Spice.ai OSS

What is Spice?

Spice is a small, portable runtime that provides developers with a unified SQL query interface to locally materialize, accelerate, and query datasets sourced from any database, data warehouse, or data lake.

📣 Read the Spice.ai OSS announcement blog post.

Spice makes it easy to build data-driven and data-intensive applications by streamlining the use of data and machine learning (ML) in software.

The Spice runtime is written in Rust and leverages industry leading technologies like Apache DataFusion, Apache Arrow, Apache Arrow Flight, and DuckDB.

Why Spice?

Spice makes querying data by SQL across one or more data sources simple and fast. Easily co-locate a managed working set of data with your application or ML, locally accelerated in-memory with Arrow, with SQLite/DuckDB, or with an attached database like PostgreSQL for high-performance, low-latency queries. Accelerated engines run in your infrastructure giving you flexibility and control over price and performance.

How is Spice different?

1. Local Acceleration with both OLAP (Arrow/DuckDB) and OLTP (SQLite/PostgreSQL) databases at dataset granularity compared to other OLAP only or OLTP only systems.

2. Separation of materialization and storage/compute compared with monolith data systems and data lakes. Keep compute colocated with source data while bringing a materialized working set next to your application, dashboard, or data/ML pipeline.

3. Edge to cloud native. Chainable and designed to be deployed standalone, as a container sidecar, as a microservice, in a cluster across laptops, the Edge, On-Prem, to a POP, and to all public clouds.

Before Spice

With Spice

Example Use-Cases

1. Faster applications and frontends. Accelerate and co-locate datasets with applications and frontends, to serve more concurrent queries and users with faster page loads and data updates. Try the CQRS sample app

2. Faster dashboards, analytics, and BI. Faster, more responsive dashboards without massive compute costs. Watch the Apache Superset demo

3. Faster data pipelines, machine learning training and inferencing. Co-locate datasets in pipelines where the data is needed to minimize data-movement and improve query performance. Predict hard drive failure with the SMART data demo

4. Easily query many data sources. Federated SQL query across databases, data warehouses, and data lakes using Data Connectors.

Supported Data Connectors

Currently supported data connectors for upstream datasets. More coming soon.

Name	Description	Status	Protocol/Format	Refresh Modes
databricks	Databricks	Alpha	Spark Connect S3/Delta Lake	full
postgres	PostgreSQL	Alpha		full
spiceai	Spice.ai	Alpha	Arrow Flight	append, full
s3	S3	Alpha	Parquet	full
dremio	Dremio	Alpha	Arrow Flight	full
mysql	MySQL	Alpha		full
duckdb	DuckDB	Alpha		full
snowflake	Snowflake	Coming soon!	Arrow Flight SQL	full
bigquery	BigQuery	Coming soon!	Arrow Flight SQL	full

Supported Data Stores/Accelerators

Currently supported data stores for local materialization/acceleration. More coming soon.

Name	Description	Status	Engine Modes	Refresh Modes
arrow	In-Memory Arrow Records	Alpha	memory	append, full
duckdb	Embedded DuckDB	Alpha	memory, file	append, full
sqlite	Embedded SQLite	Alpha	memory, file	append, full
postgres	Attached PostgreSQL	Alpha		append, full

Intelligent Applications

Spice enables developers to build both data and AI-driven applications by co-locating data and ML models with applications. Read more about the vision to enable the development of intelligent AI-driven applications.

⚠️ DEVELOPER PREVIEW Spice is under active alpha stage development and is not intended to be used in production until its 1.0-stable release. If you are interested in running Spice in production, please get in touch so we can support you (See Connect with us below).

⚡️ Quickstart (Local Machine)

quickstart.mp4

Step 1. Install the Spice CLI:

On macOS, Linux, and WSL:

curl https://install.spiceai.org | /bin/bash

Or using brew:

brew install spiceai/spiceai/spice

On Windows:

curl -L "https://install.spiceai.org/Install.ps1" -o Install.ps1 && PowerShell -ExecutionPolicy Bypass -File ./Install.ps1

Step 2. Initialize a new Spice app with the spice init command:

spice init spice_qs

A spicepod.yaml file is created in the spice_qs directory. Change to that directory:

cd spice_qs

Step 3. Start the Spice runtime:

spice run

Example output will be shown as follows:

Spice.ai runtime starting...
Using latest 'local' runtime version.
2024-02-21T06:11:56.381793Z  INFO runtime::http: Spice Runtime HTTP listening on 127.0.0.1:3000
2024-02-21T06:11:56.381853Z  INFO runtime::flight: Spice Runtime Flight listening on 127.0.0.1:50051
2024-02-21T06:11:56.382038Z  INFO runtime::opentelemetry: Spice Runtime OpenTelemetry listening on 127.0.0.1:50052

The runtime is now started and ready for queries.

Step 4. In a new terminal window, add the spiceai/quickstart Spicepod. A Spicepod is a package of configuration defining datasets and ML models.

spice add spiceai/quickstart

The spicepod.yaml file will be updated with the spiceai/quickstart dependency.

version: v1beta1
kind: Spicepod
name: PROJECT_NAME
dependencies:
  - spiceai/quickstart

The spiceai/quickstart Spicepod will add a taxi_trips data table to the runtime which is now available to query by SQL.

2024-02-22T05:53:48.222952Z  INFO runtime: Loaded dataset: taxi_trips
2024-02-22T05:53:48.223101Z  INFO runtime::dataconnector: Refreshing data for taxi_trips

Step 5. Start the Spice SQL REPL:

spice sql

The SQL REPL inferface will be shown:

Welcome to the interactive Spice.ai SQL Query Utility! Type 'help' for help.

show tables; -- list available tables
sql>

Enter show tables; to display the available tables for query:

sql> show tables
+------------+
| table_name |
+------------+
| taxi_trips |
+------------+

Query took: 0.007505084 seconds. 1/1 rows displayed.

Enter a query to display the longest taxi trips:

sql> SELECT trip_distance, total_amount FROM taxi_trips ORDER BY trip_distance DESC LIMIT 10;

Output:

+---------------+--------------+
| trip_distance | total_amount |
+---------------+--------------+
| 312722.3      | 22.15        |
| 97793.92      | 36.31        |
| 82015.45      | 21.56        |
| 72975.97      | 20.04        |
| 71752.26      | 49.57        |
| 59282.45      | 33.52        |
| 59076.43      | 23.17        |
| 58298.51      | 18.63        |
| 51619.36      | 24.2         |
| 44018.64      | 52.43        |
+---------------+--------------+

Query took: 0.002458976 seconds

⚙️ Runtime Container Deployment

Using the Docker image locally:

docker pull spiceai/spiceai

In a Dockerfile:

from spiceai/spiceai:latest

Using Helm:

helm repo add spiceai https://helm.spiceai.org
helm install spiceai spiceai/spiceai

🏎️ Next Steps

You can use any number of predefined datasets available from the Spice.ai Cloud Platform in the Spice runtime.

A list of publicly available datasets from Spice.ai can be found here: https://docs.spice.ai/building-blocks/datasets.

In order to access public datasets from Spice.ai, you will first need to create an account with Spice.ai by selecting the free tier membership.

Navigate to spice.ai and create a new account by clicking on Try for Free.

After creating an account, you will need to create an app in order to create to an API key.

You will now be able to access datasets from Spice.ai. For this demonstration, we will be using the spice.ai/eth.recent_blocks dataset.

Step 1. Log in and authenticate from the command line using the spice login command. A pop up browser window will prompt you to authenticate:

spice login

Step 2. Initialize a new project and start the runtime:

# Initialize a new Spice app
spice init spice_app

# Change to app directory
cd spice_app

# Start the runtime
spice run

Step 3. Configure the dataset:

In a new terminal window, configure a new dataset using the spice dataset configure command:

spice dataset configure

You will be prompted to enter a name. Enter a name that represents the contents of the dataset

dataset name: (spice_app) eth_recent_blocks

Enter the description of the dataset:

description: eth recent logs

Enter the location of the dataset:

from: spice.ai/eth.recent_blocks

Select y when prompted whether to accelerate the data:

Locally accelerate (y/n)? y

You should see the following output from your runtime terminal:

2024-02-21T22:49:10.038461Z  INFO runtime: Loaded dataset: eth_recent_blocks

Step 4. In a new terminal window, use the Spice SQL REPL to query the dataset

spice sql

SELECT number, size, gas_used from eth_recent_blocks LIMIT 10;

The output displays the results of the query along with the query execution time:

+----------+--------+----------+
| number   | size   | gas_used |
+----------+--------+----------+
| 19281345 | 400378 | 16150051 |
| 19281344 | 200501 | 16480224 |
| 19281343 | 97758  | 12605531 |
| 19281342 | 89629  | 12035385 |
| 19281341 | 133649 | 13335719 |
| 19281340 | 307584 | 18389159 |
| 19281339 | 89233  | 13391332 |
| 19281338 | 75250  | 12806684 |
| 19281337 | 100721 | 11823522 |
| 19281336 | 150137 | 13418403 |
+----------+--------+----------+

Query took: 0.004057791 seconds

You can experiment with the time it takes to generate queries when using non-accelerated datasets. You can change the acceleration setting from true to false in the datasets.yaml file.

📄 Documentation

Comprehensive documentation is available at docs.spiceai.org.

🔨 Upcoming Features

🚀 See the Roadmap to v1.0-stable for upcoming features.

🤝 Connect with us

We greatly appreciate and value your support! You can help Spice in a number of ways:

• Build an app with Spice.ai and send us feedback and suggestions at [email protected] or on Discord, X, or LinkedIn.
• File an issue if you see something not quite working correctly.
• Join our team (We’re hiring!)
• Contribute code or documentation to the project (see CONTRIBUTING.md).
• Follow our blog at blog.spiceai.org

⭐️ star this repo! Thank you for your support! 🙏