sleep, + start_to_close_timeout=timedelta(seconds=5), + ) + await asyncio.gather(activity1, activity2) + return None diff --git a/dsl/README.md b/dsl/README.md index 4000b3a3..d5051132 100644 --- a/dsl/README.md +++ b/dsl/README.md @@ -6,24 +6,24 @@ samples [in TypeScript](https://github.com/temporalio/samples-typescript/tree/ma For this sample, the optional `dsl` dependency group must be included. To include, run: - poetry install --with dsl + uv sync --group dsl To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. Then, in another terminal, run the following to execute a workflow of steps defined in [workflow1.yaml](workflow1.yaml): - poetry run python starter.py workflow1.yaml + uv run starter.py workflow1.yaml This will run the workflow and show the final variables that the workflow returns. Looking in the worker terminal, each step executed will be visible. Similarly we can do the same for the more advanced [workflow2.yaml](workflow2.yaml) file: - poetry run python starter.py workflow2.yaml + uv run starter.py workflow2.yaml This sample gives a guide of how one can write a workflow to interpret arbitrary steps from a user-provided DSL. Many DSL models are more advanced and are more specific to conform to business logic needs. \ No newline at end of file diff --git a/encryption/README.md b/encryption/README.md index b3e1de43..09828793 100644 --- a/encryption/README.md +++ b/encryption/README.md @@ -4,51 +4,48 @@ This sample shows how to make an encryption codec for end-to-end encryption. It samples [in TypeScript](https://github.com/temporalio/samples-typescript/tree/main/encryption) and [in Go](https://github.com/temporalio/samples-go/tree/main/encryption). + For this sample, the optional `encryption` dependency group must be included. To include, run: - poetry install --with encryption + uv sync --group encryption To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run starter.py -The workflow should complete with the hello result. To view the workflow, use [tctl](https://docs.temporal.io/tctl-v1/): +The workflow should complete with the hello result. To view the workflow, use [temporal](https://docs.temporal.io/cli): - tctl workflow show --workflow_id encryption-workflow-id + temporal workflow show --workflow-id encryption-workflow-id -Note how the input/result look like (with wrapping removed): +Note how the result looks like (with wrapping removed): ``` - Input:[encoding binary/encrypted: payload encoding is not supported] - ... - Result:[encoding binary/encrypted: payload encoding is not supported] + Output:[encoding binary/encrypted: payload encoding is not supported] ``` -This is because the data is encrypted and not visible. To make data visible to external Temporal tools like `tctl` and +This is because the data is encrypted and not visible. To make data visible to external Temporal tools like `temporal` and the UI, start a codec server in another terminal: - poetry run python codec_server.py + uv run codec_server.py -Now with that running, run `tctl` again with the codec endpoint: +Now with that running, run `temporal` again with the codec endpoint: - tctl --codec_endpoint http://localhost:8081 workflow show --workflow_id encryption-workflow-id + temporal workflow show --workflow-id encryption-workflow-id --codec-endpoint http://localhost:8081 Notice now the output has the unencrypted values: ``` - Input:["Temporal"] - ... Result:["Hello, Temporal"] ``` This decryption did not leave the local machine here. Same case with the web UI. If you go to the web UI, you'll only see encrypted input/results. But, assuming your web UI -is at `http://localhost:8080`, if you set the "Remote Codec Endpoint" in the web UI to `http://localhost:8081` you can +is at `http://localhost:8233` (this is the default for the local dev server), if you set the "Remote Codec Endpoint" in the web UI to `http://localhost:8081` you can then see the unencrypted results. This is possible because CORS settings in the codec server allow the browser to access the codec server directly over localhost. They can be changed to suit Temporal cloud web UI instead if necessary. \ No newline at end of file diff --git a/encryption/codec.py b/encryption/codec.py index 6aaa58be..9f79526f 100644 --- a/encryption/codec.py +++ b/encryption/codec.py @@ -1,4 +1,3 @@ -import base64 import os from typing import Iterable, List diff --git a/encryption/codec_server.py b/encryption/codec_server.py index 4bd04214..3e3029f2 100644 --- a/encryption/codec_server.py +++ b/encryption/codec_server.py @@ -12,8 +12,8 @@ def build_codec_server() -> web.Application: # Cors handler async def cors_options(req: web.Request) -> web.Response: resp = web.Response() - if req.headers.get(hdrs.ORIGIN) == "http://localhost:8080": - resp.headers[hdrs.ACCESS_CONTROL_ALLOW_ORIGIN] = "http://localhost:8080" + if req.headers.get(hdrs.ORIGIN) == "http://localhost:8233": + resp.headers[hdrs.ACCESS_CONTROL_ALLOW_ORIGIN] = "http://localhost:8233" resp.headers[hdrs.ACCESS_CONTROL_ALLOW_METHODS] = "POST" resp.headers[hdrs.ACCESS_CONTROL_ALLOW_HEADERS] = "content-type,x-namespace" return resp diff --git a/gevent_async/README.md b/gevent_async/README.md index 9b9bdff3..d1d9f048 100644 --- a/gevent_async/README.md +++ b/gevent_async/README.md @@ -12,17 +12,17 @@ workflows. For this sample, the optional `gevent` dependency group must be included. To include, run: - poetry install --with gevent + uv sync --group gevent To run the sample, first see [README.md](../README.md) for prerequisites such as having a localhost Temporal server running. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. The worker has a workflow and two activities, one `asyncio` based and one gevent based. Now in another terminal, run the following from this directory to execute the workflow: - poetry run python starter.py + uv run starter.py The workflow should run and complete with the hello result. Note on the worker terminal there will be logs of the workflow and activity executions. \ No newline at end of file diff --git a/hello/README.md b/hello/README.md index 9e13f544..45d24768 100644 --- a/hello/README.md +++ b/hello/README.md @@ -5,7 +5,7 @@ These samples show basic workflow and activity features. To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to run the `hello_activity.py` sample: - poetry run python hello_activity.py + uv run hello_activity.py The result will be: diff --git a/hello/hello_activity.py b/hello/hello_activity.py index 6615b55d..9801ee1b 100644 --- a/hello/hello_activity.py +++ b/hello/hello_activity.py @@ -1,5 +1,4 @@ import asyncio -import logging from dataclasses import dataclass from datetime import timedelta @@ -38,7 +37,8 @@ async def run(self, name: str) -> str: async def main(): - # Uncomment the line below to see logging + # Uncomment the lines below to see logging output + # import logging # logging.basicConfig(level=logging.INFO) # Start client diff --git a/hello/hello_activity_choice.py b/hello/hello_activity_choice.py index 56b7b7e6..6d15af53 100644 --- a/hello/hello_activity_choice.py +++ b/hello/hello_activity_choice.py @@ -54,44 +54,27 @@ class ShoppingList: @workflow.defn class PurchaseFruitsWorkflow: @workflow.run - async def run(self, list: ShoppingList) -> str: + async def run(self, shopping_list: ShoppingList) -> str: # Order each thing on the list ordered: List[str] = [] - for item in list.items: + for item in shopping_list.items: if item.fruit is Fruit.APPLE: - ordered.append( - await workflow.execute_activity( - order_apples, - item.amount, - start_to_close_timeout=timedelta(seconds=5), - ) - ) + order_function = order_apples elif item.fruit is Fruit.BANANA: - ordered.append( - await workflow.execute_activity( - order_bananas, - item.amount, - start_to_close_timeout=timedelta(seconds=5), - ) - ) + order_function = order_bananas elif item.fruit is Fruit.CHERRY: - ordered.append( - await workflow.execute_activity( - order_cherries, - item.amount, - start_to_close_timeout=timedelta(seconds=5), - ) - ) + order_function = order_cherries elif item.fruit is Fruit.ORANGE: - ordered.append( - await workflow.execute_activity( - order_oranges, - item.amount, - start_to_close_timeout=timedelta(seconds=5), - ) - ) + order_function = order_oranges else: raise ValueError(f"Unrecognized fruit: {item.fruit}") + ordered.append( + await workflow.execute_activity( + order_function, + item.amount, + start_to_close_timeout=timedelta(seconds=5), + ) + ) return "".join(ordered) diff --git a/hello/hello_mtls.py b/hello/hello_mtls.py index f613fba4..1ac6f9d8 100644 --- a/hello/hello_mtls.py +++ b/hello/hello_mtls.py @@ -5,7 +5,8 @@ from typing import Optional from temporalio import activity, workflow -from temporalio.client import Client, TLSConfig +from temporalio.client import Client +from temporalio.service import TLSConfig from temporalio.worker import Worker diff --git a/hello/hello_patch.py b/hello/hello_patch.py index 43371d4f..0ed19b2f 100644 --- a/hello/hello_patch.py +++ b/hello/hello_patch.py @@ -1,5 +1,4 @@ import asyncio -import logging import sys from dataclasses import dataclass from datetime import timedelta @@ -96,7 +95,8 @@ async def main(): version = sys.argv[1] - # Uncomment the line below to see logging + # Uncomment the lines below to see logging output + # import logging # logging.basicConfig(level=logging.INFO) # Start client diff --git a/hello/hello_search_attributes.py b/hello/hello_search_attributes.py index 6892fb71..8b504ea6 100644 --- a/hello/hello_search_attributes.py +++ b/hello/hello_search_attributes.py @@ -1,10 +1,7 @@ import asyncio -from typing import List from temporalio import workflow -from temporalio.client import Client, WorkflowExecutionDescription -from temporalio.common import SearchAttributeValues -from temporalio.converter import default as default_converter +from temporalio.client import Client from temporalio.worker import Worker diff --git a/hello/hello_update.py b/hello/hello_update.py index 1b1236d3..111d95b1 100644 --- a/hello/hello_update.py +++ b/hello/hello_update.py @@ -7,7 +7,8 @@ @workflow.defn class GreetingWorkflow: - is_complete = False + def __init__(self): + self.is_complete = False @workflow.run async def run(self) -> str: diff --git a/langchain/README.md b/langchain/README.md new file mode 100644 index 00000000..264eba28 --- /dev/null +++ b/langchain/README.md @@ -0,0 +1,28 @@ +# LangChain Sample + +This sample shows you how you can use Temporal to orchestrate workflows for [LangChain](https://www.langchain.com). + +For this sample, the optional `langchain` dependency group must be included. To include, run: + + uv sync --group langchain + +Export your [OpenAI API key](https://platform.openai.com/api-keys) as an environment variable. Replace `YOUR_API_KEY` with your actual OpenAI API key. + + export OPENAI_API_KEY='...' + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +worker: + + uv run worker.py + +This will start the worker. Then, in another terminal, run the following to execute a workflow: + + uv run starter.py + +Then, in another terminal, run the following command to translate a phrase: + + curl -X POST "http://localhost:8000/translate?phrase=hello%20world&language=Spanish" + +Which should produce some output like: + + {"translation":"Hola mundo"} \ No newline at end of file diff --git a/langchain/activities.py b/langchain/activities.py new file mode 100644 index 00000000..4425b969 --- /dev/null +++ b/langchain/activities.py @@ -0,0 +1,31 @@ +from dataclasses import dataclass + +from langchain_openai import ChatOpenAI +from temporalio import activity + +from langchain.prompts import ChatPromptTemplate + + +@dataclass +class TranslateParams: + phrase: str + language: str + + +@activity.defn +async def translate_phrase(params: TranslateParams) -> dict: + # LangChain setup + template = """You are a helpful assistant who translates between languages. + Translate the following phrase into the specified language: {phrase} + Language: {language}""" + chat_prompt = ChatPromptTemplate.from_messages( + [ + ("system", template), + ("human", "Translate"), + ] + ) + chain = chat_prompt | ChatOpenAI() + # Use the asynchronous invoke method + return dict( + await chain.ainvoke({"phrase": params.phrase, "language": params.language}) + ) diff --git a/langchain/starter.py b/langchain/starter.py new file mode 100644 index 00000000..ce7ff2ce --- /dev/null +++ b/langchain/starter.py @@ -0,0 +1,38 @@ +from contextlib import asynccontextmanager +from uuid import uuid4 + +import uvicorn +from activities import TranslateParams +from fastapi import FastAPI, HTTPException +from temporalio.client import Client +from workflow import LangChainWorkflow + + +@asynccontextmanager +async def lifespan(app: FastAPI): + app.state.temporal_client = await Client.connect("localhost:7233") + yield + + +app = FastAPI(lifespan=lifespan) + + +@app.post("/translate") +async def translate(phrase: str, language: str): + client = app.state.temporal_client + try: + result = await client.execute_workflow( + LangChainWorkflow.run, + TranslateParams(phrase, language), + id=f"langchain-translation-{uuid4()}", + task_queue="langchain-task-queue", + ) + translation_content = result.get("content", "Translation not available") + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + return {"translation": translation_content} + + +if __name__ == "__main__": + uvicorn.run(app, host="localhost", port=8000) diff --git a/langchain/worker.py b/langchain/worker.py new file mode 100644 index 00000000..0fa488b3 --- /dev/null +++ b/langchain/worker.py @@ -0,0 +1,37 @@ +import asyncio + +from activities import translate_phrase +from temporalio.client import Client +from temporalio.worker import Worker +from workflow import LangChainWorkflow + +interrupt_event = asyncio.Event() + + +async def main(): + client = await Client.connect("localhost:7233") + worker = Worker( + client, + task_queue="langchain-task-queue", + workflows=[LangChainWorkflow], + activities=[translate_phrase], + ) + + print("\nWorker started, ctrl+c to exit\n") + await worker.run() + try: + # Wait indefinitely until the interrupt event is set + await interrupt_event.wait() + finally: + # The worker will be shutdown gracefully due to the async context manager + print("\nShutting down the worker\n") + + +if __name__ == "__main__": + loop = asyncio.get_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + print("\nInterrupt received, shutting down...\n") + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/langchain/workflow.py b/langchain/workflow.py new file mode 100644 index 00000000..c005f095 --- /dev/null +++ b/langchain/workflow.py @@ -0,0 +1,17 @@ +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from activities import TranslateParams, translate_phrase + + +@workflow.defn +class LangChainWorkflow: + @workflow.run + async def run(self, params: TranslateParams) -> dict: + return await workflow.execute_activity( + translate_phrase, + params, + schedule_to_close_timeout=timedelta(seconds=30), + ) diff --git a/message_passing/__init__.py b/message_passing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/message_passing/introduction/README.md b/message_passing/introduction/README.md new file mode 100644 index 00000000..7a2bd351 --- /dev/null +++ b/message_passing/introduction/README.md @@ -0,0 +1,18 @@ +# Introduction to message-passing + +This sample provides an introduction to using Query, Signal, and Update. + +See https://docs.temporal.io/develop/python/message-passing. + +To run, first see the main [README.md](../../README.md) for prerequisites. + +Then create two terminals and `cd` to this directory. + +Run the worker in one terminal: + + uv run worker.py + +And execute the workflow in the other terminal: + + uv run starter.py + diff --git a/message_passing/introduction/__init__.py b/message_passing/introduction/__init__.py new file mode 100644 index 00000000..79d4a7b1 --- /dev/null +++ b/message_passing/introduction/__init__.py @@ -0,0 +1,13 @@ +from enum import IntEnum + +TASK_QUEUE = "message-passing-introduction-task-queue" + + +class Language(IntEnum): + ARABIC = 1 + CHINESE = 2 + ENGLISH = 3 + FRENCH = 4 + HINDI = 5 + PORTUGUESE = 6 + SPANISH = 7 diff --git a/message_passing/introduction/activities.py b/message_passing/introduction/activities.py new file mode 100644 index 00000000..7f2c9c56 --- /dev/null +++ b/message_passing/introduction/activities.py @@ -0,0 +1,25 @@ +import asyncio +from typing import Optional + +from temporalio import activity + +from message_passing.introduction import Language + + +@activity.defn +async def call_greeting_service(to_language: Language) -> Optional[str]: + """ + An Activity that simulates a call to a remote greeting service. + The remote greeting service supports the full range of languages. + """ + greetings = { + Language.ARABIC: "مرحبا بالعالم", + Language.CHINESE: "你好，世界", + Language.ENGLISH: "Hello, world", + Language.FRENCH: "Bonjour, monde", + Language.HINDI: "नमस्ते दुनिया", + Language.PORTUGUESE: "Olá mundo", + Language.SPANISH: "Hola mundo", + } + await asyncio.sleep(0.2) # Simulate a network call + return greetings.get(to_language) diff --git a/message_passing/introduction/starter.py b/message_passing/introduction/starter.py new file mode 100644 index 00000000..13a48c3a --- /dev/null +++ b/message_passing/introduction/starter.py @@ -0,0 +1,52 @@ +import asyncio +from typing import Optional + +from temporalio.client import Client, WorkflowUpdateStage + +from message_passing.introduction import TASK_QUEUE +from message_passing.introduction.workflows import ( + ApproveInput, + GetLanguagesInput, + GreetingWorkflow, + Language, +) + + +async def main(client: Optional[Client] = None): + client = client or await Client.connect("localhost:7233") + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id="greeting-workflow-1234", + task_queue=TASK_QUEUE, + ) + + # 👉 Send a Query + supported_languages = await wf_handle.query( + GreetingWorkflow.get_languages, GetLanguagesInput(include_unsupported=False) + ) + print(f"supported languages: {supported_languages}") + + # 👉 Execute an Update + previous_language = await wf_handle.execute_update( + GreetingWorkflow.set_language, Language.CHINESE + ) + current_language = await wf_handle.query(GreetingWorkflow.get_language) + print(f"language changed: {previous_language.name} -> {current_language.name}") + + # 👉 Start an Update and then wait for it to complete + update_handle = await wf_handle.start_update( + GreetingWorkflow.set_language_using_activity, + Language.ARABIC, + wait_for_stage=WorkflowUpdateStage.ACCEPTED, + ) + previous_language = await update_handle.result() + current_language = await wf_handle.query(GreetingWorkflow.get_language) + print(f"language changed: {previous_language.name} -> {current_language.name}") + + # 👉 Send a Signal + await wf_handle.signal(GreetingWorkflow.approve, ApproveInput(name="")) + print(await wf_handle.result()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/message_passing/introduction/worker.py b/message_passing/introduction/worker.py new file mode 100644 index 00000000..25f4121f --- /dev/null +++ b/message_passing/introduction/worker.py @@ -0,0 +1,36 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from message_passing.introduction import TASK_QUEUE +from message_passing.introduction.activities import call_greeting_service +from message_passing.introduction.workflows import GreetingWorkflow + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + client = await Client.connect("localhost:7233") + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + activities=[call_greeting_service], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/introduction/workflows.py b/message_passing/introduction/workflows.py new file mode 100644 index 00000000..97d2b874 --- /dev/null +++ b/message_passing/introduction/workflows.py @@ -0,0 +1,119 @@ +import asyncio +from dataclasses import dataclass +from datetime import timedelta +from typing import List, Optional + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +with workflow.unsafe.imports_passed_through(): + from message_passing.introduction import Language + from message_passing.introduction.activities import call_greeting_service + + +@dataclass +class GetLanguagesInput: + include_unsupported: bool + + +@dataclass +class ApproveInput: + name: str + + +@workflow.defn +class GreetingWorkflow: + """ + A workflow that that returns a greeting in one of multiple supported + languages. + + It exposes a query to obtain the current language, a signal to approve the + workflow so that it is allowed to return its result, and two updates for + changing the current language and receiving the previous language in + response. + + One of the update handlers is not an `async def`, so it can only mutate and + return local workflow state; the other update handler is `async def` and + executes an activity which calls a remote service, giving access to language + translations which are not available in local workflow state. + """ + + def __init__(self) -> None: + self.approved_for_release = False + self.approver_name: Optional[str] = None + self.greetings = { + Language.CHINESE: "你好，世界", + Language.ENGLISH: "Hello, world", + } + self.language = Language.ENGLISH + self.lock = asyncio.Lock() # used by the async handler below + + @workflow.run + async def run(self) -> str: + # 👉 In addition to waiting for the `approve` Signal, we also wait for + # all handlers to finish. Otherwise, the Workflow might return its + # result while an async set_language_using_activity Update is in + # progress. + await workflow.wait_condition( + lambda: self.approved_for_release and workflow.all_handlers_finished() + ) + return self.greetings[self.language] + + @workflow.query + def get_languages(self, input: GetLanguagesInput) -> List[Language]: + # 👉 A Query handler returns a value: it can inspect but must not mutate the Workflow state. + if input.include_unsupported: + return sorted(Language) + else: + return sorted(self.greetings) + + @workflow.signal + def approve(self, input: ApproveInput) -> None: + # 👉 A Signal handler mutates the Workflow state but cannot return a value. + self.approved_for_release = True + self.approver_name = input.name + + @workflow.update + def set_language(self, language: Language) -> Language: + # 👉 An Update handler can mutate the Workflow state and return a value. + previous_language, self.language = self.language, language + return previous_language + + @set_language.validator + def validate_language(self, language: Language) -> None: + if language not in self.greetings: + # 👉 In an Update validator you raise any exception to reject the Update. + raise ValueError(f"{language.name} is not supported") + + @workflow.update + async def set_language_using_activity(self, language: Language) -> Language: + # 👉 This update handler is async, so it can execute an activity. + if language not in self.greetings: + # 👉 We use a lock so that, if this handler is executed multiple + # times, each execution can schedule the activity only when the + # previously scheduled activity has completed. This ensures that + # multiple calls to set_language are processed in order. + async with self.lock: + greeting = await workflow.execute_activity( + call_greeting_service, + language, + start_to_close_timeout=timedelta(seconds=10), + ) + # 👉 The requested language might not be supported by the remote + # service. If so, we raise ApplicationError, which will fail the + # Update. The WorkflowExecutionUpdateAccepted event will still + # be added to history. (Update validators can be used to reject + # updates before any event is written to history, but they + # cannot be async, and so we cannot use an update validator for + # this purpose.) + if greeting is None: + raise ApplicationError( + f"Greeting service does not support {language.name}" + ) + self.greetings[language] = greeting + previous_language, self.language = self.language, language + return previous_language + + @workflow.query + def get_language(self) -> Language: + return self.language diff --git a/message_passing/safe_message_handlers/README.md b/message_passing/safe_message_handlers/README.md new file mode 100644 index 00000000..069d6bca --- /dev/null +++ b/message_passing/safe_message_handlers/README.md @@ -0,0 +1,21 @@ +# Atomic message handlers + +This sample shows off important techniques for handling signals and updates, aka messages. In particular, it illustrates how message handlers can interleave or not be completed before the workflow completes, and how you can manage that. + +* Here, using workflow.wait_condition, signal and update handlers will only operate when the workflow is within a certain state--between cluster_started and cluster_shutdown. +* Message handlers can block and their actions can be interleaved with one another and with the main workflow. This can easily cause bugs, so you can use a lock to protect shared state from interleaved access. +* An "Entity" workflow, i.e. a long-lived workflow, periodically "continues as new". It must do this to prevent its history from growing too large, and it passes its state to the next workflow. You can check `workflow.info().is_continue_as_new_suggested()` to see when it's time. +* Most people want their message handlers to finish before the workflow run completes or continues as new. Use `await workflow.wait_condition(lambda: workflow.all_handlers_finished())` to achieve this. +* Message handlers can be made idempotent. See update `ClusterManager.assign_nodes_to_job`. + +To run, first see [README.md](../../README.md) for prerequisites. + +Then, run the following from this directory to run the worker: +\ + uv run worker.py + +Then, in another terminal, run the following to execute the workflow: + + uv run starter.py + +This will start a worker to run your workflow and activities, then start a ClusterManagerWorkflow and put it through its paces. diff --git a/message_passing/safe_message_handlers/__init__.py b/message_passing/safe_message_handlers/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/message_passing/safe_message_handlers/activities.py b/message_passing/safe_message_handlers/activities.py new file mode 100644 index 00000000..da8a8be0 --- /dev/null +++ b/message_passing/safe_message_handlers/activities.py @@ -0,0 +1,55 @@ +import asyncio +from dataclasses import dataclass +from typing import List, Set + +from temporalio import activity + + +@dataclass +class AssignNodesToJobInput: + nodes: List[str] + job_name: str + + +@dataclass +class ClusterState: + node_ids: List[str] + + +@activity.defn +async def start_cluster() -> ClusterState: + return ClusterState(node_ids=[f"node-{i}" for i in range(25)]) + + +@activity.defn +async def assign_nodes_to_job(input: AssignNodesToJobInput) -> None: + print(f"Assigning nodes {input.nodes} to job {input.job_name}") + await asyncio.sleep(0.1) + + +@dataclass +class UnassignNodesForJobInput: + nodes: List[str] + job_name: str + + +@activity.defn +async def unassign_nodes_for_job(input: UnassignNodesForJobInput) -> None: + print(f"Deallocating nodes {input.nodes} from job {input.job_name}") + await asyncio.sleep(0.1) + + +@dataclass +class FindBadNodesInput: + nodes_to_check: Set[str] + + +@activity.defn +async def find_bad_nodes(input: FindBadNodesInput) -> Set[str]: + await asyncio.sleep(0.1) + bad_nodes = set([id for id in input.nodes_to_check if hash(id) % 5 == 0]) + if bad_nodes: + print(f"Found bad nodes: {bad_nodes}") + else: + print("No new bad nodes found.") + return bad_nodes diff --git a/message_passing/safe_message_handlers/starter.py b/message_passing/safe_message_handlers/starter.py new file mode 100644 index 00000000..7ffe13d9 --- /dev/null +++ b/message_passing/safe_message_handlers/starter.py @@ -0,0 +1,86 @@ +import argparse +import asyncio +import logging +import uuid +from typing import Optional + +from temporalio import common +from temporalio.client import Client, WorkflowHandle + +from message_passing.safe_message_handlers.workflow import ( + ClusterManagerAssignNodesToJobInput, + ClusterManagerDeleteJobInput, + ClusterManagerInput, + ClusterManagerWorkflow, +) + + +async def do_cluster_lifecycle(wf: WorkflowHandle, delay_seconds: Optional[int] = None): + cluster_status = await wf.execute_update( + ClusterManagerWorkflow.wait_until_cluster_started + ) + print(f"Cluster started with {len(cluster_status.nodes)} nodes") + + print("Assigning jobs to nodes...") + allocation_updates = [] + for i in range(6): + allocation_updates.append( + wf.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=2, job_name=f"task-{i}" + ), + ) + ) + await asyncio.gather(*allocation_updates) + + print(f"Sleeping for {delay_seconds} second(s)") + if delay_seconds: + await asyncio.sleep(delay_seconds) + + print("Deleting jobs...") + deletion_updates = [] + for i in range(6): + deletion_updates.append( + wf.execute_update( + ClusterManagerWorkflow.delete_job, + ClusterManagerDeleteJobInput(job_name=f"task-{i}"), + ) + ) + await asyncio.gather(*deletion_updates) + + await wf.signal(ClusterManagerWorkflow.shutdown_cluster) + + +async def main(should_test_continue_as_new: bool): + # Connect to Temporal + client = await Client.connect("localhost:7233") + + print("Starting cluster") + cluster_manager_handle = await client.start_workflow( + ClusterManagerWorkflow.run, + ClusterManagerInput(test_continue_as_new=should_test_continue_as_new), + id=f"ClusterManagerWorkflow-{uuid.uuid4()}", + task_queue="safe-message-handlers-task-queue", + id_reuse_policy=common.WorkflowIDReusePolicy.TERMINATE_IF_RUNNING, + ) + delay_seconds = 10 if should_test_continue_as_new else 1 + await do_cluster_lifecycle(cluster_manager_handle, delay_seconds=delay_seconds) + result = await cluster_manager_handle.result() + print( + f"Cluster shut down successfully." + f" It had {result.num_currently_assigned_nodes} nodes assigned at the end." + ) + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + parser = argparse.ArgumentParser(description="Atomic message handlers") + parser.add_argument( + "--test-continue-as-new", + help="Make the ClusterManagerWorkflow continue as new before shutting down", + action="store_true", + default=False, + ) + args = parser.parse_args() + asyncio.run(main(args.test_continue_as_new)) diff --git a/message_passing/safe_message_handlers/worker.py b/message_passing/safe_message_handlers/worker.py new file mode 100644 index 00000000..34e71290 --- /dev/null +++ b/message_passing/safe_message_handlers/worker.py @@ -0,0 +1,44 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from message_passing.safe_message_handlers.workflow import ( + ClusterManagerWorkflow, + assign_nodes_to_job, + find_bad_nodes, + start_cluster, + unassign_nodes_for_job, +) + +interrupt_event = asyncio.Event() + + +async def main(): + client = await Client.connect("localhost:7233") + + async with Worker( + client, + task_queue="safe-message-handlers-task-queue", + workflows=[ClusterManagerWorkflow], + activities=[ + assign_nodes_to_job, + unassign_nodes_for_job, + find_bad_nodes, + start_cluster, + ], + ): + logging.info("ClusterManagerWorkflow worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/safe_message_handlers/workflow.py b/message_passing/safe_message_handlers/workflow.py new file mode 100644 index 00000000..ca549a61 --- /dev/null +++ b/message_passing/safe_message_handlers/workflow.py @@ -0,0 +1,265 @@ +import asyncio +import dataclasses +from dataclasses import dataclass +from datetime import timedelta +from typing import Dict, List, Optional, Set + +from temporalio import workflow +from temporalio.common import RetryPolicy +from temporalio.exceptions import ApplicationError + +from message_passing.safe_message_handlers.activities import ( + AssignNodesToJobInput, + FindBadNodesInput, + UnassignNodesForJobInput, + assign_nodes_to_job, + find_bad_nodes, + start_cluster, + unassign_nodes_for_job, +) + + +# In workflows that continue-as-new, it's convenient to store all your state in one serializable structure +# to make it easier to pass between runs +@dataclass +class ClusterManagerState: + cluster_started: bool = False + cluster_shutdown: bool = False + nodes: Dict[str, Optional[str]] = dataclasses.field(default_factory=dict) + jobs_assigned: Set[str] = dataclasses.field(default_factory=set) + + +@dataclass +class ClusterManagerInput: + state: Optional[ClusterManagerState] = None + test_continue_as_new: bool = False + + +@dataclass +class ClusterManagerResult: + num_currently_assigned_nodes: int + num_bad_nodes: int + + +# Be in the habit of storing message inputs and outputs in serializable structures. +# This makes it easier to add more over time in a backward-compatible way. +@dataclass +class ClusterManagerAssignNodesToJobInput: + # If larger or smaller than previous amounts, will resize the job. + total_num_nodes: int + job_name: str + + +@dataclass +class ClusterManagerDeleteJobInput: + job_name: str + + +@dataclass +class ClusterManagerAssignNodesToJobResult: + nodes_assigned: Set[str] + + +# ClusterManagerWorkflow keeps track of the assignments of a cluster of nodes. +# Via signals, the cluster can be started and shutdown. +# Via updates, clients can also assign jobs to nodes and delete jobs. +# These updates must run atomically. +@workflow.defn +class ClusterManagerWorkflow: + @workflow.init + def __init__(self, input: ClusterManagerInput) -> None: + if input.state: + self.state = input.state + else: + self.state = ClusterManagerState() + + if input.test_continue_as_new: + self.max_history_length: Optional[int] = 120 + self.sleep_interval_seconds = 1 + else: + self.max_history_length = None + self.sleep_interval_seconds = 600 + + # Protects workflow state from interleaved access + self.nodes_lock = asyncio.Lock() + + @workflow.update + async def wait_until_cluster_started(self) -> ClusterManagerState: + await workflow.wait_condition(lambda: self.state.cluster_started) + return self.state + + @workflow.signal + async def shutdown_cluster(self) -> None: + await workflow.wait_condition(lambda: self.state.cluster_started) + self.state.cluster_shutdown = True + workflow.logger.info("Cluster shut down") + + # This is an update as opposed to a signal because the client may want to wait for nodes to be allocated + # before sending work to those nodes. + # Returns the list of node names that were allocated to the job. + @workflow.update + async def assign_nodes_to_job( + self, input: ClusterManagerAssignNodesToJobInput + ) -> ClusterManagerAssignNodesToJobResult: + await workflow.wait_condition(lambda: self.state.cluster_started) + if self.state.cluster_shutdown: + # If you want the client to receive a failure, either add an update validator and throw the + # exception from there, or raise an ApplicationError. Other exceptions in the main handler + # will cause the workflow to keep retrying and get it stuck. + raise ApplicationError( + "Cannot assign nodes to a job: Cluster is already shut down" + ) + + async with self.nodes_lock: + # Idempotency guard. + if input.job_name in self.state.jobs_assigned: + return ClusterManagerAssignNodesToJobResult( + self.get_assigned_nodes(job_name=input.job_name) + ) + unassigned_nodes = self.get_unassigned_nodes() + if len(unassigned_nodes) < input.total_num_nodes: + # If you want the client to receive a failure, either add an update validator and throw the + # exception from there, or raise an ApplicationError. Other exceptions in the main handler + # will cause the workflow to keep retrying and get it stuck. + raise ApplicationError( + f"Cannot assign {input.total_num_nodes} nodes; have only {len(unassigned_nodes)} available" + ) + nodes_to_assign = unassigned_nodes[: input.total_num_nodes] + # This await would be dangerous without nodes_lock because it yields control and allows interleaving + # with delete_job and perform_health_checks, which both touch self.state.nodes. + await self._assign_nodes_to_job(nodes_to_assign, input.job_name) + return ClusterManagerAssignNodesToJobResult( + nodes_assigned=self.get_assigned_nodes(job_name=input.job_name) + ) + + async def _assign_nodes_to_job( + self, assigned_nodes: List[str], job_name: str + ) -> None: + await workflow.execute_activity( + assign_nodes_to_job, + AssignNodesToJobInput(nodes=assigned_nodes, job_name=job_name), + start_to_close_timeout=timedelta(seconds=10), + ) + for node in assigned_nodes: + self.state.nodes[node] = job_name + self.state.jobs_assigned.add(job_name) + + # Even though it returns nothing, this is an update because the client may want to track it, for example + # to wait for nodes to be unassigned before reassigning them. + @workflow.update + async def delete_job(self, input: ClusterManagerDeleteJobInput) -> None: + await workflow.wait_condition(lambda: self.state.cluster_started) + if self.state.cluster_shutdown: + # If you want the client to receive a failure, either add an update validator and throw the + # exception from there, or raise an ApplicationError. Other exceptions in the main handler + # will cause the workflow to keep retrying and get it stuck. + raise ApplicationError("Cannot delete a job: Cluster is already shut down") + + async with self.nodes_lock: + nodes_to_unassign = [ + k for k, v in self.state.nodes.items() if v == input.job_name + ] + # This await would be dangerous without nodes_lock because it yields control and allows interleaving + # with assign_nodes_to_job and perform_health_checks, which all touch self.state.nodes. + await self._unassign_nodes_for_job(nodes_to_unassign, input.job_name) + + async def _unassign_nodes_for_job( + self, nodes_to_unassign: List[str], job_name: str + ): + await workflow.execute_activity( + unassign_nodes_for_job, + UnassignNodesForJobInput(nodes=nodes_to_unassign, job_name=job_name), + start_to_close_timeout=timedelta(seconds=10), + ) + for node in nodes_to_unassign: + self.state.nodes[node] = None + + def get_unassigned_nodes(self) -> List[str]: + return [k for k, v in self.state.nodes.items() if v is None] + + def get_bad_nodes(self) -> Set[str]: + return set([k for k, v in self.state.nodes.items() if v == "BAD!"]) + + def get_assigned_nodes(self, *, job_name: Optional[str] = None) -> Set[str]: + if job_name: + return set([k for k, v in self.state.nodes.items() if v == job_name]) + else: + return set( + [ + k + for k, v in self.state.nodes.items() + if v is not None and v != "BAD!" + ] + ) + + async def perform_health_checks(self) -> None: + async with self.nodes_lock: + assigned_nodes = self.get_assigned_nodes() + try: + # This await would be dangerous without nodes_lock because it yields control and allows interleaving + # with assign_nodes_to_job and delete_job, which both touch self.state.nodes. + bad_nodes = await workflow.execute_activity( + find_bad_nodes, + FindBadNodesInput(nodes_to_check=assigned_nodes), + start_to_close_timeout=timedelta(seconds=10), + # This health check is optional, and our lock would block the whole workflow if we let it retry forever. + retry_policy=RetryPolicy(maximum_attempts=1), + ) + for node in bad_nodes: + self.state.nodes[node] = "BAD!" + except Exception as e: + workflow.logger.warn( + f"Health check failed with error {type(e).__name__}:{e}" + ) + + @workflow.run + async def run(self, input: ClusterManagerInput) -> ClusterManagerResult: + cluster_state = await workflow.execute_activity( + start_cluster, schedule_to_close_timeout=timedelta(seconds=10) + ) + self.state.nodes = {k: None for k in cluster_state.node_ids} + self.state.cluster_started = True + workflow.logger.info("Cluster started") + + # Perform health checks at intervals. + while True: + await self.perform_health_checks() + try: + await workflow.wait_condition( + lambda: self.state.cluster_shutdown + or self.should_continue_as_new(), + timeout=timedelta(seconds=self.sleep_interval_seconds), + ) + except asyncio.TimeoutError: + pass + if self.state.cluster_shutdown: + break + # The cluster manager is a long-running "entity" workflow so we need to periodically checkpoint its state and + # continue-as-new. + if self.should_continue_as_new(): + # We don't want to leave any job assignment or deletion handlers half-finished when we continue as new. + await workflow.wait_condition(lambda: workflow.all_handlers_finished()) + workflow.logger.info("Continuing as new") + workflow.continue_as_new( + ClusterManagerInput( + state=self.state, + test_continue_as_new=input.test_continue_as_new, + ) + ) + # Make sure we finish off handlers such as deleting jobs before we complete the workflow. + await workflow.wait_condition(lambda: workflow.all_handlers_finished()) + return ClusterManagerResult( + len(self.get_assigned_nodes()), + len(self.get_bad_nodes()), + ) + + def should_continue_as_new(self) -> bool: + if workflow.info().is_continue_as_new_suggested(): + return True + # This is just for ease-of-testing. In production, we trust temporal to tell us when to continue as new. + if ( + self.max_history_length + and workflow.info().get_current_history_length() > self.max_history_length + ): + return True + return False diff --git a/message_passing/update_with_start/lazy_initialization/README.md b/message_passing/update_with_start/lazy_initialization/README.md new file mode 100644 index 00000000..0dbe1844 --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/README.md @@ -0,0 +1,18 @@ +# Update With Start: Lazy init + +This sample illustrates the use of update-with-start to send Updates to a Workflow, starting the Workflow if +it is not running yet ("lazy init"). The Workflow represents a Shopping Cart in an e-commerce application, and +update-with-start is used to add items to the cart, receiving back the updated cart subtotal. + +To run, first see the main [README.md](../../../README.md) for prerequisites. + +Then run the following from this directory: + + uv run worker.py + +Then, in another terminal: + + uv run starter.py + +This will start a worker to run your workflow and activities, then simulate a backend application receiving +requests to add items to a shopping cart, before finalizing the order. diff --git a/message_passing/update_with_start/lazy_initialization/__init__.py b/message_passing/update_with_start/lazy_initialization/__init__.py new file mode 100644 index 00000000..ac3632ee --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/__init__.py @@ -0,0 +1 @@ +TASK_QUEUE = "update-with-start-lazy-initialization" diff --git a/message_passing/update_with_start/lazy_initialization/activities.py b/message_passing/update_with_start/lazy_initialization/activities.py new file mode 100644 index 00000000..fa730e47 --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/activities.py @@ -0,0 +1,20 @@ +import asyncio +from dataclasses import dataclass +from typing import Optional + +from temporalio import activity + + +@dataclass +class ShoppingCartItem: + sku: str + quantity: int + + +@activity.defn +async def get_price(item: ShoppingCartItem) -> Optional[int]: + await asyncio.sleep(0.1) + price = None if item.sku == "sku-456" else 599 + if price is None: + return None + return price * item.quantity diff --git a/message_passing/update_with_start/lazy_initialization/starter.py b/message_passing/update_with_start/lazy_initialization/starter.py new file mode 100644 index 00000000..b3274f9d --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/starter.py @@ -0,0 +1,78 @@ +import asyncio +import uuid +from typing import Optional, Tuple + +from temporalio import common +from temporalio.client import ( + Client, + WithStartWorkflowOperation, + WorkflowHandle, + WorkflowUpdateFailedError, +) +from temporalio.exceptions import ApplicationError + +from message_passing.update_with_start.lazy_initialization import TASK_QUEUE +from message_passing.update_with_start.lazy_initialization.workflows import ( + ShoppingCartItem, + ShoppingCartWorkflow, +) + + +async def handle_add_item_request( + session_id: str, item_id: str, quantity: int, temporal_client: Client +) -> Tuple[Optional[int], WorkflowHandle]: + """ + Handle a client request to add an item to the shopping cart. The user is not logged in, but a session ID is + available from a cookie, and we use this as the cart ID. The Temporal client was created at service-start + time and is shared by all request handlers. + + A Workflow Type exists that can be used to represent a shopping cart. The method uses update-with-start to + add an item to the shopping cart, creating the cart if it doesn't already exist. + + Note that the workflow handle is available, even if the Update fails. + """ + cart_id = f"cart-{session_id}" + start_op = WithStartWorkflowOperation( + ShoppingCartWorkflow.run, + id=cart_id, + id_conflict_policy=common.WorkflowIDConflictPolicy.USE_EXISTING, + task_queue=TASK_QUEUE, + ) + try: + price = await temporal_client.execute_update_with_start_workflow( + ShoppingCartWorkflow.add_item, + ShoppingCartItem(sku=item_id, quantity=quantity), + start_workflow_operation=start_op, + ) + except WorkflowUpdateFailedError as err: + if ( + isinstance(err.cause, ApplicationError) + and err.cause.type == "ItemUnavailableError" + ): + price = None + else: + raise err + + workflow_handle = await start_op.workflow_handle() + + return price, workflow_handle + + +async def main(): + print("🛒") + session_id = f"session-{uuid.uuid4()}" + temporal_client = await Client.connect("localhost:7233") + subtotal_1, _ = await handle_add_item_request( + session_id, "sku-123", 1, temporal_client + ) + subtotal_2, wf_handle = await handle_add_item_request( + session_id, "sku-456", 1, temporal_client + ) + print(f"subtotals were, {[subtotal_1, subtotal_2]}") + await wf_handle.signal(ShoppingCartWorkflow.checkout) + final_order = await wf_handle.result() + print(f"final order: {final_order}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/message_passing/update_with_start/lazy_initialization/worker.py b/message_passing/update_with_start/lazy_initialization/worker.py new file mode 100644 index 00000000..1964b43e --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/worker.py @@ -0,0 +1,35 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from message_passing.update_with_start.lazy_initialization import TASK_QUEUE, workflows +from message_passing.update_with_start.lazy_initialization.activities import get_price + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + client = await Client.connect("localhost:7233") + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[workflows.ShoppingCartWorkflow], + activities=[get_price], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/update_with_start/lazy_initialization/workflows.py b/message_passing/update_with_start/lazy_initialization/workflows.py new file mode 100644 index 00000000..49964ff2 --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/workflows.py @@ -0,0 +1,60 @@ +from dataclasses import dataclass +from datetime import timedelta +from typing import List, Tuple + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +with workflow.unsafe.imports_passed_through(): + from message_passing.update_with_start.lazy_initialization.activities import ( + ShoppingCartItem, + get_price, + ) + + +@dataclass +class FinalizedOrder: + id: str + items: List[Tuple[ShoppingCartItem, int]] + total: int + + +@workflow.defn +class ShoppingCartWorkflow: + def __init__(self): + self.items: List[Tuple[ShoppingCartItem, int]] = [] + self.order_submitted = False + + @workflow.run + async def run(self) -> FinalizedOrder: + await workflow.wait_condition( + lambda: workflow.all_handlers_finished() and self.order_submitted + ) + return FinalizedOrder( + id=workflow.info().workflow_id, + items=self.items, + total=sum(price for _, price in self.items), + ) + + @workflow.update + async def add_item(self, item: ShoppingCartItem) -> int: + price = await workflow.execute_activity( + get_price, item, start_to_close_timeout=timedelta(seconds=10) + ) + if price is None: + raise ApplicationError( + f"Item unavailable: {item}", + type="ItemUnavailableError", + ) + self.items.append((item, price)) + + return sum(price for _, price in self.items) + + @add_item.validator + def validate_add_item(self, item: ShoppingCartItem) -> None: + if self.order_submitted: + raise ApplicationError("Order already submitted") + + @workflow.signal + def checkout(self): + self.order_submitted = True diff --git a/message_passing/waiting_for_handlers/README.md b/message_passing/waiting_for_handlers/README.md new file mode 100644 index 00000000..238c1fb8 --- /dev/null +++ b/message_passing/waiting_for_handlers/README.md @@ -0,0 +1,41 @@ +# Waiting for message handlers + +This workflow demonstrates how to wait for signal and update handlers to +finish in the following circumstances: + +- Before a successful return +- On failure +- On cancellation + +Your workflow can also exit via Continue-As-New. In that case you would +usually wait for the handlers to finish immediately before the call to +continue_as_new(); that's not illustrated in this sample. + + +To run, open two terminals and `cd` to this directory in them. + +Run the worker in one terminal: + + uv run worker.py + +And run the workflow-starter code in the other terminal: + + uv run starter.py + + +Here's the output you'll see: + +``` +workflow exit type: SUCCESS + 🟢 caller received update result + 🟢 caller received workflow result + + +workflow exit type: FAILURE + 🟢 caller received update result + 🔴 caught exception while waiting for workflow result: Workflow execution failed: deliberately failing workflow + + +workflow exit type: CANCELLATION + 🟢 caller received update result +``` \ No newline at end of file diff --git a/message_passing/waiting_for_handlers/__init__.py b/message_passing/waiting_for_handlers/__init__.py new file mode 100644 index 00000000..1274f9bf --- /dev/null +++ b/message_passing/waiting_for_handlers/__init__.py @@ -0,0 +1,21 @@ +from dataclasses import dataclass +from enum import IntEnum + +TASK_QUEUE = "my-task-queue" +WORKFLOW_ID = "my-workflow-id" + + +class WorkflowExitType(IntEnum): + SUCCESS = 0 + FAILURE = 1 + CANCELLATION = 2 + + +@dataclass +class WorkflowInput: + exit_type: WorkflowExitType + + +@dataclass +class WorkflowResult: + data: str diff --git a/message_passing/waiting_for_handlers/activities.py b/message_passing/waiting_for_handlers/activities.py new file mode 100644 index 00000000..610db849 --- /dev/null +++ b/message_passing/waiting_for_handlers/activities.py @@ -0,0 +1,8 @@ +import asyncio + +from temporalio import activity + + +@activity.defn +async def activity_executed_by_update_handler(): + await asyncio.sleep(1) diff --git a/message_passing/waiting_for_handlers/starter.py b/message_passing/waiting_for_handlers/starter.py new file mode 100644 index 00000000..908095c5 --- /dev/null +++ b/message_passing/waiting_for_handlers/starter.py @@ -0,0 +1,69 @@ +import asyncio + +from temporalio import client, common + +from message_passing.waiting_for_handlers import ( + TASK_QUEUE, + WORKFLOW_ID, + WorkflowExitType, + WorkflowInput, +) +from message_passing.waiting_for_handlers.workflows import WaitingForHandlersWorkflow + + +async def starter(exit_type: WorkflowExitType): + cl = await client.Client.connect("localhost:7233") + wf_handle = await cl.start_workflow( + WaitingForHandlersWorkflow.run, + WorkflowInput(exit_type=exit_type), + id=WORKFLOW_ID, + task_queue=TASK_QUEUE, + id_conflict_policy=common.WorkflowIDConflictPolicy.TERMINATE_EXISTING, + ) + await _check_run(wf_handle, exit_type) + + +async def _check_run( + wf_handle: client.WorkflowHandle, + exit_type: WorkflowExitType, +): + try: + up_handle = await wf_handle.start_update( + WaitingForHandlersWorkflow.my_update, + wait_for_stage=client.WorkflowUpdateStage.ACCEPTED, + ) + except Exception as e: + print(f" 🔴 caught exception while starting update: {e}: {e.__cause__ or ''}") + + if exit_type == WorkflowExitType.CANCELLATION: + await wf_handle.cancel() + + try: + await up_handle.result() + print(" 🟢 caller received update result") + except Exception as e: + print( + f" 🔴 caught exception while waiting for update result: {e}: {e.__cause__ or ''}" + ) + + try: + await wf_handle.result() + print(" 🟢 caller received workflow result") + except BaseException as e: + print( + f" 🔴 caught exception while waiting for workflow result: {e}: {e.__cause__ or ''}" + ) + + +async def main(): + for exit_type in [ + WorkflowExitType.SUCCESS, + WorkflowExitType.FAILURE, + WorkflowExitType.CANCELLATION, + ]: + print(f"\n\nworkflow exit type: {exit_type.name}") + await starter(exit_type) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/message_passing/waiting_for_handlers/worker.py b/message_passing/waiting_for_handlers/worker.py new file mode 100644 index 00000000..9eea60a3 --- /dev/null +++ b/message_passing/waiting_for_handlers/worker.py @@ -0,0 +1,40 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from message_passing.waiting_for_handlers import TASK_QUEUE +from message_passing.waiting_for_handlers.activities import ( + activity_executed_by_update_handler, +) +from message_passing.waiting_for_handlers.workflows import WaitingForHandlersWorkflow + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + client = await Client.connect("localhost:7233") + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[WaitingForHandlersWorkflow], + activities=[ + activity_executed_by_update_handler, + ], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/waiting_for_handlers/workflows.py b/message_passing/waiting_for_handlers/workflows.py new file mode 100644 index 00000000..b4ed2991 --- /dev/null +++ b/message_passing/waiting_for_handlers/workflows.py @@ -0,0 +1,95 @@ +import asyncio +from datetime import timedelta + +from temporalio import exceptions, workflow + +from message_passing.waiting_for_handlers import ( + WorkflowExitType, + WorkflowInput, + WorkflowResult, +) +from message_passing.waiting_for_handlers.activities import ( + activity_executed_by_update_handler, +) + + +def is_workflow_exit_exception(e: BaseException) -> bool: + """ + True if the exception is of a type that will cause the workflow to exit. + + This is as opposed to exceptions that cause a workflow task failure, which + are retried automatically by Temporal. + """ + # 👉 If you have set additional failure_exception_types you should also + # check for these here. + return isinstance(e, (asyncio.CancelledError, exceptions.FailureError)) + + +@workflow.defn +class WaitingForHandlersWorkflow: + @workflow.run + async def run(self, input: WorkflowInput) -> WorkflowResult: + """ + This workflow.run method demonstrates a pattern that can be used to wait for signal and + update handlers to finish in the following circumstances: + + - On successful workflow return + - On workflow cancellation + - On workflow failure + + Your workflow can also exit via Continue-As-New. In that case you would usually wait for + the handlers to finish immediately before the call to continue_as_new(); that's not + illustrated in this sample. + + If you additionally need to perform cleanup or compensation on workflow failure or + cancellation, see the message_passing/waiting_for_handlers_and_compensation sample. + """ + try: + # 👉 Use this `try...except` style, instead of waiting for message + # handlers to finish in a `finally` block. The reason is that some + # exception types cause a workflow task failure as opposed to + # workflow exit, in which case we do *not* want to wait for message + # handlers to finish. + result = await self._my_workflow_application_logic(input) + await workflow.wait_condition(workflow.all_handlers_finished) + return result + # 👉 Catch BaseException since asyncio.CancelledError does not inherit + # from Exception. + except BaseException as e: + if is_workflow_exit_exception(e): + await workflow.wait_condition(workflow.all_handlers_finished) + raise + + # Methods below this point can be ignored unless you are interested in + # the implementation details of this sample. + + def __init__(self) -> None: + self._update_started = False + + @workflow.update + async def my_update(self) -> str: + self._update_started = True + await workflow.execute_activity( + activity_executed_by_update_handler, + start_to_close_timeout=timedelta(seconds=10), + ) + return "update-result" + + async def _my_workflow_application_logic( + self, input: WorkflowInput + ) -> WorkflowResult: + # The main workflow logic is implemented in a separate method in order + # to separate "platform-level" concerns (waiting for handlers to finish + # and error handling) from application logic. + + # Wait until handlers have started, so that we are demonstrating that we + # wait for them to finish. + await workflow.wait_condition(lambda: self._update_started) + if input.exit_type == WorkflowExitType.SUCCESS: + return WorkflowResult(data="workflow-result") + elif input.exit_type == WorkflowExitType.FAILURE: + raise exceptions.ApplicationError("deliberately failing workflow") + elif input.exit_type == WorkflowExitType.CANCELLATION: + # Block forever; the starter will send a workflow cancellation request. + await asyncio.Future() + raise AssertionError("unreachable") diff --git a/message_passing/waiting_for_handlers_and_compensation/README.md b/message_passing/waiting_for_handlers_and_compensation/README.md new file mode 100644 index 00000000..2ef5fe1f --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/README.md @@ -0,0 +1,39 @@ +# Waiting for message handlers, and performing compensation and cleanup in message handlers + +This sample demonstrates how to do the following: + +1. Ensure that all update/signal handlers are finished before a successful + workflow return, and on workflow cancellation and failure. +2. Perform compensation/cleanup in an update handler when the workflow is + cancelled or fails. + +For a simpler sample showing how to do (1) without (2), see [safe_message_handlers](../safe_message_handlers/README.md). + +To run, open two terminals and `cd` to this directory in them. + +Run the worker in one terminal: + + uv run worker.py + +And run the workflow-starter code in the other terminal: + + uv run starter.py + + +Here's the output you'll see: + +``` +workflow exit type: SUCCESS + 🟢 caller received update result + 🟢 caller received workflow result + + +workflow exit type: FAILURE + 🔴 caught exception while waiting for update result: Workflow update failed: The update failed because the workflow run exited + 🔴 caught exception while waiting for workflow result: Workflow execution failed: deliberately failing workflow + + +workflow exit type: CANCELLATION + 🔴 caught exception while waiting for update result: Workflow update failed: The update failed because the workflow run exited + 🔴 caught exception while waiting for workflow result: Workflow execution failed: Workflow cancelled +``` \ No newline at end of file diff --git a/message_passing/waiting_for_handlers_and_compensation/__init__.py b/message_passing/waiting_for_handlers_and_compensation/__init__.py new file mode 100644 index 00000000..1274f9bf --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/__init__.py @@ -0,0 +1,21 @@ +from dataclasses import dataclass +from enum import IntEnum + +TASK_QUEUE = "my-task-queue" +WORKFLOW_ID = "my-workflow-id" + + +class WorkflowExitType(IntEnum): + SUCCESS = 0 + FAILURE = 1 + CANCELLATION = 2 + + +@dataclass +class WorkflowInput: + exit_type: WorkflowExitType + + +@dataclass +class WorkflowResult: + data: str diff --git a/message_passing/waiting_for_handlers_and_compensation/activities.py b/message_passing/waiting_for_handlers_and_compensation/activities.py new file mode 100644 index 00000000..36c5a5cb --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/activities.py @@ -0,0 +1,18 @@ +import asyncio + +from temporalio import activity + + +@activity.defn +async def activity_executed_to_perform_workflow_compensation(): + await asyncio.sleep(1) + + +@activity.defn +async def activity_executed_by_update_handler(): + await asyncio.sleep(1) + + +@activity.defn +async def activity_executed_by_update_handler_to_perform_compensation(): + await asyncio.sleep(1) diff --git a/message_passing/waiting_for_handlers_and_compensation/starter.py b/message_passing/waiting_for_handlers_and_compensation/starter.py new file mode 100644 index 00000000..812bee5f --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/starter.py @@ -0,0 +1,71 @@ +import asyncio + +from temporalio import client, common + +from message_passing.waiting_for_handlers_and_compensation import ( + TASK_QUEUE, + WORKFLOW_ID, + WorkflowExitType, + WorkflowInput, +) +from message_passing.waiting_for_handlers_and_compensation.workflows import ( + WaitingForHandlersAndCompensationWorkflow, +) + + +async def starter(exit_type: WorkflowExitType): + cl = await client.Client.connect("localhost:7233") + wf_handle = await cl.start_workflow( + WaitingForHandlersAndCompensationWorkflow.run, + WorkflowInput(exit_type=exit_type), + id=WORKFLOW_ID, + task_queue=TASK_QUEUE, + id_conflict_policy=common.WorkflowIDConflictPolicy.TERMINATE_EXISTING, + ) + await _check_run(wf_handle, exit_type) + + +async def _check_run( + wf_handle: client.WorkflowHandle, + exit_type: WorkflowExitType, +): + try: + up_handle = await wf_handle.start_update( + WaitingForHandlersAndCompensationWorkflow.my_update, + wait_for_stage=client.WorkflowUpdateStage.ACCEPTED, + ) + except Exception as e: + print(f" 🔴 caught exception while starting update: {e}: {e.__cause__ or ''}") + + if exit_type == WorkflowExitType.CANCELLATION: + await wf_handle.cancel() + + try: + await up_handle.result() + print(" 🟢 caller received update result") + except Exception as e: + print( + f" 🔴 caught exception while waiting for update result: {e}: {e.__cause__ or ''}" + ) + + try: + await wf_handle.result() + print(" 🟢 caller received workflow result") + except Exception as e: + print( + f" 🔴 caught exception while waiting for workflow result: {e}: {e.__cause__ or ''}" + ) + + +async def main(): + for exit_type in [ + WorkflowExitType.SUCCESS, + WorkflowExitType.FAILURE, + WorkflowExitType.CANCELLATION, + ]: + print(f"\n\nworkflow exit type: {exit_type.name}") + await starter(exit_type) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/message_passing/waiting_for_handlers_and_compensation/worker.py b/message_passing/waiting_for_handlers_and_compensation/worker.py new file mode 100644 index 00000000..7daf768f --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/worker.py @@ -0,0 +1,46 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from message_passing.waiting_for_handlers_and_compensation import TASK_QUEUE +from message_passing.waiting_for_handlers_and_compensation.activities import ( + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, +) +from message_passing.waiting_for_handlers_and_compensation.workflows import ( + WaitingForHandlersAndCompensationWorkflow, +) + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + client = await Client.connect("localhost:7233") + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[WaitingForHandlersAndCompensationWorkflow], + activities=[ + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, + ], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/waiting_for_handlers_and_compensation/workflows.py b/message_passing/waiting_for_handlers_and_compensation/workflows.py new file mode 100644 index 00000000..450dff2c --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/workflows.py @@ -0,0 +1,176 @@ +import asyncio +from datetime import timedelta +from typing import cast + +from temporalio import exceptions, workflow + +from message_passing.waiting_for_handlers_and_compensation import ( + WorkflowExitType, + WorkflowInput, + WorkflowResult, +) +from message_passing.waiting_for_handlers_and_compensation.activities import ( + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, +) + + +@workflow.defn +class WaitingForHandlersAndCompensationWorkflow: + """ + This Workflow demonstrates how to wait for message handlers to finish and + perform compensation/cleanup: + + 1. It ensures that all signal and update handlers have finished before a + successful return, and on failure and cancellation. + 2. The update handler performs any necessary compensation/cleanup when the + workflow is cancelled or fails. + + If all you need to do is wait for handlers, without performing cleanup or compensation, + then see the simpler sample message_passing/waiting_for_handlers. + """ + + def __init__(self) -> None: + # 👉 If the workflow exits prematurely, this future will be completed + # with the associated exception as its value. Message handlers can then + # "race" this future against a task performing the message handler's own + # application logic; if this future completes before the message handler + # task then the handler should abort and perform compensation. + self.workflow_exit: asyncio.Future[None] = asyncio.Future() + + # The following attributes are implementation detail of this sample and can be + # ignored + self._update_started = False + self._update_compensation_done = False + self._workflow_compensation_done = False + + @workflow.run + async def run(self, input: WorkflowInput) -> WorkflowResult: + try: + # 👉 Use this `try...except` style, instead of waiting for message + # handlers to finish in a `finally` block. The reason is that some + # exception types cause a workflow task failure as opposed to + # workflow exit, in which case we do *not* want to wait for message + # handlers to finish. + + # 👉 The actual workflow application logic is implemented in a + # separate method in order to separate "platform-level" concerns + # (waiting for handlers to finish and ensuring that compensation is + # performed when appropriate) from application logic. + result = await self._my_workflow_application_logic(input) + self.workflow_exit.set_result(None) + await workflow.wait_condition(workflow.all_handlers_finished) + return result + # 👉 Catch BaseException since asyncio.CancelledError does not inherit + # from Exception. + except BaseException as e: + if is_workflow_exit_exception(e): + self.workflow_exit.set_exception(e) + await workflow.wait_condition(workflow.all_handlers_finished) + await self.workflow_compensation() + self._workflow_compensation_done = True + raise + + async def workflow_compensation(self): + await workflow.execute_activity( + activity_executed_to_perform_workflow_compensation, + start_to_close_timeout=timedelta(seconds=10), + ) + self._update_compensation_done = True + + @workflow.update + async def my_update(self) -> str: + """ + An update handler that handles exceptions raised in its own execution + and in that of the main workflow method. + + It ensures that: + - Compensation/cleanup is always performed when appropriate + - The update caller gets the update result, or WorkflowUpdateFailedError + """ + # 👉 "Race" the workflow_exit future against the handler's own + # application logic. Always use `workflow.wait` instead of + # `asyncio.wait` in Workflow code: asyncio's version is + # non-deterministic. (Note that coroutines must be wrapped in tasks in + # order to use workflow.wait.) + update_task = asyncio.create_task(self._my_update_application_logic()) + await workflow.wait( # type: ignore + [update_task, self.workflow_exit], return_when=asyncio.FIRST_EXCEPTION + ) + try: + if update_task.done(): + # 👉 The update has finished (whether successfully or not). + # Regardless of whether the main workflow method is about to + # exit or not, the update caller should receive a response + # informing them of the outcome of the update. So return the + # result, or raise the exception that caused the update handler + # to exit. + return await update_task + else: + # 👉 The main workflow method exited prematurely due to an + # error, and this happened before the update finished. Fail the + # update with the workflow exception as cause. + raise exceptions.ApplicationError( + "The update failed because the workflow run exited" + ) from cast(BaseException, self.workflow_exit.exception()) + # 👉 Catch BaseException since asyncio.CancelledError does not inherit + # from Exception. + except BaseException as e: + if is_workflow_exit_exception(e): + try: + await self.my_update_compensation() + except BaseException as e: + raise exceptions.ApplicationError( + "Update compensation failed" + ) from e + raise + + async def my_update_compensation(self): + await workflow.execute_activity( + activity_executed_by_update_handler_to_perform_compensation, + start_to_close_timeout=timedelta(seconds=10), + ) + self._update_compensation_done = True + + @workflow.query + def workflow_compensation_done(self) -> bool: + return self._workflow_compensation_done + + @workflow.query + def update_compensation_done(self) -> bool: + return self._update_compensation_done + + # Methods below this point are placeholders for the actual application logic + # that you would perform in your main workflow method or update handler. + # They can be ignored unless you are interested in the implementation + # details of this sample. + + async def _my_update_application_logic(self) -> str: + self._update_started = True + await workflow.execute_activity( + activity_executed_by_update_handler, + start_to_close_timeout=timedelta(seconds=10), + ) + return "update-result" + + async def _my_workflow_application_logic( + self, input: WorkflowInput + ) -> WorkflowResult: + # Wait until handlers have started, so that we are demonstrating that we + # wait for them to finish. + await workflow.wait_condition(lambda: self._update_started) + if input.exit_type == WorkflowExitType.SUCCESS: + return WorkflowResult(data="workflow-result") + elif input.exit_type == WorkflowExitType.FAILURE: + raise exceptions.ApplicationError("deliberately failing workflow") + elif input.exit_type == WorkflowExitType.CANCELLATION: + # Block forever; the starter will send a workflow cancellation request. + await asyncio.Future() + raise AssertionError("unreachable") + + +def is_workflow_exit_exception(e: BaseException) -> bool: + # 👉 If you have set additional failure_exception_types you should also + # check for these here. + return isinstance(e, (asyncio.CancelledError, exceptions.FailureError)) diff --git a/open_telemetry/README.md b/open_telemetry/README.md index e742b3dd..06df7326 100644 --- a/open_telemetry/README.md +++ b/open_telemetry/README.md @@ -4,35 +4,34 @@ This sample shows how to configure OpenTelemetry to capture workflow traces and For this sample, the optional `open_telemetry` dependency group must be included. To include, run: - poetry install --with open_telemetry + uv sync --group open-telemetry -To run, first see [README.md](../README.md) for prerequisites. Then run the following to start a Jaeger container -with OTLP collector enabled to collect and view the trace results: +To run, first see [README.md](../README.md) for prerequisites. Then run the following to start an [Aspire](https://hub.docker.com/r/microsoft/dotnet-aspire-dashboard/) OTEL collector - docker run -d --name jaeger \ - -e COLLECTOR_OTLP_ENABLED=true \ - -p 16686:16686 \ - -p 4317:4317 \ - -p 4318:4318 \ - jaegertracing/all-in-one:latest + docker compose up Now, from this directory, start the worker in its own terminal: - poetry run python worker.py + uv run worker.py -This will start the worker. Then, in another terminal, run the following to execute the workflow: +Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run starter.py -The workflow should complete with the hello result. The workflow trace can now be viewed in Jaeger at -http://localhost:16686/. Under service, select `my-service` and "Find Traces". The workflow should appear and when -clicked, may look something like: +The workflow should complete with the hello result. -![Jaeger Screenshot](jaeger-screenshot.png) +Now view the Aspire UI at http://localhost:18888/. + +To view metrics sent describing the worker and the workflow that was executed, select `Metrics` on the left and under "Select a resource" select "temporal-core-sdk". It may look like this: + +![Aspire metrics screenshot](aspire-metrics-screenshot.png) + + +To view workflow spans, select `Traces` on the left and under "Select a resource" select "temporal-core-sdk". It may look like this: + +![Aspire traces screenshot](aspire-traces-screenshot.png) Note, in-workflow spans do not have a time associated with them. This is by intention since due to replay. In OpenTelemetry, only the process that started the span may end it. But in Temporal a span may cross workers/processes. Therefore we intentionally start-then-end in-workflow spans immediately. So while the start time and hierarchy is -accurate, the duration is not. - -The metrics should have been dumped out in the terminal where the OpenTelemetry collector container is running. +accurate, the duration is not. \ No newline at end of file diff --git a/open_telemetry/aspire-metrics-screenshot.png b/open_telemetry/aspire-metrics-screenshot.png new file mode 100644 index 00000000..ed4ab8e9 Binary files /dev/null and b/open_telemetry/aspire-metrics-screenshot.png differ diff --git a/open_telemetry/aspire-traces-screenshot.png b/open_telemetry/aspire-traces-screenshot.png new file mode 100644 index 00000000..298c07eb Binary files /dev/null and b/open_telemetry/aspire-traces-screenshot.png differ diff --git a/open_telemetry/docker-compose.yaml b/open_telemetry/docker-compose.yaml new file mode 100644 index 00000000..da3c02ae --- /dev/null +++ b/open_telemetry/docker-compose.yaml @@ -0,0 +1,8 @@ +services: + aspire-dashboard: + environment: + Dashboard__Frontend__AuthMode: Unsecured + image: mcr.microsoft.com/dotnet/aspire-dashboard:8.0 + ports: + - 4317:18889 + - 18888:18888 \ No newline at end of file diff --git a/open_telemetry/jaeger-screenshot.png b/open_telemetry/jaeger-screenshot.png deleted file mode 100644 index 8376d49c..00000000 Binary files a/open_telemetry/jaeger-screenshot.png and /dev/null differ diff --git a/open_telemetry/otel-metrics-collector-config.yaml b/open_telemetry/otel-metrics-collector-config.yaml deleted file mode 100644 index 45265f88..00000000 --- a/open_telemetry/otel-metrics-collector-config.yaml +++ /dev/null @@ -1,15 +0,0 @@ -receivers: - otlp: - protocols: - grpc: -exporters: - logging: - loglevel: debug -processors: - batch: -service: - pipelines: - metrics: - receivers: [otlp] - exporters: [logging] - processors: [batch] \ No newline at end of file diff --git a/open_telemetry/worker.py b/open_telemetry/worker.py index f4347152..4b344123 100644 --- a/open_telemetry/worker.py +++ b/open_telemetry/worker.py @@ -1,7 +1,6 @@ import asyncio from datetime import timedelta -import opentelemetry.context from opentelemetry import trace from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.resources import SERVICE_NAME, Resource diff --git a/patching/README.md b/patching/README.md index 61106f31..ac7c55c2 100644 --- a/patching/README.md +++ b/patching/README.md @@ -8,15 +8,15 @@ To run, first see [README.md](../README.md) for prerequisites. Then follow the p This stage is for existing running workflows. To simulate our initial workflow, run the worker in a separate terminal: - poetry run python worker.py --workflow initial + uv run worker.py --workflow initial Now we can start this workflow: - poetry run python starter.py --start-workflow initial-workflow-id + uv run starter.py --start-workflow initial-workflow-id This will output "Started workflow with ID initial-workflow-id and ...". Now query this workflow: - poetry run python starter.py --query-workflow initial-workflow-id + uv run starter.py --query-workflow initial-workflow-id This will output "Query result for ID initial-workflow-id: pre-patch". @@ -25,21 +25,21 @@ This will output "Query result for ID initial-workflow-id: pre-patch". This stage is for needing to run old and new workflows at the same time. To simulate our patched workflow, stop the worker from before and start it again with the patched workflow: - poetry run python worker.py --workflow patched + uv run worker.py --workflow patched Now let's start another workflow with this patched code: - poetry run python starter.py --start-workflow patched-workflow-id + uv run starter.py --start-workflow patched-workflow-id This will output "Started workflow with ID patched-workflow-id and ...". Now query the old workflow that's still running: - poetry run python starter.py --query-workflow initial-workflow-id + uv run starter.py --query-workflow initial-workflow-id This will output "Query result for ID initial-workflow-id: pre-patch" since it is pre-patch. But if we execute a query against the new code: - poetry run python starter.py --query-workflow patched-workflow-id + uv run starter.py --query-workflow patched-workflow-id We get "Query result for ID patched-workflow-id: post-patch". This is how old workflow code can take old paths and new workflow code can take new paths. @@ -50,22 +50,22 @@ Once we know that all workflows that started with the initial code from "Stage 1 the patch so we can deprecate it. To use the patch deprecated workflow, stop the workflow from before and start it again with: - poetry run python worker.py --workflow patch-deprecated + uv run worker.py --workflow patch-deprecated All workflows in "Stage 2" and any new workflows will work. Now let's start another workflow with this patch deprecated code: - poetry run python starter.py --start-workflow patch-deprecated-workflow-id + uv run starter.py --start-workflow patch-deprecated-workflow-id This will output "Started workflow with ID patch-deprecated-workflow-id and ...". Now query the patched workflow that's still running: - poetry run python starter.py --query-workflow patched-workflow-id + uv run starter.py --query-workflow patched-workflow-id This will output "Query result for ID patched-workflow-id: post-patch". And if we execute a query against the latest workflow: - poetry run python starter.py --query-workflow patch-deprecated-workflow-id + uv run starter.py --query-workflow patch-deprecated-workflow-id As expected, this will output "Query result for ID patch-deprecated-workflow-id: post-patch". @@ -75,22 +75,22 @@ Once we know we don't even have any workflows running on "Stage 2" or before (i. both code paths), we can just remove the patch deprecation altogether. To use the patch complete workflow, stop the workflow from before and start it again with: - poetry run python worker.py --workflow patch-complete + uv run worker.py --workflow patch-complete All workflows in "Stage 3" and any new workflows will work. Now let's start another workflow with this patch complete code: - poetry run python starter.py --start-workflow patch-complete-workflow-id + uv run starter.py --start-workflow patch-complete-workflow-id This will output "Started workflow with ID patch-complete-workflow-id and ...". Now query the patch deprecated workflow that's still running: - poetry run python starter.py --query-workflow patch-deprecated-workflow-id + uv run starter.py --query-workflow patch-deprecated-workflow-id This will output "Query result for ID patch-deprecated-workflow-id: post-patch". 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Then, run the following from this directory to run the sample: -```bash -poetry run python run_worker.py -poetry run python run_frequent.py -``` + uv run run_worker.py + +Then, in another terminal, run the following to execute the workflow: + + uv run run_frequent.py The Workflow will continue to poll the service and heartbeat on every iteration until it succeeds. diff --git a/polling/frequent/activities.py b/polling/frequent/activities.py index bc667b7f..a112b417 100644 --- a/polling/frequent/activities.py +++ b/polling/frequent/activities.py @@ -1,23 +1,26 @@ import asyncio -from dataclasses import dataclass from temporalio import activity -from polling.test_service import TestService - - -@dataclass -class ComposeGreetingInput: - greeting: str - name: str +from polling.test_service import ComposeGreetingInput, get_service_result @activity.defn async def compose_greeting(input: ComposeGreetingInput) -> str: - test_service = TestService() while True: try: - result = test_service.get_service_result(input) - return result - except Exception: + try: + result = await get_service_result(input) + activity.logger.info(f"Exiting activity ${result}") + return result + except Exception: + # swallow exception since service is down + activity.logger.debug("Failed, trying again shortly", exc_info=True) + activity.heartbeat("Invoking activity") + await asyncio.sleep(1) + except asyncio.CancelledError: + # activity was either cancelled or workflow was completed or worker shut down + # if you need to clean up you can catch this. + # Here we are just reraising the exception + raise diff --git a/polling/frequent/run_frequent.py b/polling/frequent/run_frequent.py index bc43ccfd..664a677c 100644 --- a/polling/frequent/run_frequent.py +++ b/polling/frequent/run_frequent.py @@ -1,7 +1,8 @@ import asyncio from temporalio.client import Client -from workflows import GreetingWorkflow + +from polling.frequent.workflows import GreetingWorkflow async def main(): diff --git a/polling/frequent/run_worker.py b/polling/frequent/run_worker.py index 97b327ff..00fcc27e 100644 --- a/polling/frequent/run_worker.py +++ b/polling/frequent/run_worker.py @@ -1,9 +1,10 @@ import asyncio -from activities import compose_greeting from temporalio.client import Client from temporalio.worker import Worker -from workflows import GreetingWorkflow + +from polling.frequent.activities import compose_greeting +from polling.frequent.workflows import GreetingWorkflow async def main(): diff --git a/polling/frequent/workflows.py b/polling/frequent/workflows.py index c5329ad9..0dac0a49 100644 --- a/polling/frequent/workflows.py +++ b/polling/frequent/workflows.py @@ -3,7 +3,7 @@ from temporalio import workflow with workflow.unsafe.imports_passed_through(): - from activities import ComposeGreetingInput, compose_greeting + from polling.frequent.activities import ComposeGreetingInput, compose_greeting @workflow.defn diff --git a/polling/infrequent/README.md b/polling/infrequent/README.md index 7c0a3225..b6afeb56 100644 --- a/polling/infrequent/README.md +++ b/polling/infrequent/README.md @@ -13,10 +13,12 @@ To run, first see [README.md](../../README.md) for prerequisites. Then, run the following from this directory to run the sample: -```bash -poetry run python run_worker.py -poetry run python run_infrequent.py -``` + uv run run_worker.py + +Then, in another terminal, run the following to execute the workflow: + + uv run run_infrequent.py + Since the test service simulates being _down_ for four polling attempts and then returns _OK_ on the fifth poll attempt, the Workflow will perform four Activity retries with a 60-second poll interval, and then return the service result on the successful fifth attempt. diff --git a/polling/infrequent/activities.py b/polling/infrequent/activities.py index 2bd71587..b3db1aed 100644 --- a/polling/infrequent/activities.py +++ b/polling/infrequent/activities.py @@ -1,19 +1,10 @@ -from dataclasses import dataclass - from temporalio import activity -from polling.test_service import TestService - - -@dataclass -class ComposeGreetingInput: - greeting: str - name: str +from polling.test_service import ComposeGreetingInput, get_service_result @activity.defn async def compose_greeting(input: ComposeGreetingInput) -> str: - test_service = TestService() # If this raises an exception because it's not done yet, the activity will # continually be scheduled for retry - return await test_service.get_service_result(input) + return await get_service_result(input) diff --git a/polling/infrequent/run_infrequent.py b/polling/infrequent/run_infrequent.py index 0056a36c..7cf206f2 100644 --- a/polling/infrequent/run_infrequent.py +++ b/polling/infrequent/run_infrequent.py @@ -1,7 +1,8 @@ import asyncio from temporalio.client import Client -from workflows import GreetingWorkflow + +from polling.infrequent.workflows import GreetingWorkflow async def main(): diff --git a/polling/infrequent/run_worker.py b/polling/infrequent/run_worker.py index 27db61de..f600b949 100644 --- a/polling/infrequent/run_worker.py +++ b/polling/infrequent/run_worker.py @@ -1,9 +1,10 @@ import asyncio -from activities import compose_greeting from temporalio.client import Client from temporalio.worker import Worker -from workflows import GreetingWorkflow + +from polling.infrequent.activities import compose_greeting +from polling.infrequent.workflows import GreetingWorkflow async def main(): diff --git a/polling/infrequent/workflows.py b/polling/infrequent/workflows.py index 013a3b91..35769573 100644 --- a/polling/infrequent/workflows.py +++ b/polling/infrequent/workflows.py @@ -4,7 +4,7 @@ from temporalio.common import RetryPolicy with workflow.unsafe.imports_passed_through(): - from activities import ComposeGreetingInput, compose_greeting + from polling.infrequent.activities import ComposeGreetingInput, compose_greeting @workflow.defn diff --git a/polling/periodic_sequence/README.md b/polling/periodic_sequence/README.md index 65fe0669..d632862d 100644 --- a/polling/periodic_sequence/README.md +++ b/polling/periodic_sequence/README.md @@ -8,10 +8,12 @@ To run, first see [README.md](../../README.md) for prerequisites. Then, run the following from this directory to run the sample: -```bash -poetry run python run_worker.py -poetry run python run_periodic.py -``` + uv run run_worker.py + +Then, in another terminal, run the following to execute the workflow: + + uv run run_periodic.py + This will start a Workflow and Child Workflow to periodically poll an Activity. The Parent Workflow is not aware about the Child Workflow calling Continue-As-New, and it gets notified when it completes (or fails). \ No newline at end of file diff --git a/polling/periodic_sequence/activities.py b/polling/periodic_sequence/activities.py index 1a1196c6..87b69890 100644 --- a/polling/periodic_sequence/activities.py +++ b/polling/periodic_sequence/activities.py @@ -1,14 +1,8 @@ -from dataclasses import dataclass +from typing import Any, NoReturn from temporalio import activity -@dataclass -class ComposeGreetingInput: - greeting: str - name: str - - @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: +async def compose_greeting(input: Any) -> NoReturn: raise RuntimeError("Service is down") diff --git a/polling/periodic_sequence/run_periodic.py b/polling/periodic_sequence/run_periodic.py index 5853d467..f2ddcf7a 100644 --- a/polling/periodic_sequence/run_periodic.py +++ b/polling/periodic_sequence/run_periodic.py @@ -1,7 +1,8 @@ import asyncio from temporalio.client import Client -from workflows import GreetingWorkflow + +from polling.periodic_sequence.workflows import GreetingWorkflow async def main(): diff --git a/polling/periodic_sequence/run_worker.py b/polling/periodic_sequence/run_worker.py index 8c0591f9..e04ac4dc 100644 --- a/polling/periodic_sequence/run_worker.py +++ b/polling/periodic_sequence/run_worker.py @@ -1,9 +1,10 @@ import asyncio -from activities import compose_greeting from temporalio.client import Client from temporalio.worker import Worker -from workflows import ChildWorkflow, GreetingWorkflow + +from polling.periodic_sequence.activities import compose_greeting +from polling.periodic_sequence.workflows import ChildWorkflow, GreetingWorkflow async def main(): diff --git a/polling/periodic_sequence/workflows.py b/polling/periodic_sequence/workflows.py index 12045bc8..3c6d7555 100644 --- a/polling/periodic_sequence/workflows.py +++ b/polling/periodic_sequence/workflows.py @@ -6,7 +6,8 @@ from temporalio.exceptions import ActivityError with workflow.unsafe.imports_passed_through(): - from activities import ComposeGreetingInput, compose_greeting + from polling.periodic_sequence.activities import compose_greeting + from polling.test_service import ComposeGreetingInput @workflow.defn @@ -37,6 +38,5 @@ async def run(self, name: str) -> str: except ActivityError: workflow.logger.error("Activity failed, retrying in 1 seconds") await asyncio.sleep(1) - workflow.continue_as_new(name) - raise Exception("Polling failed after all attempts") + workflow.continue_as_new(name) diff --git a/polling/test_service.py b/polling/test_service.py index 5bcec7cd..490f8476 100644 --- a/polling/test_service.py +++ b/polling/test_service.py @@ -1,14 +1,23 @@ -class TestService: - def __init__(self): - self.try_attempts = 0 - self.error_attempts = 5 - - def get_service_result(self, input): - print( - f"Attempt {self.try_attempts}" - f" of {self.error_attempts} to invoke service" - ) - self.try_attempts += 1 - if self.try_attempts % self.error_attempts == 0: - return f"{input.greeting}, {input.name}!" - raise Exception("service is down") +from dataclasses import dataclass +from typing import Counter + +from temporalio import activity + +attempts = Counter[str]() +ERROR_ATTEMPTS = 5 + + +@dataclass +class ComposeGreetingInput: + greeting: str + name: str + + +async def get_service_result(input): + workflow_id = activity.info().workflow_id + attempts[workflow_id] += 1 + + print(f"Attempt {attempts[workflow_id]} of {ERROR_ATTEMPTS} to invoke service") + if attempts[workflow_id] == ERROR_ATTEMPTS: + return f"{input.greeting}, {input.name}!" + raise Exception("service is down") diff --git a/prometheus/README.md b/prometheus/README.md index 6f605d62..091d5171 100644 --- a/prometheus/README.md +++ b/prometheus/README.md @@ -5,13 +5,13 @@ This sample shows how to have SDK Prometheus metrics made available via HTTP. To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker and the metrics will be visible for this process at http://127.0.0.1:9000/metrics. Then, in another terminal, run the following to execute a workflow: - poetry run python starter.py + uv run starter.py After executing the workflow, the process will stay open so the metrics if this separate process can be accessed at http://127.0.0.1:9001/metrics. \ No newline at end of file diff --git a/pydantic_converter/README.md b/pydantic_converter/README.md index 5914fa3d..bdbf0329 100644 --- a/pydantic_converter/README.md +++ b/pydantic_converter/README.md @@ -1,31 +1,19 @@ # Pydantic Converter Sample -This sample shows how to create a custom Pydantic converter to properly serialize Pydantic models. +This sample shows how to use the Pydantic data converter. -For this sample, the optional `pydantic` dependency group must be included. To include, run: +For this sample, the optional `pydantic_converter` dependency group must be included. To include, run: - poetry install --with pydantic + uv sync --group pydantic-converter To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run starter.py In the worker terminal, the workflow and its activity will log that it received the Pydantic models. In the starter terminal, the Pydantic models in the workflow result will be logged. - -### Notes - -This is the preferred way to use Pydantic models with Temporal Python SDK. The converter code is small and meant to -embed into other projects. - -This sample also demonstrates use of `datetime` inside of Pydantic models. Due to a known issue with the Temporal -sandbox, this class is seen by Pydantic as `date` instead of `datetime` upon deserialization. This is due to a -[known Python issue](https://github.com/python/cpython/issues/89010) where, when we proxy the `datetime` class in the -sandbox to prevent non-deterministic calls like `now()`, `issubclass` fails for the proxy type causing Pydantic to think -it's a `date` instead. In `worker.py`, we have shown a workaround of disabling restrictions on `datetime` which solves -this issue but no longer protects against workflow developers making non-deterministic calls in that module. \ No newline at end of file diff --git a/pydantic_converter/starter.py b/pydantic_converter/starter.py index 5aceca6e..7cc4cc2d 100644 --- a/pydantic_converter/starter.py +++ b/pydantic_converter/starter.py @@ -4,8 +4,8 @@ from ipaddress import IPv4Address from temporalio.client import Client +from temporalio.contrib.pydantic import pydantic_data_converter -from pydantic_converter.converter import pydantic_data_converter from pydantic_converter.worker import MyPydanticModel, MyWorkflow @@ -29,7 +29,7 @@ async def main(): some_date=datetime(2001, 2, 3, 4, 5, 6), ), ], - id=f"pydantic_converter-workflow-id", + id="pydantic_converter-workflow-id", task_queue="pydantic_converter-task-queue", ) logging.info("Got models from client: %s" % result) diff --git a/pydantic_converter/worker.py b/pydantic_converter/worker.py index b7e0bedf..eac0966c 100644 --- a/pydantic_converter/worker.py +++ b/pydantic_converter/worker.py @@ -1,5 +1,4 @@ import asyncio -import dataclasses import logging from datetime import datetime, timedelta from ipaddress import IPv4Address @@ -8,17 +7,12 @@ from temporalio import activity, workflow from temporalio.client import Client from temporalio.worker import Worker -from temporalio.worker.workflow_sandbox import ( - SandboxedWorkflowRunner, - SandboxRestrictions, -) -# We always want to pass through external modules to the sandbox that we know -# are safe for workflow use +# Always pass through external modules to the sandbox that you know are safe for +# workflow use with workflow.unsafe.imports_passed_through(): from pydantic import BaseModel - - from pydantic_converter.converter import pydantic_data_converter + from temporalio.contrib.pydantic import pydantic_data_converter class MyPydanticModel(BaseModel): @@ -42,29 +36,6 @@ async def run(self, models: List[MyPydanticModel]) -> List[MyPydanticModel]: ) -# Due to known issues with Pydantic's use of issubclass and our inability to -# override the check in sandbox, Pydantic will think datetime is actually date -# in the sandbox. At the expense of protecting against datetime.now() use in -# workflows, we're going to remove datetime module restrictions. See sdk-python -# README's discussion of known sandbox issues for more details. -def new_sandbox_runner() -> SandboxedWorkflowRunner: - # TODO(cretz): Use with_child_unrestricted when https://github.com/temporalio/sdk-python/issues/254 - # is fixed and released - invalid_module_member_children = dict( - SandboxRestrictions.invalid_module_members_default.children - ) - del invalid_module_member_children["datetime"] - return SandboxedWorkflowRunner( - restrictions=dataclasses.replace( - SandboxRestrictions.default, - invalid_module_members=dataclasses.replace( - SandboxRestrictions.invalid_module_members_default, - children=invalid_module_member_children, - ), - ) - ) - - interrupt_event = asyncio.Event() @@ -81,7 +52,6 @@ async def main(): task_queue="pydantic_converter-task-queue", workflows=[MyWorkflow], activities=[my_activity], - workflow_runner=new_sandbox_runner(), ): # Wait until interrupted print("Worker started, ctrl+c to exit") diff --git a/pydantic_converter_v1/README.md b/pydantic_converter_v1/README.md new file mode 100644 index 00000000..a38b00fc --- /dev/null +++ b/pydantic_converter_v1/README.md @@ -0,0 +1,31 @@ +# Pydantic v1 Converter Sample + +**This sample shows how to use Pydantic v1 with Temporal. This is not recommended: use Pydantic v2 if possible, and use the +main [pydantic_converter](../pydantic_converter/README.md) sample.** + +To install, run: + + uv sync --group pydantic-converter + uv run pip uninstall pydantic pydantic-core + uv run pip install pydantic==1.10 + +To run, first see the root [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +worker: + + uv run worker.py + +This will start the worker. Then, in another terminal, run the following to execute the workflow: + + uv run starter.py + +In the worker terminal, the workflow and its activity will log that it received the Pydantic models. In the starter +terminal, the Pydantic models in the workflow result will be logged. + +### Notes + +This sample also demonstrates use of `datetime` inside of Pydantic v1 models. Due to a known issue with the Temporal +sandbox, this class is seen by Pydantic v1 as `date` instead of `datetime` upon deserialization. This is due to a +[known Python issue](https://github.com/python/cpython/issues/89010) where, when we proxy the `datetime` class in the +sandbox to prevent non-deterministic calls like `now()`, `issubclass` fails for the proxy type causing Pydantic v1 to think +it's a `date` instead. In `worker.py`, we have shown a workaround of disabling restrictions on `datetime` which solves +this issue but no longer protects against workflow developers making non-deterministic calls in that module. \ No newline at end of file diff --git a/pydantic_converter_v1/__init__.py b/pydantic_converter_v1/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/pydantic_converter/converter.py b/pydantic_converter_v1/converter.py similarity index 89% rename from pydantic_converter/converter.py rename to pydantic_converter_v1/converter.py index a3c1cee6..81997e81 100644 --- a/pydantic_converter/converter.py +++ b/pydantic_converter_v1/converter.py @@ -42,9 +42,11 @@ class PydanticPayloadConverter(CompositePayloadConverter): def __init__(self) -> None: super().__init__( *( - c - if not isinstance(c, JSONPlainPayloadConverter) - else PydanticJSONPayloadConverter() + ( + c + if not isinstance(c, JSONPlainPayloadConverter) + else PydanticJSONPayloadConverter() + ) for c in DefaultPayloadConverter.default_encoding_payload_converters ) ) diff --git a/pydantic_converter_v1/starter.py b/pydantic_converter_v1/starter.py new file mode 100644 index 00000000..8ab58bdc --- /dev/null +++ b/pydantic_converter_v1/starter.py @@ -0,0 +1,39 @@ +import asyncio +import logging +from datetime import datetime +from ipaddress import IPv4Address + +from temporalio.client import Client + +from pydantic_converter_v1.converter import pydantic_data_converter +from pydantic_converter_v1.worker import MyPydanticModel, MyWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + # Connect client using the Pydantic converter + client = await Client.connect( + "localhost:7233", data_converter=pydantic_data_converter + ) + + # Run workflow + result = await client.execute_workflow( + MyWorkflow.run, + [ + MyPydanticModel( + some_ip=IPv4Address("127.0.0.1"), + some_date=datetime(2000, 1, 2, 3, 4, 5), + ), + MyPydanticModel( + some_ip=IPv4Address("127.0.0.2"), + some_date=datetime(2001, 2, 3, 4, 5, 6), + ), + ], + id="pydantic_converter-workflow-id", + task_queue="pydantic_converter-task-queue", + ) + logging.info("Got models from client: %s" % result) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/pydantic_converter_v1/worker.py b/pydantic_converter_v1/worker.py new file mode 100644 index 00000000..5c22b6f1 --- /dev/null +++ b/pydantic_converter_v1/worker.py @@ -0,0 +1,98 @@ +import asyncio +import dataclasses +import logging +from datetime import datetime, timedelta +from ipaddress import IPv4Address +from typing import List + +from temporalio import activity, workflow +from temporalio.client import Client +from temporalio.worker import Worker +from temporalio.worker.workflow_sandbox import ( + SandboxedWorkflowRunner, + SandboxRestrictions, +) + +# We always want to pass through external modules to the sandbox that we know +# are safe for workflow use +with workflow.unsafe.imports_passed_through(): + from pydantic import BaseModel + + from pydantic_converter_v1.converter import pydantic_data_converter + + +class MyPydanticModel(BaseModel): + some_ip: IPv4Address + some_date: datetime + + +@activity.defn +async def my_activity(models: List[MyPydanticModel]) -> List[MyPydanticModel]: + activity.logger.info("Got models in activity: %s" % models) + return models + + +@workflow.defn +class MyWorkflow: + @workflow.run + async def run(self, models: List[MyPydanticModel]) -> List[MyPydanticModel]: + workflow.logger.info("Got models in workflow: %s" % models) + return await workflow.execute_activity( + my_activity, models, start_to_close_timeout=timedelta(minutes=1) + ) + + +# Due to known issues with Pydantic's use of issubclass and our inability to +# override the check in sandbox, Pydantic will think datetime is actually date +# in the sandbox. At the expense of protecting against datetime.now() use in +# workflows, we're going to remove datetime module restrictions. See sdk-python +# README's discussion of known sandbox issues for more details. +def new_sandbox_runner() -> SandboxedWorkflowRunner: + # TODO(cretz): Use with_child_unrestricted when https://github.com/temporalio/sdk-python/issues/254 + # is fixed and released + invalid_module_member_children = dict( + SandboxRestrictions.invalid_module_members_default.children + ) + del invalid_module_member_children["datetime"] + return SandboxedWorkflowRunner( + restrictions=dataclasses.replace( + SandboxRestrictions.default, + invalid_module_members=dataclasses.replace( + SandboxRestrictions.invalid_module_members_default, + children=invalid_module_member_children, + ), + ) + ) + + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + # Connect client using the Pydantic converter + client = await Client.connect( + "localhost:7233", data_converter=pydantic_data_converter + ) + + # Run a worker for the workflow + async with Worker( + client, + task_queue="pydantic_converter-task-queue", + workflows=[MyWorkflow], + activities=[my_activity], + workflow_runner=new_sandbox_runner(), + ): + # Wait until interrupted + print("Worker started, ctrl+c to exit") + await interrupt_event.wait() + print("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/pyproject.toml b/pyproject.toml index 7a42f775..ea908cd3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,69 +1,128 @@ -[tool.poetry] +[project] name = "temporalio-samples" version = "0.1a1" description = "Temporal.io Python SDK samples" -license = "MIT" -authors = ["Temporal Technologies Inc "] +authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] +requires-python = "~=3.9" readme = "README.md" -homepage = "https://github.com/temporalio/samples-python" -repository = "https://github.com/temporalio/samples-python" -documentation = "https://docs.temporal.io/docs/python" -packages = [ - { include = "**/*.py", from = "." } -] +license = "MIT" +dependencies = ["temporalio>=1.11.1,<2"] -[tool.poetry.urls] +[project.urls] +Homepage = "https://github.com/temporalio/samples-python" +Repository = "https://github.com/temporalio/samples-python" +Documentation = "https://docs.temporal.io/docs/python" "Bug Tracker" = "https://github.com/temporalio/samples-python/issues" -[tool.poetry.dependencies] -python = "^3.8" -temporalio = "^1.5.0" - -[tool.poetry.dev-dependencies] -black = "^22.3.0" -isort = "^5.10.1" -mypy = "^0.961" -pytest = "^7.1.2" -pytest-asyncio = "^0.18.3" - -# All sample-specific dependencies are in optional groups below, named after the -# sample they apply to - -[tool.poetry.group.dsl] -optional = true -dependencies = { pyyaml = "^6.0.1", types-pyyaml = "^6.0.12", dacite = "^1.8.1" } +[dependency-groups] +dev = [ + "black>=22.3.0,<23", + "isort>=5.10.1,<6", + "mypy>=1.4.1,<2", + "pytest>=7.1.2,<8", + "pytest-asyncio>=0.18.3,<0.19", + "frozenlist>=1.4.0,<2", + "types-pyyaml>=6.0.12.20241230,<7", +] +bedrock = ["boto3>=1.34.92,<2"] +dsl = [ + "pyyaml>=6.0.1,<7", + "types-pyyaml>=6.0.12,<7", + "dacite>=1.8.1,<2", +] +encryption = [ + "cryptography>=38.0.1,<39", + "aiohttp>=3.8.1,<4", +] +gevent = ["gevent==25.4.2 ; python_version >= '3.8'"] +langchain = [ + "langchain>=0.1.7,<0.2 ; python_version >= '3.8.1' and python_version < '4.0'", + "langchain-openai>=0.0.6,<0.0.7 ; python_version >= '3.8.1' and python_version < '4.0'", + "openai>=1.4.0,<2", + "fastapi>=0.105.0,<0.106", + "tqdm>=4.62.0,<5", + "uvicorn[standard]>=0.24.0.post1,<0.25", +] +open-telemetry = [ + "temporalio[opentelemetry]", + "opentelemetry-exporter-otlp-proto-grpc==1.18.0", +] +pydantic-converter = ["pydantic>=2.10.6,<3"] +sentry = ["sentry-sdk>=1.11.0,<2"] +trio-async = [ + "trio>=0.28.0,<0.29", + "trio-asyncio>=0.15.0,<0.16", +] +cloud-export-to-parquet = [ + "pandas>=2.2.2,<3 ; python_version >= '3.9' and python_version < '4.0'", + "numpy>=1.26.0,<2 ; python_version >= '3.9' and python_version < '3.13'", + "boto3>=1.34.89,<2", + "pyarrow>=19.0.1", +] -[tool.poetry.group.encryption] -optional = true -dependencies = { cryptography = "^38.0.1", aiohttp = "^3.8.1" } +[tool.uv] +default-groups = [ + "dev", + "bedrock", + "dsl", + "encryption", + "gevent", + "langchain", + "open-telemetry", + "pydantic-converter", + "sentry", + "trio-async", +] -[tool.poetry.group.gevent] -optional = true -dependencies = { gevent = { version = "^23.9.1", python = ">=3.8" } } +[tool.hatch.build.targets.sdist] +include = ["./**/*.py"] -[tool.poetry.group.open_telemetry] -optional = true -[tool.poetry.group.open_telemetry.dependencies] -temporalio = { version = "*", extras = ["opentelemetry"] } -opentelemetry-exporter-otlp-proto-grpc = "1.18.0" +[tool.hatch.build.targets.wheel] +include = ["./**/*.py"] +packages = [ + "activity_worker", + "bedrock", + "cloud_export_to_parquet", + "context_propagation", + "custom_converter", + "custom_decorator", + "custom_metric", + "dsl", + "encryption", + "gevent_async", + "hello", + "langchain", + "message_passing", + "open_telemetry", + "patching", + "polling", + "prometheus", + "pydantic_converter", + "pydantic_converter_v1", + "pyproject.toml", + "replay", + "schedules", + "sentry", + "sleep_for_days", + "tests", + "trio_async", + "updatable_timer", + "worker_specific_task_queues", + "worker_versioning", +] -[tool.poetry.group.pydantic] -optional = true -dependencies = { pydantic = "^1.10.4" } +[tool.hatch.build.targets.wheel.sources] +"./**/*.py" = "**/*.py" -[tool.poetry.group.sentry] -optional = true -dependencies = { sentry-sdk = "^1.11.0" } +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" [tool.poe.tasks] -format = [{cmd = "black ."}, {cmd = "isort ."}] -lint = [{cmd = "black --check ."}, {cmd = "isort --check-only ."}, {ref = "lint-types" }] -lint-types = "mypy --check-untyped-defs --namespace-packages ." -test = "pytest" - -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +format = [{cmd = "uv run black ."}, {cmd = "uv run isort ."}] +lint = [{cmd = "uv run black --check ."}, {cmd = "uv run isort --check-only ."}, {ref = "lint-types" }] +lint-types = "uv run mypy --check-untyped-defs --namespace-packages ." +test = "uv run pytest" [tool.pytest.ini_options] asyncio_mode = "auto" @@ -86,3 +145,4 @@ ignore_errors = true [[tool.mypy.overrides]] module = "opentelemetry.*" ignore_errors = true + diff --git a/replay/README.md b/replay/README.md index 3a67868a..4b220f9d 100644 --- a/replay/README.md +++ b/replay/README.md @@ -6,15 +6,15 @@ workflow histories. To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. Then, in another terminal, run the following to execute a workflow: - poetry run python starter.py + uv run starter.py Next, run the replayer: - poetry run python replayer.py + uv run replayer.py Which should produce some output like: diff --git a/resource_pool/README.md b/resource_pool/README.md new file mode 100644 index 00000000..143de0f3 --- /dev/null +++ b/resource_pool/README.md @@ -0,0 +1,48 @@ +# Resource Pool Sample + +This sample shows how to use a long-lived `ResourcePoolWorkflow` to allocate `resources` to `ResourceUserWorkflows`. +Each `ResourceUserWorkflow` runs several activities while it has ownership of a resource. Note that +`ResourcePoolWorkflow` is making resource allocation decisions based on in-memory state. + +Run the following from this directory to start the worker: + + uv run worker.py + +This will start the worker. Then, in another terminal, run the following to execute several `ResourceUserWorkflows`. + + uv run starter.py + +You should see output indicating that the `ResourcePoolWorkflow` serialized access to each resource. + +You can query the set of current resource resource holders with: + + tctl wf query -w resource_pool --qt get_current_holders + +# Other approaches + +There are simpler ways to manage concurrent access to resources. Consider using resource-specific workers/task queues, +and limiting the number of activity slots on the workers. The golang SDK also [sessions](https://docs.temporal.io/develop/go/sessions) +that allow workflows to pin themselves to workers. + +The technique in this sample is capable of more complex resource allocation than the options above, but it doesn't scale +as well. Specifically, it can: +- Manage access to a set of resources that is decoupled from the set of workers and task queues +- Run arbitrary code to place workloads on resources as they become available + +# Caveats + +This sample uses true locking (not leasing!) to avoid complexity and scaling concerns associated with heartbeating via +signals. Locking carries a risk where failure to unlock permanently removing a resource from the pool. However, with +Temporal's durable execution guarantees, this can only happen if: + +- A ResourceUserWorkflows times out (prohibited in the sample code) +- An operator terminates a ResourceUserWorkflows. (Temporal recommends canceling workflows instead of terminating them whenever possible.) +- You shut down your workers and never restart them (unhandled, but irrelevant) + +If a leak were to happen, you could discover the identity of the leaker using the query above, then: + + tctl wf signal -w resource_pool --name release_resource --input '{ "release_key": "" } + +Performance: A single ResourcePoolWorkflow scales to tens, but not hundreds, of request/release events per second. It is +best suited for allocating resources to long-running workflows. Actual performance will depend on your temporal server's +persistence layer. \ No newline at end of file diff --git a/resource_pool/__init__.py b/resource_pool/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/resource_pool/pool_client/__init__.py b/resource_pool/pool_client/__init__.py new file mode 100644 index 00000000..b8471d8a --- /dev/null +++ b/resource_pool/pool_client/__init__.py @@ -0,0 +1,2 @@ +from .resource_pool_client import ResourcePoolClient +from .resource_pool_workflow import ResourcePoolWorkflow diff --git a/resource_pool/pool_client/resource_pool_client.py b/resource_pool/pool_client/resource_pool_client.py new file mode 100644 index 00000000..b7183afa --- /dev/null +++ b/resource_pool/pool_client/resource_pool_client.py @@ -0,0 +1,101 @@ +from contextlib import asynccontextmanager +from datetime import timedelta +from typing import AsyncGenerator, Optional + +from temporalio import workflow + +from resource_pool.pool_client.resource_pool_workflow import ResourcePoolWorkflow +from resource_pool.shared import ( + AcquiredResource, + AcquireRequest, + AcquireResponse, + DetachedResource, +) + + +# Use this class in workflow code that that needs to run on locked resources. +class ResourcePoolClient: + def __init__(self, pool_workflow_id: str) -> None: + self.pool_workflow_id = pool_workflow_id + self.acquired_resources: list[AcquiredResource] = [] + + signal_name = f"assign_resource_{self.pool_workflow_id}" + if workflow.get_signal_handler(signal_name) is None: + workflow.set_signal_handler(signal_name, self._handle_acquire_response) + else: + raise RuntimeError( + f"{signal_name} already registered - if you use multiple ResourcePoolClients within the " + f"same workflow, they must use different pool_workflow_ids" + ) + + def _handle_acquire_response(self, response: AcquireResponse) -> None: + self.acquired_resources.append( + AcquiredResource( + resource=response.resource, release_key=response.release_key + ) + ) + + async def _send_acquire_signal(self) -> None: + await workflow.get_external_workflow_handle_for( + ResourcePoolWorkflow.run, self.pool_workflow_id + ).signal("acquire_resource", AcquireRequest(workflow.info().workflow_id)) + + async def _send_release_signal(self, acquired_resource: AcquiredResource) -> None: + await workflow.get_external_workflow_handle_for( + ResourcePoolWorkflow.run, self.pool_workflow_id + ).signal( + "release_resource", + AcquireResponse( + resource=acquired_resource.resource, + release_key=acquired_resource.release_key, + ), + ) + + @asynccontextmanager + async def acquire_resource( + self, + *, + reattach: Optional[DetachedResource] = None, + max_wait_time: timedelta = timedelta(minutes=5), + ) -> AsyncGenerator[AcquiredResource, None]: + _warn_when_workflow_has_timeouts() + + if reattach is None: + await self._send_acquire_signal() + await workflow.wait_condition( + lambda: len(self.acquired_resources) > 0, timeout=max_wait_time + ) + resource = self.acquired_resources.pop(0) + else: + resource = AcquiredResource( + resource=reattach.resource, release_key=reattach.release_key + ) + + # Can't happen, but the typechecker doesn't know about workflow.wait_condition + if resource is None: + raise RuntimeError("resource was None when it can't be") + + # During the yield, the calling workflow owns the resource. Note that this is a lock, not a lease! Our + # finally block will release the resource if an activity fails. This is why we asserted the lack of + # workflow-level timeouts above - the finally block wouldn't run if there was a timeout. + try: + yield resource + finally: + if not resource.detached: + await self._send_release_signal(resource) + + +def _warn_when_workflow_has_timeouts() -> None: + def has_timeout(timeout: Optional[timedelta]) -> bool: + # After continue_as_new, timeouts are 0, even if they were None before continue_as_new (and were not set in the + # continue_as_new call). + return timeout is not None and timeout > timedelta(0) + + if has_timeout(workflow.info().run_timeout): + workflow.logger.warning( + f"ResourceLockingWorkflow cannot have a run_timeout (found {workflow.info().run_timeout}) - this will leak locks" + ) + if has_timeout(workflow.info().execution_timeout): + workflow.logger.warning( + f"ResourceLockingWorkflow cannot have an execution_timeout (found {workflow.info().execution_timeout}) - this will leak locks" + ) diff --git a/resource_pool/pool_client/resource_pool_workflow.py b/resource_pool/pool_client/resource_pool_workflow.py new file mode 100644 index 00000000..2321f82d --- /dev/null +++ b/resource_pool/pool_client/resource_pool_workflow.py @@ -0,0 +1,149 @@ +from dataclasses import dataclass +from typing import Optional + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +from resource_pool.shared import AcquireRequest, AcquireResponse + + +# Internal to this workflow, we'll associate randomly generated release signal names with each acquire request. +@dataclass +class InternalAcquireRequest(AcquireRequest): + release_signal: Optional[str] + + +@dataclass +class ResourcePoolWorkflowInput: + # Key is resource, value is current holder of the resource (None if not held) + resources: dict[str, Optional[InternalAcquireRequest]] + waiters: list[InternalAcquireRequest] + + +@workflow.defn +class ResourcePoolWorkflow: + @workflow.init + def __init__(self, input: ResourcePoolWorkflowInput) -> None: + self.resources = input.resources + self.waiters = input.waiters + self.release_key_to_resource: dict[str, str] = {} + + for resource, holder in self.resources.items(): + if holder is not None and holder.release_signal is not None: + self.release_key_to_resource[holder.release_signal] = resource + + @workflow.signal + async def add_resources(self, resources: list[str]) -> None: + for resource in resources: + if resource in self.resources: + workflow.logger.warning( + f"Ignoring attempt to add already-existing resource: {resource}" + ) + else: + self.resources[resource] = None + + @workflow.signal + async def acquire_resource(self, request: AcquireRequest) -> None: + self.waiters.append( + InternalAcquireRequest(workflow_id=request.workflow_id, release_signal=None) + ) + workflow.logger.info( + f"workflow_id={request.workflow_id} is waiting for a resource" + ) + + @workflow.signal + async def release_resource(self, acquire_response: AcquireResponse) -> None: + release_key = acquire_response.release_key + resource = self.release_key_to_resource.get(release_key) + if resource is None: + workflow.logger.warning(f"Ignoring unknown release_key: {release_key}") + return + + holder = self.resources[resource] + if holder is None: + workflow.logger.warning( + f"Ignoring request to release resource that is not held: {resource}" + ) + return + + # Remove the current holder + workflow.logger.info( + f"workflow_id={holder.workflow_id} released resource {resource}" + ) + self.resources[resource] = None + del self.release_key_to_resource[release_key] + + @workflow.query + def get_current_holders(self) -> dict[str, Optional[InternalAcquireRequest]]: + return self.resources + + async def assign_resource( + self, resource: str, internal_request: InternalAcquireRequest + ) -> None: + workflow.logger.info( + f"workflow_id={internal_request.workflow_id} acquired resource {resource}" + ) + + requester = workflow.get_external_workflow_handle(internal_request.workflow_id) + try: + release_signal = str(workflow.uuid4()) + await requester.signal( + f"assign_resource_{workflow.info().workflow_id}", + AcquireResponse(release_key=release_signal, resource=resource), + ) + + internal_request.release_signal = release_signal + self.resources[resource] = internal_request + self.release_key_to_resource[release_signal] = resource + except ApplicationError as e: + if e.type == "ExternalWorkflowExecutionNotFound": + workflow.logger.info( + f"Could not assign resource {resource} to {internal_request.workflow_id}: {e.message}" + ) + else: + raise e + + async def assign_next_resource(self) -> bool: + if len(self.waiters) == 0: + return False + + next_free_resource = self.get_free_resource() + if next_free_resource is None: + return False + + next_waiter = self.waiters.pop(0) + await self.assign_resource(next_free_resource, next_waiter) + return True + + def get_free_resource(self) -> Optional[str]: + return next( + (resource for resource, holder in self.resources.items() if holder is None), + None, + ) + + def can_assign_resource(self) -> bool: + return len(self.waiters) > 0 and self.get_free_resource() is not None + + def should_continue_as_new(self) -> bool: + return ( + workflow.info().is_continue_as_new_suggested() + and workflow.all_handlers_finished() + ) + + @workflow.run + async def run(self, _: ResourcePoolWorkflowInput) -> None: + while True: + await workflow.wait_condition( + lambda: self.can_assign_resource() or self.should_continue_as_new() + ) + + if await self.assign_next_resource(): + continue + + if self.should_continue_as_new(): + workflow.continue_as_new( + ResourcePoolWorkflowInput( + resources=self.resources, + waiters=self.waiters, + ) + ) diff --git a/resource_pool/resource_user_workflow.py b/resource_pool/resource_user_workflow.py new file mode 100644 index 00000000..80ee7fba --- /dev/null +++ b/resource_pool/resource_user_workflow.py @@ -0,0 +1,88 @@ +import asyncio +from dataclasses import dataclass, field +from datetime import timedelta +from typing import Optional + +from temporalio import activity, workflow + +from resource_pool.pool_client import ResourcePoolClient +from resource_pool.shared import DetachedResource + + +@dataclass +class UseResourceActivityInput: + resource: str + iteration: str + + +@activity.defn +async def use_resource(input: UseResourceActivityInput) -> None: + info = activity.info() + activity.logger.info( + f"{info.workflow_id} starts using {input.resource} the {input.iteration} time" + ) + await asyncio.sleep(3) + activity.logger.info( + f"{info.workflow_id} done using {input.resource} the {input.iteration} time" + ) + + +@dataclass +class ResourceUserWorkflowInput: + # The id of the resource pool workflow to request a resource from + resource_pool_workflow_id: str + + # If set, this workflow will fail after the "first" or "second" activity. + iteration_to_fail_after: Optional[str] + + # If True, this workflow will continue as new after the last activity. The next iteration will run more activities, + # but will not continue as new. + should_continue_as_new: bool + + # Used to transfer resource ownership between iterations during continue_as_new + already_acquired_resource: Optional[DetachedResource] = field(default=None) + + +class FailWorkflowException(Exception): + pass + + +# Wait this long for a resource before giving up +MAX_RESOURCE_WAIT_TIME = timedelta(minutes=5) + + +@workflow.defn(failure_exception_types=[FailWorkflowException]) +class ResourceUserWorkflow: + @workflow.run + async def run(self, input: ResourceUserWorkflowInput) -> None: + pool_client = ResourcePoolClient(input.resource_pool_workflow_id) + + async with pool_client.acquire_resource( + reattach=input.already_acquired_resource + ) as acquired_resource: + for iteration in ["first", "second"]: + await workflow.execute_activity( + use_resource, + UseResourceActivityInput(acquired_resource.resource, iteration), + start_to_close_timeout=timedelta(seconds=10), + ) + + if iteration == input.iteration_to_fail_after: + workflow.logger.info( + f"Failing after iteration {input.iteration_to_fail_after}" + ) + raise FailWorkflowException() + + # This workflow only continues as new so it can demonstrate how to pass acquired resources across + # iterations. Ordinarily, such a short workflow would not use continue as new. + if input.should_continue_as_new: + detached_resource = acquired_resource.detach() + + next_input = ResourceUserWorkflowInput( + resource_pool_workflow_id=input.resource_pool_workflow_id, + iteration_to_fail_after=input.iteration_to_fail_after, + should_continue_as_new=False, + already_acquired_resource=detached_resource, + ) + + workflow.continue_as_new(next_input) diff --git a/resource_pool/shared.py b/resource_pool/shared.py new file mode 100644 index 00000000..3930bb72 --- /dev/null +++ b/resource_pool/shared.py @@ -0,0 +1,31 @@ +from dataclasses import dataclass, field + +RESOURCE_POOL_WORKFLOW_ID = "resource_pool" + + +@dataclass +class AcquireRequest: + workflow_id: str + + +@dataclass +class AcquireResponse: + release_key: str + resource: str + + +@dataclass +class DetachedResource: + resource: str + release_key: str + + +@dataclass +class AcquiredResource: + resource: str + release_key: str + detached: bool = field(default=False) + + def detach(self) -> DetachedResource: + self.detached = True + return DetachedResource(resource=self.resource, release_key=self.release_key) diff --git a/resource_pool/starter.py b/resource_pool/starter.py new file mode 100644 index 00000000..2ae1ab44 --- /dev/null +++ b/resource_pool/starter.py @@ -0,0 +1,66 @@ +import asyncio +from typing import Any + +from temporalio.client import Client, WorkflowFailureError, WorkflowHandle +from temporalio.common import WorkflowIDConflictPolicy + +from resource_pool.pool_client.resource_pool_workflow import ( + ResourcePoolWorkflow, + ResourcePoolWorkflowInput, +) +from resource_pool.resource_user_workflow import ( + ResourceUserWorkflow, + ResourceUserWorkflowInput, +) +from resource_pool.shared import RESOURCE_POOL_WORKFLOW_ID + + +async def main() -> None: + # Connect client + client = await Client.connect("localhost:7233") + + # Initialize the resource pool + resource_pool_handle = await client.start_workflow( + workflow=ResourcePoolWorkflow.run, + arg=ResourcePoolWorkflowInput( + resources={"resource_a": None, "resource_b": None}, + waiters=[], + ), + id=RESOURCE_POOL_WORKFLOW_ID, + task_queue="resource_pool-task-queue", + id_conflict_policy=WorkflowIDConflictPolicy.USE_EXISTING, + ) + + # Start the ResourceUserWorkflows + resource_user_handles: list[WorkflowHandle[Any, Any]] = [] + for i in range(0, 4): + input = ResourceUserWorkflowInput( + resource_pool_workflow_id=RESOURCE_POOL_WORKFLOW_ID, + iteration_to_fail_after=None, + should_continue_as_new=False, + ) + if i == 0: + input.should_continue_as_new = True + if i == 1: + input.iteration_to_fail_after = "first" + + handle = await client.start_workflow( + workflow=ResourceUserWorkflow.run, + arg=input, + id=f"resource-user-workflow-{i}", + task_queue="resource_pool-task-queue", + ) + resource_user_handles.append(handle) + + for handle in resource_user_handles: + try: + await handle.result() + except WorkflowFailureError: + pass + + # Clean up after ourselves. In the real world, the resource pool workflow would run forever. + await resource_pool_handle.terminate() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/resource_pool/worker.py b/resource_pool/worker.py new file mode 100644 index 00000000..cb3a06dd --- /dev/null +++ b/resource_pool/worker.py @@ -0,0 +1,31 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from resource_pool.pool_client.resource_pool_workflow import ResourcePoolWorkflow +from resource_pool.resource_user_workflow import ResourceUserWorkflow, use_resource + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + + # Start client + client = await Client.connect("localhost:7233") + + # Run a worker for the workflow + worker = Worker( + client, + task_queue="resource_pool-task-queue", + workflows=[ResourcePoolWorkflow, ResourceUserWorkflow], + activities=[ + use_resource, + ], + ) + + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/schedules/README.md b/schedules/README.md index 9bf40ca0..8327d769 100644 --- a/schedules/README.md +++ b/schedules/README.md @@ -4,18 +4,18 @@ These samples show how to schedule a Workflow Execution and control certain acti To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to run the `schedules/` sample: - poetry run python run_worker.py - poetry run python start_schedule.py + uv run run_worker.py + uv run start_schedule.py Replace `start_schedule.py` in the command with any other example filename to run it instead. - poetry run python backfill_schedule.py - poetry run python delete_schedule.py - poetry run python describe_schedule.py - poetry run python list_schedule.py - poetry run python pause_schedule.py + uv run backfill_schedule.py + uv run delete_schedule.py + uv run describe_schedule.py + uv run list_schedule.py + uv run pause_schedule.py python run python trigger_schedule.py - poetry run python update_schedule.py + uv run update_schedule.py - create: Creates a new Schedule. Newly created Schedules return a Schedule ID to be used in other Schedule commands. - backfill: Backfills the Schedule by going through the specified time periods as if they passed right now. diff --git a/schedules/backfill_schedule.py b/schedules/backfill_schedule.py index 65658409..769b6a78 100644 --- a/schedules/backfill_schedule.py +++ b/schedules/backfill_schedule.py @@ -16,7 +16,7 @@ async def main(): end_at=now - timedelta(minutes=9), overlap=ScheduleOverlapPolicy.ALLOW_ALL, ), - ), + ) if __name__ == "__main__": diff --git a/sentry/README.md b/sentry/README.md index 45977731..30667d5e 100644 --- a/sentry/README.md +++ b/sentry/README.md @@ -4,16 +4,16 @@ This sample shows how to configure [Sentry](https://sentry.io) to intercept and For this sample, the optional `sentry` dependency group must be included. To include, run: - poetry install --with sentry + uv sync --group sentry To run, first see [README.md](../README.md) for prerequisites. Set `SENTRY_DSN` environment variable to the Sentry DSN. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run starter.py The workflow should complete with the hello result. If you alter the workflow or the activity to raise an `ApplicationError` instead, it should appear in Sentry. \ No newline at end of file diff --git a/sentry/interceptor.py b/sentry/interceptor.py index f1737ed2..eceba41d 100644 --- a/sentry/interceptor.py +++ b/sentry/interceptor.py @@ -37,8 +37,10 @@ async def execute_activity(self, input: ExecuteActivityInput) -> Any: try: return await super().execute_activity(input) except Exception as e: - if len(input.args) == 1 and is_dataclass(input.args[0]): - set_context("temporal.activity.input", asdict(input.args[0])) + if len(input.args) == 1: + [arg] = input.args + if is_dataclass(arg) and not isinstance(arg, type): + set_context("temporal.activity.input", asdict(arg)) set_context("temporal.activity.info", activity.info().__dict__) capture_exception() raise e @@ -58,8 +60,10 @@ async def execute_workflow(self, input: ExecuteWorkflowInput) -> Any: try: return await super().execute_workflow(input) except Exception as e: - if len(input.args) == 1 and is_dataclass(input.args[0]): - set_context("temporal.workflow.input", asdict(input.args[0])) + if len(input.args) == 1: + [arg] = input.args + if is_dataclass(arg) and not isinstance(arg, type): + set_context("temporal.workflow.input", asdict(arg)) set_context("temporal.workflow.info", workflow.info().__dict__) if not workflow.unsafe.is_replaying(): diff --git a/sleep_for_days/README.md b/sleep_for_days/README.md new file mode 100644 index 00000000..766aefc9 --- /dev/null +++ b/sleep_for_days/README.md @@ -0,0 +1,18 @@ +# Sleep for Days + +This sample demonstrates how to create a Temporal workflow that runs forever, sending an email every 30 days. + +To run, first see the main [README.md](../../README.md) for prerequisites. + +Then create two terminals and `cd` to this directory. + +Run the worker in one terminal: + + uv run worker.py + +And execute the workflow in the other terminal: + + uv run starter.py + +This sample will run indefinitely until you send a signal to `complete`. See how to send a signal via Temporal CLI [here](https://docs.temporal.io/cli/workflow#signal). + diff --git a/sleep_for_days/__init__.py b/sleep_for_days/__init__.py new file mode 100644 index 00000000..04611d30 --- /dev/null +++ b/sleep_for_days/__init__.py @@ -0,0 +1 @@ +TASK_QUEUE = "sleep-for-days-task-queue" diff --git a/sleep_for_days/activities.py b/sleep_for_days/activities.py new file mode 100644 index 00000000..30972098 --- /dev/null +++ b/sleep_for_days/activities.py @@ -0,0 +1,18 @@ +from dataclasses import dataclass + +from temporalio import activity + + +@dataclass +class SendEmailInput: + email_msg: str + + +@activity.defn() +async def send_email(input: SendEmailInput) -> str: + """ + A stub Activity for sending an email. + """ + result = f"Email message: {input.email_msg}, sent" + activity.logger.info(result) + return result diff --git a/sleep_for_days/starter.py b/sleep_for_days/starter.py new file mode 100644 index 00000000..765842b2 --- /dev/null +++ b/sleep_for_days/starter.py @@ -0,0 +1,23 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client + +from sleep_for_days import TASK_QUEUE +from sleep_for_days.workflows import SleepForDaysWorkflow + + +async def main(client: Optional[Client] = None): + client = client or await Client.connect("localhost:7233") + wf_handle = await client.start_workflow( + SleepForDaysWorkflow.run, + id=f"sleep-for-days-workflow-id-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + ) + # Wait for workflow completion (runs indefinitely until it receives a signal) + print(await wf_handle.result()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sleep_for_days/worker.py b/sleep_for_days/worker.py new file mode 100644 index 00000000..d03ec726 --- /dev/null +++ b/sleep_for_days/worker.py @@ -0,0 +1,27 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from sleep_for_days import TASK_QUEUE +from sleep_for_days.activities import send_email +from sleep_for_days.workflows import SleepForDaysWorkflow + + +async def main(): + client = await Client.connect("localhost:7233") + + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[SleepForDaysWorkflow], + activities=[send_email], + ) + + await worker.run() + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + asyncio.run(main()) diff --git a/sleep_for_days/workflows.py b/sleep_for_days/workflows.py new file mode 100644 index 00000000..eff0b273 --- /dev/null +++ b/sleep_for_days/workflows.py @@ -0,0 +1,37 @@ +import asyncio +from dataclasses import dataclass +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from sleep_for_days.activities import SendEmailInput, send_email + + +@workflow.defn() +class SleepForDaysWorkflow: + def __init__(self) -> None: + self.is_complete = False + + @workflow.run + async def run(self) -> str: + while not self.is_complete: + await workflow.execute_activity( + send_email, + SendEmailInput("30 days until the next email"), + start_to_close_timeout=timedelta(seconds=10), + ) + await workflow.wait( + [ + asyncio.create_task(workflow.sleep(timedelta(days=30))), + asyncio.create_task( + workflow.wait_condition(lambda: self.is_complete) + ), + ], + return_when=asyncio.FIRST_COMPLETED, + ) + return "done!" + + @workflow.signal + def complete(self): + self.is_complete = True diff --git a/tests/conftest.py b/tests/conftest.py index 95294fb4..e63a059b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -41,7 +41,12 @@ def event_loop(): async def env(request) -> AsyncGenerator[WorkflowEnvironment, None]: env_type = request.config.getoption("--workflow-environment") if env_type == "local": - env = await WorkflowEnvironment.start_local() + env = await WorkflowEnvironment.start_local( + dev_server_extra_args=[ + "--dynamic-config-value", + "frontend.enableExecuteMultiOperation=true", + ] + ) elif env_type == "time-skipping": env = await WorkflowEnvironment.start_time_skipping() else: diff --git a/tests/context_propagation/__init__.py b/tests/context_propagation/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/context_propagation/workflow_test.py b/tests/context_propagation/workflow_test.py new file mode 100644 index 00000000..8de2120d --- /dev/null +++ b/tests/context_propagation/workflow_test.py @@ -0,0 +1,46 @@ +import uuid + +from temporalio import activity +from temporalio.client import Client +from temporalio.exceptions import ApplicationError +from temporalio.worker import Worker + +from context_propagation.interceptor import ContextPropagationInterceptor +from context_propagation.shared import user_id +from context_propagation.workflows import SayHelloWorkflow + + +async def test_workflow_with_context_propagator(client: Client): + # Mock out the activity to assert the context value + @activity.defn(name="say_hello_activity") + async def say_hello_activity_mock(name: str) -> str: + try: + assert user_id.get() == "test-user" + except Exception as err: + raise ApplicationError("Assertion fail", non_retryable=True) from err + return f"Mock for {name}" + + # Replace interceptors in client + new_config = client.config() + new_config["interceptors"] = [ContextPropagationInterceptor()] + client = Client(**new_config) + task_queue = f"tq-{uuid.uuid4()}" + + async with Worker( + client, + task_queue=task_queue, + activities=[say_hello_activity_mock], + workflows=[SayHelloWorkflow], + ): + # Set the user during start/signal, but unset after + token = user_id.set("test-user") + handle = await client.start_workflow( + SayHelloWorkflow.run, + "some-name", + id=f"wf-{uuid.uuid4()}", + task_queue=task_queue, + ) + await handle.signal(SayHelloWorkflow.signal_complete) + user_id.reset(token) + result = await handle.result() + assert result == "Mock for some-name" diff --git a/tests/custom_metric/__init__.py b/tests/custom_metric/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/custom_metric/workflow_test.py b/tests/custom_metric/workflow_test.py new file mode 100644 index 00000000..4e107b79 --- /dev/null +++ b/tests/custom_metric/workflow_test.py @@ -0,0 +1,32 @@ +import uuid + +from temporalio import activity +from temporalio.client import Client +from temporalio.worker import Worker + +from custom_metric.worker import StartTwoActivitiesWorkflow + +_TASK_QUEUE = "custom-metric-task-queue" + +activity_counter = 0 + + +async def test_custom_metric_workflow(client: Client): + @activity.defn(name="print_and_sleep") + async def print_message_mock(): + global activity_counter + activity_counter += 1 + + async with Worker( + client, + task_queue=_TASK_QUEUE, + workflows=[StartTwoActivitiesWorkflow], + activities=[print_message_mock], + ): + result = await client.execute_workflow( + StartTwoActivitiesWorkflow.run, + id=str(uuid.uuid4()), + task_queue=_TASK_QUEUE, + ) + assert result is None + assert activity_counter == 2 diff --git a/tests/hello/hello_update_test.py b/tests/hello/hello_update_test.py new file mode 100644 index 00000000..f7664ecb --- /dev/null +++ b/tests/hello/hello_update_test.py @@ -0,0 +1,25 @@ +import uuid + +import pytest +from temporalio.client import Client, WorkflowExecutionStatus +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from hello.hello_update import GreetingWorkflow + + +async def test_update_workflow(client: Client, env: WorkflowEnvironment): + task_queue_name = str(uuid.uuid4()) + async with Worker(client, task_queue=task_queue_name, workflows=[GreetingWorkflow]): + handle = await client.start_workflow( + GreetingWorkflow.run, id=str(uuid.uuid4()), task_queue=task_queue_name + ) + + assert WorkflowExecutionStatus.RUNNING == (await handle.describe()).status + + update_result = await handle.execute_update( + GreetingWorkflow.update_workflow_status + ) + assert "Workflow status updated" == update_result + assert "Hello, World!" == (await handle.result()) + assert WorkflowExecutionStatus.COMPLETED == (await handle.describe()).status diff --git a/tests/message_passing/introduction/test_introduction_sample.py b/tests/message_passing/introduction/test_introduction_sample.py new file mode 100644 index 00000000..de981dc2 --- /dev/null +++ b/tests/message_passing/introduction/test_introduction_sample.py @@ -0,0 +1,123 @@ +import uuid + +import pytest +from temporalio.client import Client, WorkflowUpdateFailedError +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from message_passing.introduction.starter import TASK_QUEUE +from message_passing.introduction.workflows import ( + GetLanguagesInput, + GreetingWorkflow, + Language, + call_greeting_service, +) + + +async def test_queries(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + ): + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ENGLISH + assert await wf_handle.query( + GreetingWorkflow.get_languages, GetLanguagesInput(include_unsupported=False) + ) == [Language.CHINESE, Language.ENGLISH] + assert await wf_handle.query( + GreetingWorkflow.get_languages, GetLanguagesInput(include_unsupported=True) + ) == [ + Language.ARABIC, + Language.CHINESE, + Language.ENGLISH, + Language.FRENCH, + Language.HINDI, + Language.PORTUGUESE, + Language.SPANISH, + ] + + +async def test_set_language(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + ): + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ENGLISH + previous_language = await wf_handle.execute_update( + GreetingWorkflow.set_language, Language.CHINESE + ) + assert previous_language == Language.ENGLISH + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.CHINESE + + +async def test_set_invalid_language(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + ): + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ENGLISH + + with pytest.raises(WorkflowUpdateFailedError): + await wf_handle.execute_update( + GreetingWorkflow.set_language, Language.ARABIC + ) + + +async def test_set_language_that_is_only_available_via_remote_service( + client: Client, env: WorkflowEnvironment +): + """ + Similar to test_set_invalid_language, but this time Arabic is available + since we use the remote service. + """ + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + activities=[call_greeting_service], + ): + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ENGLISH + previous_language = await wf_handle.execute_update( + GreetingWorkflow.set_language_using_activity, + Language.ARABIC, + ) + assert previous_language == Language.ENGLISH + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ARABIC diff --git a/tests/message_passing/lazy_initialization/test_lazy_initialization.py b/tests/message_passing/lazy_initialization/test_lazy_initialization.py new file mode 100644 index 00000000..dcf94582 --- /dev/null +++ b/tests/message_passing/lazy_initialization/test_lazy_initialization.py @@ -0,0 +1,60 @@ +import pytest +from temporalio import common +from temporalio.client import Client, WithStartWorkflowOperation +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from message_passing.update_with_start.lazy_initialization.workflows import ( + ShoppingCartItem, + ShoppingCartWorkflow, + get_price, +) + + +async def test_shopping_cart_workflow(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue="lazy-initialization-test", + workflows=[ShoppingCartWorkflow], + activities=[get_price], + ): + cart_id = "cart--session-1234" + make_start_op = lambda: WithStartWorkflowOperation( + ShoppingCartWorkflow.run, + id=cart_id, + id_conflict_policy=common.WorkflowIDConflictPolicy.USE_EXISTING, + task_queue="lazy-initialization-test", + ) + start_op_1 = make_start_op() + price = await client.execute_update_with_start_workflow( + ShoppingCartWorkflow.add_item, + ShoppingCartItem(sku="item-1", quantity=2), + start_workflow_operation=start_op_1, + ) + + assert price == 1198 + + workflow_handle = await start_op_1.workflow_handle() + + start_op_2 = make_start_op() + price = await client.execute_update_with_start_workflow( + ShoppingCartWorkflow.add_item, + ShoppingCartItem(sku="item-2", quantity=1), + start_workflow_operation=start_op_2, + ) + assert price == 1797 + + workflow_handle = await start_op_2.workflow_handle() + + await workflow_handle.signal(ShoppingCartWorkflow.checkout) + + finalized_order = await workflow_handle.result() + assert finalized_order.items == [ + (ShoppingCartItem(sku="item-1", quantity=2), 1198), + (ShoppingCartItem(sku="item-2", quantity=1), 599), + ] + assert finalized_order.total == 1797 diff --git a/tests/message_passing/safe_message_handlers/workflow_test.py b/tests/message_passing/safe_message_handlers/workflow_test.py new file mode 100644 index 00000000..8fd28d8d --- /dev/null +++ b/tests/message_passing/safe_message_handlers/workflow_test.py @@ -0,0 +1,170 @@ +import asyncio +import uuid +from typing import Callable, Sequence + +import pytest +from temporalio.client import Client, WorkflowUpdateFailedError +from temporalio.exceptions import ApplicationError +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from message_passing.safe_message_handlers.activities import ( + assign_nodes_to_job, + find_bad_nodes, + start_cluster, + unassign_nodes_for_job, +) +from message_passing.safe_message_handlers.workflow import ( + ClusterManagerAssignNodesToJobInput, + ClusterManagerDeleteJobInput, + ClusterManagerInput, + ClusterManagerWorkflow, +) + +ACTIVITIES: Sequence[Callable] = [ + assign_nodes_to_job, + unassign_nodes_for_job, + find_bad_nodes, + start_cluster, +] + + +async def test_safe_message_handlers(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + task_queue = f"tq-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[ClusterManagerWorkflow], + activities=ACTIVITIES, + ): + cluster_manager_handle = await client.start_workflow( + ClusterManagerWorkflow.run, + ClusterManagerInput(), + id=f"ClusterManagerWorkflow-{uuid.uuid4()}", + task_queue=task_queue, + ) + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.wait_until_cluster_started + ) + + allocation_updates = [] + for i in range(6): + allocation_updates.append( + cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=2, job_name=f"task-{i}" + ), + ) + ) + results = await asyncio.gather(*allocation_updates) + for result in results: + assert len(result.nodes_assigned) == 2 + + await asyncio.sleep(1) + + deletion_updates = [] + for i in range(6): + deletion_updates.append( + cluster_manager_handle.execute_update( + ClusterManagerWorkflow.delete_job, + ClusterManagerDeleteJobInput(job_name=f"task-{i}"), + ) + ) + await asyncio.gather(*deletion_updates) + + await cluster_manager_handle.signal(ClusterManagerWorkflow.shutdown_cluster) + + cluster_manager_result = await cluster_manager_handle.result() + assert cluster_manager_result.num_currently_assigned_nodes == 0 + + +async def test_update_idempotency(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + task_queue = f"tq-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[ClusterManagerWorkflow], + activities=ACTIVITIES, + ): + cluster_manager_handle = await client.start_workflow( + ClusterManagerWorkflow.run, + ClusterManagerInput(), + id=f"ClusterManagerWorkflow-{uuid.uuid4()}", + task_queue=task_queue, + ) + + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.wait_until_cluster_started + ) + + result_1 = await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=5, job_name="jobby-job" + ), + ) + # simulate that in calling it twice, the operation is idempotent + result_2 = await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=5, job_name="jobby-job" + ), + ) + # the second call should not assign more nodes (it may return fewer if the health check finds bad nodes + # in between the two signals.) + assert result_1.nodes_assigned >= result_2.nodes_assigned + + +async def test_update_failure(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + task_queue = f"tq-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[ClusterManagerWorkflow], + activities=ACTIVITIES, + ): + cluster_manager_handle = await client.start_workflow( + ClusterManagerWorkflow.run, + ClusterManagerInput(), + id=f"ClusterManagerWorkflow-{uuid.uuid4()}", + task_queue=task_queue, + ) + + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.wait_until_cluster_started + ) + + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=24, job_name="big-task" + ), + ) + try: + # Try to assign too many nodes + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=3, job_name="little-task" + ), + ) + except WorkflowUpdateFailedError as e: + assert isinstance(e.cause, ApplicationError) + assert e.cause.message == "Cannot assign 3 nodes; have only 1 available" + finally: + await cluster_manager_handle.signal(ClusterManagerWorkflow.shutdown_cluster) + result = await cluster_manager_handle.result() + assert result.num_currently_assigned_nodes + result.num_bad_nodes == 24 diff --git a/tests/message_passing/waiting_for_handlers/waiting_for_handlers_test.py b/tests/message_passing/waiting_for_handlers/waiting_for_handlers_test.py new file mode 100644 index 00000000..e6d200e3 --- /dev/null +++ b/tests/message_passing/waiting_for_handlers/waiting_for_handlers_test.py @@ -0,0 +1,80 @@ +from enum import Enum + +import pytest +from temporalio import client, worker +from temporalio.testing import WorkflowEnvironment + +from message_passing.waiting_for_handlers import WorkflowExitType, WorkflowInput +from message_passing.waiting_for_handlers.activities import ( + activity_executed_by_update_handler, +) +from message_passing.waiting_for_handlers.starter import TASK_QUEUE +from message_passing.waiting_for_handlers.workflows import WaitingForHandlersWorkflow + + +class UpdateExpect(Enum): + SUCCESS = "success" + FAILURE = "failure" + + +class WorkflowExpect(Enum): + SUCCESS = "success" + FAILURE = "failure" + + +@pytest.mark.parametrize( + ["exit_type_name", "update_expect", "workflow_expect"], + [ + (WorkflowExitType.SUCCESS.name, UpdateExpect.SUCCESS, WorkflowExpect.SUCCESS), + (WorkflowExitType.FAILURE.name, UpdateExpect.SUCCESS, WorkflowExpect.FAILURE), + ( + WorkflowExitType.CANCELLATION.name, + UpdateExpect.SUCCESS, + WorkflowExpect.FAILURE, + ), + ], +) +async def test_waiting_for_handlers( + env: WorkflowEnvironment, + exit_type_name: str, + update_expect: UpdateExpect, + workflow_expect: WorkflowExpect, +): + [exit_type] = [t for t in WorkflowExitType if t.name == exit_type_name] + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with worker.Worker( + env.client, + task_queue=TASK_QUEUE, + workflows=[WaitingForHandlersWorkflow], + activities=[ + activity_executed_by_update_handler, + ], + ): + wf_handle = await env.client.start_workflow( + WaitingForHandlersWorkflow.run, + WorkflowInput(exit_type=exit_type), + id="waiting-for-handlers-test", + task_queue=TASK_QUEUE, + ) + up_handle = await wf_handle.start_update( + WaitingForHandlersWorkflow.my_update, + wait_for_stage=client.WorkflowUpdateStage.ACCEPTED, + ) + + if exit_type == WorkflowExitType.CANCELLATION: + await wf_handle.cancel() + + if update_expect == UpdateExpect.SUCCESS: + await up_handle.result() + else: + with pytest.raises(client.WorkflowUpdateFailedError): + await up_handle.result() + + if workflow_expect == WorkflowExpect.SUCCESS: + await wf_handle.result() + else: + with pytest.raises(client.WorkflowFailureError): + await wf_handle.result() diff --git a/tests/message_passing/waiting_for_handlers_and_compensation/waiting_for_handlers_and_compensation_test.py b/tests/message_passing/waiting_for_handlers_and_compensation/waiting_for_handlers_and_compensation_test.py new file mode 100644 index 00000000..2b10d396 --- /dev/null +++ b/tests/message_passing/waiting_for_handlers_and_compensation/waiting_for_handlers_and_compensation_test.py @@ -0,0 +1,106 @@ +import uuid +from enum import Enum + +import pytest +from temporalio import client, worker +from temporalio.testing import WorkflowEnvironment + +from message_passing.waiting_for_handlers_and_compensation import ( + WorkflowExitType, + WorkflowInput, +) +from message_passing.waiting_for_handlers_and_compensation.activities import ( + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, +) +from message_passing.waiting_for_handlers_and_compensation.starter import TASK_QUEUE +from message_passing.waiting_for_handlers_and_compensation.workflows import ( + WaitingForHandlersAndCompensationWorkflow, +) + + +class UpdateExpect(Enum): + SUCCESS = "success" + FAILURE = "failure" + + +class WorkflowExpect(Enum): + SUCCESS = "success" + FAILURE = "failure" + + +@pytest.mark.parametrize( + ["exit_type_name", "update_expect", "workflow_expect"], + [ + (WorkflowExitType.SUCCESS.name, UpdateExpect.SUCCESS, WorkflowExpect.SUCCESS), + (WorkflowExitType.FAILURE.name, UpdateExpect.FAILURE, WorkflowExpect.FAILURE), + ( + WorkflowExitType.CANCELLATION.name, + UpdateExpect.FAILURE, + WorkflowExpect.FAILURE, + ), + ], +) +async def test_waiting_for_handlers_and_compensation( + env: WorkflowEnvironment, + exit_type_name: str, + update_expect: UpdateExpect, + workflow_expect: WorkflowExpect, +): + [exit_type] = [t for t in WorkflowExitType if t.name == exit_type_name] + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with worker.Worker( + env.client, + task_queue=TASK_QUEUE, + workflows=[WaitingForHandlersAndCompensationWorkflow], + activities=[ + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, + ], + ): + wf_handle = await env.client.start_workflow( + WaitingForHandlersAndCompensationWorkflow.run, + WorkflowInput(exit_type=exit_type), + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + up_handle = await wf_handle.start_update( + WaitingForHandlersAndCompensationWorkflow.my_update, + wait_for_stage=client.WorkflowUpdateStage.ACCEPTED, + ) + + if exit_type == WorkflowExitType.CANCELLATION: + await wf_handle.cancel() + + if update_expect == UpdateExpect.SUCCESS: + await up_handle.result() + assert not ( + await wf_handle.query( + WaitingForHandlersAndCompensationWorkflow.update_compensation_done + ) + ) + else: + with pytest.raises(client.WorkflowUpdateFailedError): + await up_handle.result() + assert await wf_handle.query( + WaitingForHandlersAndCompensationWorkflow.update_compensation_done + ) + + if workflow_expect == WorkflowExpect.SUCCESS: + await wf_handle.result() + assert not ( + await wf_handle.query( + WaitingForHandlersAndCompensationWorkflow.workflow_compensation_done + ) + ) + else: + with pytest.raises(client.WorkflowFailureError): + await wf_handle.result() + assert await wf_handle.query( + WaitingForHandlersAndCompensationWorkflow.workflow_compensation_done + ) diff --git a/tests/polling/infrequent/__init__.py b/tests/polling/infrequent/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/polling/infrequent/workflow_test.py b/tests/polling/infrequent/workflow_test.py new file mode 100644 index 00000000..31f3f987 --- /dev/null +++ b/tests/polling/infrequent/workflow_test.py @@ -0,0 +1,31 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from polling.infrequent.activities import compose_greeting +from polling.infrequent.workflows import GreetingWorkflow + + +async def test_infrequent_polling_workflow(client: Client, env: WorkflowEnvironment): + if not env.supports_time_skipping: + pytest.skip("Too slow to test with time-skipping disabled") + + # Start a worker that hosts the workflow and activity implementations. + task_queue = f"tq-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[GreetingWorkflow], + activities=[compose_greeting], + ): + handle = await client.start_workflow( + GreetingWorkflow.run, + "Temporal", + id=f"infrequent-polling-{uuid.uuid4()}", + task_queue=task_queue, + ) + result = await handle.result() + assert result == "Hello, Temporal!" diff --git a/tests/pydantic_converter/workflow_test.py b/tests/pydantic_converter/workflow_test.py index a547caf0..c673164e 100644 --- a/tests/pydantic_converter/workflow_test.py +++ b/tests/pydantic_converter/workflow_test.py @@ -3,15 +3,10 @@ from ipaddress import IPv4Address from temporalio.client import Client +from temporalio.contrib.pydantic import pydantic_data_converter from temporalio.worker import Worker -from pydantic_converter.converter import pydantic_data_converter -from pydantic_converter.worker import ( - MyPydanticModel, - MyWorkflow, - my_activity, - new_sandbox_runner, -) +from pydantic_converter.worker import MyPydanticModel, MyWorkflow, my_activity async def test_workflow_with_pydantic_model(client: Client): @@ -35,7 +30,6 @@ async def test_workflow_with_pydantic_model(client: Client): task_queue=task_queue_name, workflows=[MyWorkflow], activities=[my_activity], - workflow_runner=new_sandbox_runner(), ): result = await client.execute_workflow( MyWorkflow.run, diff --git a/tests/pydantic_converter_v1/__init__.py b/tests/pydantic_converter_v1/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/pydantic_converter_v1/workflow_test.py b/tests/pydantic_converter_v1/workflow_test.py new file mode 100644 index 00000000..fd0af800 --- /dev/null +++ b/tests/pydantic_converter_v1/workflow_test.py @@ -0,0 +1,46 @@ +import uuid +from datetime import datetime +from ipaddress import IPv4Address + +from temporalio.client import Client +from temporalio.worker import Worker + +from pydantic_converter_v1.converter import pydantic_data_converter +from pydantic_converter_v1.worker import ( + MyPydanticModel, + MyWorkflow, + my_activity, + new_sandbox_runner, +) + + +async def test_workflow_with_pydantic_model(client: Client): + # Replace data converter in client + new_config = client.config() + new_config["data_converter"] = pydantic_data_converter + client = Client(**new_config) + task_queue_name = str(uuid.uuid4()) + + orig_models = [ + MyPydanticModel( + some_ip=IPv4Address("127.0.0.1"), some_date=datetime(2000, 1, 2, 3, 4, 5) + ), + MyPydanticModel( + some_ip=IPv4Address("127.0.0.2"), some_date=datetime(2001, 2, 3, 4, 5, 6) + ), + ] + + async with Worker( + client, + task_queue=task_queue_name, + workflows=[MyWorkflow], + activities=[my_activity], + workflow_runner=new_sandbox_runner(), + ): + result = await client.execute_workflow( + MyWorkflow.run, + orig_models, + id=str(uuid.uuid4()), + task_queue=task_queue_name, + ) + assert orig_models == result diff --git a/tests/resource_pool/__init__.py b/tests/resource_pool/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/resource_pool/workflow_test.py b/tests/resource_pool/workflow_test.py new file mode 100644 index 00000000..42a6fbde --- /dev/null +++ b/tests/resource_pool/workflow_test.py @@ -0,0 +1,120 @@ +import asyncio +from collections import defaultdict +from typing import Any, Optional, Sequence + +from temporalio import activity +from temporalio.client import Client, WorkflowFailureError, WorkflowHandle +from temporalio.common import WorkflowIDConflictPolicy +from temporalio.worker import Worker + +from resource_pool.pool_client.resource_pool_workflow import ( + ResourcePoolWorkflow, + ResourcePoolWorkflowInput, +) +from resource_pool.resource_user_workflow import ( + ResourceUserWorkflow, + ResourceUserWorkflowInput, + UseResourceActivityInput, +) +from resource_pool.shared import RESOURCE_POOL_WORKFLOW_ID + +TASK_QUEUE = "resource_pool-task-queue" + + +async def test_resource_pool_workflow(client: Client): + # key is resource, value is a description of resource usage + resource_usage: defaultdict[str, list[Sequence[str]]] = defaultdict(list) + + # Mock out the activity to count executions + @activity.defn(name="use_resource") + async def use_resource_mock(input: UseResourceActivityInput) -> None: + workflow_id = activity.info().workflow_id + resource_usage[input.resource].append((workflow_id, "start")) + # We need a small sleep here to bait out races + await asyncio.sleep(0.05) + resource_usage[input.resource].append((workflow_id, "end")) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[ResourcePoolWorkflow, ResourceUserWorkflow], + activities=[use_resource_mock], + ): + await run_all_workflows(client) + + # Did any workflow run in more than one place? + workflow_id_to_resource: dict[str, str] = {} + for resource, events in resource_usage.items(): + for workflow_id, event in events: + if workflow_id in workflow_id_to_resource: + existing_resource = workflow_id_to_resource[workflow_id] + assert ( + existing_resource == resource + ), f"{workflow_id} ran on both {resource} and {existing_resource}" + else: + workflow_id_to_resource[workflow_id] = resource + + # Did any resource have more than one workflow on it at a time? + for resource, events in resource_usage.items(): + holder: Optional[str] = None + for workflow_id, event in events: + if event == "start": + assert ( + holder is None + ), f"{workflow_id} started on {resource} held by {holder}" + holder = workflow_id + else: + assert ( + holder == workflow_id + ), f"{workflow_id} ended on {resource} held by {holder}" + holder = None + + # Are all the resources free, per the query? + handle: WorkflowHandle[ + ResourcePoolWorkflow, None + ] = client.get_workflow_handle_for( + ResourcePoolWorkflow.run, RESOURCE_POOL_WORKFLOW_ID + ) + query_result = await handle.query(ResourcePoolWorkflow.get_current_holders) + assert query_result == {"r_a": None, "r_b": None, "r_c": None} + + +async def run_all_workflows(client: Client): + resource_pool_handle = await client.start_workflow( + workflow=ResourcePoolWorkflow.run, + arg=ResourcePoolWorkflowInput( + resources={"r_a": None, "r_b": None, "r_c": None}, + waiters=[], + ), + id=RESOURCE_POOL_WORKFLOW_ID, + task_queue=TASK_QUEUE, + id_conflict_policy=WorkflowIDConflictPolicy.USE_EXISTING, + ) + + resource_user_handles: list[WorkflowHandle[Any, Any]] = [] + for i in range(0, 8): + input = ResourceUserWorkflowInput( + resource_pool_workflow_id=RESOURCE_POOL_WORKFLOW_ID, + iteration_to_fail_after=None, + should_continue_as_new=False, + ) + if i == 0: + input.should_continue_as_new = True + if i == 1: + input.iteration_to_fail_after = "first" + + handle = await client.start_workflow( + workflow=ResourceUserWorkflow.run, + arg=input, + id=f"resource-user-workflow-{i}", + task_queue=TASK_QUEUE, + ) + resource_user_handles.append(handle) + + for handle in resource_user_handles: + try: + await handle.result() + except WorkflowFailureError: + pass + + await resource_pool_handle.terminate() diff --git a/tests/sleep_for_days/__init__.py b/tests/sleep_for_days/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/sleep_for_days/workflow_test.py b/tests/sleep_for_days/workflow_test.py new file mode 100644 index 00000000..fa3f0686 --- /dev/null +++ b/tests/sleep_for_days/workflow_test.py @@ -0,0 +1,53 @@ +import uuid +from datetime import timedelta + +from temporalio import activity +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from sleep_for_days.starter import TASK_QUEUE +from sleep_for_days.workflows import SendEmailInput, SleepForDaysWorkflow + + +async def test_sleep_for_days_workflow(): + num_activity_executions = 0 + + # Mock out the activity to count executions + @activity.defn(name="send_email") + async def send_email_mock(input: SendEmailInput) -> str: + nonlocal num_activity_executions + num_activity_executions += 1 + return input.email_msg + + async with await WorkflowEnvironment.start_time_skipping() as env: + # if env.supports_time_skipping: + # pytest.skip( + # "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + # ) + async with Worker( + env.client, + task_queue=TASK_QUEUE, + workflows=[SleepForDaysWorkflow], + activities=[send_email_mock], + ): + handle = await env.client.start_workflow( + SleepForDaysWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + + start_time = await env.get_current_time() + # Time-skip 5 minutes. + await env.sleep(timedelta(minutes=5)) + # Check that the activity has been called, we're now waiting for the sleep to finish. + assert num_activity_executions == 1 + # Time-skip 3 days. + await env.sleep(timedelta(days=90)) + # Expect 3 more activity calls. + assert num_activity_executions == 4 + # Send the signal to complete the workflow. + await handle.signal(SleepForDaysWorkflow.complete) + # Expect no more activity calls to have been made - workflow is complete. + assert num_activity_executions == 4 + # Expect more than 90 days to have passed. + assert (await env.get_current_time() - start_time) > timedelta(days=90) diff --git a/tests/trio_async/__init__.py b/tests/trio_async/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/trio_async/workflow_test.py b/tests/trio_async/workflow_test.py new file mode 100644 index 00000000..e6981bee --- /dev/null +++ b/tests/trio_async/workflow_test.py @@ -0,0 +1,40 @@ +import uuid + +import trio_asyncio +from temporalio.client import Client +from temporalio.worker import Worker + +from trio_async import activities, workflows + + +async def test_workflow_with_trio(client: Client): + @trio_asyncio.aio_as_trio + async def inside_trio(client: Client) -> list[str]: + # Create Trio thread executor + with trio_asyncio.TrioExecutor(max_workers=200) as thread_executor: + task_queue = f"tq-{uuid.uuid4()}" + # Run worker + async with Worker( + client, + task_queue=task_queue, + activities=[ + activities.say_hello_activity_async, + activities.say_hello_activity_sync, + ], + workflows=[workflows.SayHelloWorkflow], + activity_executor=thread_executor, + workflow_task_executor=thread_executor, + ): + # Run workflow and return result + return await client.execute_workflow( + workflows.SayHelloWorkflow.run, + "some-user", + id=f"wf-{uuid.uuid4()}", + task_queue=task_queue, + ) + + result = trio_asyncio.run(inside_trio, client) + assert result == [ + "Hello, some-user! (from asyncio)", + "Hello, some-user! (from thread)", + ] diff --git a/tests/updatable_timer/__init__.py b/tests/updatable_timer/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/updatable_timer/updatable_timer_test.py b/tests/updatable_timer/updatable_timer_test.py new file mode 100644 index 00000000..f1e38245 --- /dev/null +++ b/tests/updatable_timer/updatable_timer_test.py @@ -0,0 +1,33 @@ +import datetime +import logging +import math +import uuid + +from temporalio.client import Client, WorkflowExecutionStatus +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from updatable_timer.workflow import Workflow + + +async def test_updatable_timer_workflow(client: Client): + logging.basicConfig(level=logging.DEBUG) + + task_queue_name = str(uuid.uuid4()) + async with await WorkflowEnvironment.start_time_skipping() as env: + async with Worker(env.client, task_queue=task_queue_name, workflows=[Workflow]): + in_a_day = float( + (datetime.datetime.now() + datetime.timedelta(days=1)).timestamp() + ) + in_an_hour = float( + (datetime.datetime.now() + datetime.timedelta(hours=1)).timestamp() + ) + handle = await env.client.start_workflow( + Workflow.run, in_a_day, id=str(uuid.uuid4()), task_queue=task_queue_name + ) + wake_up_time1 = await handle.query(Workflow.get_wake_up_time) + assert math.isclose(wake_up_time1, in_a_day) + await handle.signal(Workflow.update_wake_up_time, in_an_hour) + wake_up_time2 = await handle.query(Workflow.get_wake_up_time) + assert math.isclose(wake_up_time2, in_an_hour) + await handle.result() diff --git a/trio_async/README.md b/trio_async/README.md new file mode 100644 index 00000000..01891215 --- /dev/null +++ b/trio_async/README.md @@ -0,0 +1,23 @@ +# Trio Async Sample + +This sample shows how to use Temporal asyncio with [Trio](https://trio.readthedocs.io) using +[Trio asyncio](https://trio-asyncio.readthedocs.io). Specifically it demonstrates using a traditional Temporal client +and worker in a Trio setting, and how Trio-based code can run in both asyncio async activities and threaded sync +activities. + +For this sample, the optional `trio_async` dependency group must be included. To include, run: + + uv sync --group trio_async + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +worker: + + uv run worker.py + +This will start the worker. Then, in another terminal, run the following to execute the workflow: + + uv run starter.py + +The starter should complete with: + + INFO:root:Workflow result: ['Hello, Temporal! (from asyncio)', 'Hello, Temporal! (from thread)'] \ No newline at end of file diff --git a/trio_async/__init__.py b/trio_async/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trio_async/activities.py b/trio_async/activities.py new file mode 100644 index 00000000..c253c93e --- /dev/null +++ b/trio_async/activities.py @@ -0,0 +1,51 @@ +import asyncio +import time + +import trio +import trio_asyncio +from temporalio import activity + + +# An asyncio-based async activity +@activity.defn +async def say_hello_activity_async(name: str) -> str: + # Demonstrate a sleep in both asyncio and Trio, showing that both asyncio + # and Trio primitives can be used + + # First asyncio + activity.logger.info("Sleeping in asyncio") + await asyncio.sleep(0.1) + + # Now Trio. We have to invoke the function separately decorated. + # We cannot use the @trio_as_aio decorator on the activity itself because + # it doesn't use functools wrap or similar so it doesn't respond to things + # like __name__ that @activity.defn needs. + return await say_hello_in_trio_from_asyncio(name) + + +@trio_asyncio.trio_as_aio +async def say_hello_in_trio_from_asyncio(name: str) -> str: + activity.logger.info("Sleeping in Trio (from asyncio)") + await trio.sleep(0.1) + return f"Hello, {name}! (from asyncio)" + + +# A thread-based sync activity +@activity.defn +def say_hello_activity_sync(name: str) -> str: + # Demonstrate a sleep in both threaded and Trio, showing that both + # primitives can be used + + # First, thread-blocking + activity.logger.info("Sleeping normally") + time.sleep(0.1) + + # Now Trio. We have to use Trio's thread sync tools to run trio calls from + # a different thread. + return trio.from_thread.run(say_hello_in_trio_from_sync, name) + + +async def say_hello_in_trio_from_sync(name: str) -> str: + activity.logger.info("Sleeping in Trio (from thread)") + await trio.sleep(0.1) + return f"Hello, {name}! (from thread)" diff --git a/trio_async/starter.py b/trio_async/starter.py new file mode 100644 index 00000000..67f7568b --- /dev/null +++ b/trio_async/starter.py @@ -0,0 +1,28 @@ +import logging + +import trio_asyncio +from temporalio.client import Client + +from trio_async import workflows + + +@trio_asyncio.aio_as_trio # Note this decorator which allows asyncio primitives +async def main(): + logging.basicConfig(level=logging.INFO) + + # Connect client + client = await Client.connect("localhost:7233") + + # Execute the workflow + result = await client.execute_workflow( + workflows.SayHelloWorkflow.run, + "Temporal", + id=f"trio-async-workflow-id", + task_queue="trio-async-task-queue", + ) + logging.info(f"Workflow result: {result}") + + +if __name__ == "__main__": + # Note how we're using Trio event loop, not asyncio + trio_asyncio.run(main) diff --git a/trio_async/worker.py b/trio_async/worker.py new file mode 100644 index 00000000..29f059b4 --- /dev/null +++ b/trio_async/worker.py @@ -0,0 +1,66 @@ +import asyncio +import logging +import os +import sys + +import trio_asyncio +from temporalio.client import Client +from temporalio.worker import Worker + +from trio_async import activities, workflows + + +@trio_asyncio.aio_as_trio # Note this decorator which allows asyncio primitives +async def main(): + logging.basicConfig(level=logging.INFO) + + # Connect client + client = await Client.connect("localhost:7233") + + # Temporal runs threaded activities and workflow tasks via run_in_executor. + # Due to how trio_asyncio works, you can only do run_in_executor with their + # specific executor. We make sure to give it 200 max since we are using it + # for both activities and workflow tasks and by default the worker supports + # 100 max concurrent activity tasks and 100 max concurrent workflow tasks. + with trio_asyncio.TrioExecutor(max_workers=200) as thread_executor: + + # Run a worker for the workflow + async with Worker( + client, + task_queue="trio-async-task-queue", + activities=[ + activities.say_hello_activity_async, + activities.say_hello_activity_sync, + ], + workflows=[workflows.SayHelloWorkflow], + activity_executor=thread_executor, + workflow_task_executor=thread_executor, + ): + # Wait until interrupted + logging.info("Worker started, ctrl+c to exit") + try: + await asyncio.Future() + except asyncio.CancelledError: + # Ignore, happens on ctrl+C + pass + finally: + logging.info("Shutting down") + + +if __name__ == "__main__": + # Note how we're using Trio event loop, not asyncio + try: + trio_asyncio.run(main) + except KeyboardInterrupt: + # Ignore ctrl+c + pass + except BaseException as err: + # On Python 3.11+ Trio represents keyboard interrupt inside an exception + # group + is_interrupt = ( + sys.version_info >= (3, 11) + and isinstance(err, BaseExceptionGroup) + and err.subgroup(KeyboardInterrupt) + ) + if not is_interrupt: + raise diff --git a/trio_async/workflows.py b/trio_async/workflows.py new file mode 100644 index 00000000..adcb4761 --- /dev/null +++ b/trio_async/workflows.py @@ -0,0 +1,30 @@ +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from trio_async.activities import say_hello_activity_async, say_hello_activity_sync + + +@workflow.defn +class SayHelloWorkflow: + @workflow.run + async def run(self, name: str) -> list[str]: + # Workflows don't use default asyncio event loop or Trio, they use a + # custom event loop. Therefore Trio primitives should never be used in a + # workflow, only asyncio helpers (which delegate to the custom loop). + return [ + # That these are two different activities for async or sync means + # nothing to the workflow, we just have both to demonstrate the + # activity side + await workflow.execute_activity( + say_hello_activity_async, + name, + start_to_close_timeout=timedelta(minutes=5), + ), + await workflow.execute_activity( + say_hello_activity_sync, + name, + start_to_close_timeout=timedelta(minutes=5), + ), + ] diff --git a/updatable_timer/README.md b/updatable_timer/README.md new file mode 100644 index 00000000..04960265 --- /dev/null +++ b/updatable_timer/README.md @@ -0,0 +1,42 @@ +# Updatable Timer Sample + +Demonstrates a helper class which relies on `workflow.wait_condition` to implement a blocking sleep that can be updated at any moment. + +The sample is composed of the three executables: + +* `worker.py` hosts the Workflow Executions. +* `starter.py` starts Workflow Executions. +* `wake_up_timer_updater.py` Signals the Workflow Execution with the new time to wake up. + +First start the Worker: + +```bash +uv run worker.py +``` +Check the output of the Worker window. The expected output is: + +``` +Worker started, ctrl+c to exit +``` + +Then in a different terminal window start the Workflow Execution: + +```bash +uv run starter.py +``` +Check the output of the Worker window. The expected output is: +``` +Workflow started: run_id=... +``` + +Then run the updater as many times as you want to change timer to 10 seconds from now: + +```bash +uv run wake_up_time_updater.py +``` + +Check the output of the worker window. The expected output is: + +``` +Updated wake up time to 10 seconds from now +``` \ No newline at end of file diff --git a/updatable_timer/__init__.py b/updatable_timer/__init__.py new file mode 100644 index 00000000..a5ee5055 --- /dev/null +++ b/updatable_timer/__init__.py @@ -0,0 +1 @@ +TASK_QUEUE = "updatable-timer" diff --git a/updatable_timer/starter.py b/updatable_timer/starter.py new file mode 100644 index 00000000..88b4d0d4 --- /dev/null +++ b/updatable_timer/starter.py @@ -0,0 +1,32 @@ +import asyncio +import logging +from datetime import datetime, timedelta +from typing import Optional + +from temporalio import exceptions +from temporalio.client import Client + +from updatable_timer import TASK_QUEUE +from updatable_timer.workflow import Workflow + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + client = client or await Client.connect("localhost:7233") + try: + handle = await client.start_workflow( + Workflow.run, + (datetime.now() + timedelta(days=1)).timestamp(), + id=f"updatable-timer-workflow", + task_queue=TASK_QUEUE, + ) + logging.info(f"Workflow started: run_id={handle.result_run_id}") + except exceptions.WorkflowAlreadyStartedError as e: + logging.info( + f"Workflow already running: workflow_id={e.workflow_id}, run_id={e.run_id}" + ) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/updatable_timer/updatable_timer_lib.py b/updatable_timer/updatable_timer_lib.py new file mode 100644 index 00000000..b90ae868 --- /dev/null +++ b/updatable_timer/updatable_timer_lib.py @@ -0,0 +1,39 @@ +import asyncio +from datetime import datetime, timedelta + +from temporalio import workflow + + +class UpdatableTimer: + def __init__(self, wake_up_time: datetime) -> None: + self.wake_up_time = wake_up_time + self.wake_up_time_updated = False + + async def sleep(self) -> None: + workflow.logger.info(f"sleep_until: {self.wake_up_time}") + while True: + now = workflow.now() + + sleep_interval = self.wake_up_time - now + if sleep_interval <= timedelta(0): + break + workflow.logger.info(f"Going to sleep for {sleep_interval}") + + try: + self.wake_up_time_updated = False + await workflow.wait_condition( + lambda: self.wake_up_time_updated, + timeout=sleep_interval, + ) + except asyncio.TimeoutError: + # checks condition at the beginning of the loop + continue + workflow.logger.info(f"sleep_until completed") + + def update_wake_up_time(self, wake_up_time: datetime) -> None: + workflow.logger.info(f"update_wake_up_time: {wake_up_time}") + self.wake_up_time = wake_up_time + self.wake_up_time_updated = True + + def get_wake_up_time(self) -> datetime: + return self.wake_up_time diff --git a/updatable_timer/wake_up_time_updater.py b/updatable_timer/wake_up_time_updater.py new file mode 100644 index 00000000..f406c186 --- /dev/null +++ b/updatable_timer/wake_up_time_updater.py @@ -0,0 +1,26 @@ +import asyncio +import logging +from datetime import datetime, timedelta +from typing import Optional + +from temporalio.client import Client + +from updatable_timer.workflow import Workflow + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + client = client or await Client.connect("localhost:7233") + handle = client.get_workflow_handle(workflow_id="updatable-timer-workflow") + # signal workflow about the wake up time change + await handle.signal( + Workflow.update_wake_up_time, + (datetime.now() + timedelta(seconds=10)).timestamp(), + ) + + logging.info("Updated wake up time to 10 seconds from now") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/updatable_timer/worker.py b/updatable_timer/worker.py new file mode 100644 index 00000000..096fa1ff --- /dev/null +++ b/updatable_timer/worker.py @@ -0,0 +1,35 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from updatable_timer import TASK_QUEUE +from updatable_timer.workflow import Workflow + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + client = await Client.connect("localhost:7233") + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[Workflow], + ): + logging.info("Worker started, ctrl+c to exit") + # Wait until interrupted + await interrupt_event.wait() + logging.info("Interrupt received, shutting down...") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/updatable_timer/workflow.py b/updatable_timer/workflow.py new file mode 100644 index 00000000..749df6fc --- /dev/null +++ b/updatable_timer/workflow.py @@ -0,0 +1,32 @@ +from datetime import datetime, timezone +from typing import Optional + +from temporalio import workflow + +from updatable_timer.updatable_timer_lib import UpdatableTimer + + +@workflow.defn +class Workflow: + @workflow.init + def __init__(self, wake_up_time: float) -> None: + self.timer = UpdatableTimer( + datetime.fromtimestamp(wake_up_time, tz=timezone.utc) + ) + + @workflow.run + async def run(self, wake_up_time: float): + await self.timer.sleep() + + @workflow.signal + async def update_wake_up_time(self, wake_up_time: float) -> None: + workflow.logger.info(f"update_wake_up_time: {wake_up_time}") + + self.timer.update_wake_up_time( + datetime.fromtimestamp(wake_up_time, tz=timezone.utc) + ) + + @workflow.query + def get_wake_up_time(self) -> float: + workflow.logger.info(f"get_wake_up_time") + return float(self.timer.get_wake_up_time().timestamp()) diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..d0a1a41d --- /dev/null +++ b/uv.lock @@ -0,0 +1,3124 @@ +version = 1 +revision = 2 +requires-python = ">=3.9, <4" +resolution-markers = [ + "python_full_version >= '3.12.4'", + "python_full_version >= '3.12' and python_full_version < 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upload-time = "2024-11-28T08:50:35.681Z" }, +] diff --git a/worker_specific_task_queues/README.md b/worker_specific_task_queues/README.md index 8e8e577a..01f72890 100644 --- a/worker_specific_task_queues/README.md +++ b/worker_specific_task_queues/README.md @@ -24,16 +24,16 @@ Activities have been artificially slowed with `time.sleep(3)` to simulate doing To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker: - poetry run python worker.py + uv run worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run starter.py #### Example output: ```bash -(temporalio-samples-py3.10) user@machine:~/samples-python/activities_sticky_queues$ poetry run python starter.py +(temporalio-samples-py3.10) user@machine:~/samples-python/activities_sticky_queues$ uv run starter.py Output checksums: 49d7419e6cba3575b3158f62d053f922aa08b23c64f05411cda3213b56c84ba4 49d7419e6cba3575b3158f62d053f922aa08b23c64f05411cda3213b56c84ba4 diff --git a/worker_versioning/README.md b/worker_versioning/README.md index 04f958bf..21af863f 100644 --- a/worker_versioning/README.md +++ b/worker_versioning/README.md @@ -5,7 +5,7 @@ feature to deploy incompatible changes to workflow code more easily. To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory: - poetry run python example.py + uv run example.py This will add some Build IDs to a Task Queue, and will also run Workers with those versions to show how you can mark add versions, mark them as compatible (or not) with one another, and run Workers at specific versions. You'll