Codestin Search App

HaoXuAI · 2025-04-04T23:38:05Z

What this PR does / why we need it:

Introduce the ComputeEngine component that runs current materialization and get_historical_features on a compute engine such as Spark, Flink, etc.
This will hand over heavy computing work from online/offline stores to the engines. And let the online/offline deal with IO only.
The compute engine builds the execution plan in a DAG format named FeatureBuilder. It derives feature generation from Feature View definitiions including:

Transformation (via Transformation API)
Aggregation (via Aggregation API)
Join (join with an entity, adding Customized JOIN and join with another Feature View to do in the future)
Filter (Point in time filter, ttl filter, filter by custom expression)
...

User facing API, e.g:

def transform_feature(df: DataFrame) -> DataFrame:
    df = df.withColumn("sum_conv_rate", df["sum_conv_rate"] * 2)
    df = df.withColumn("avg_acc_rate", df["avg_acc_rate"] * 2)
    return df

driver_stats_fv = BatchFeatureView(
    name="driver_hourly_stats",
    entities=[driver],
    mode="python",
    batch_engine=Field(...), # Not supported yet.
    aggregations=[
        Aggregation(column="conv_rate", function="sum"),
        Aggregation(column="acc_rate", function="avg"),
    ],
    join=[...], # Not supported yet
    udf=transform_feature,
    udf_string="transform_feature",
    ttl=timedelta(days=3), # Not supported for materialization yet
    filter="filter_expr", # Not fully support
    schema=[
        Field(name="conv_rate", dtype=Float32),
        Field(name="acc_rate", dtype=Float32),
        Field(name="avg_daily_trips", dtype=Int64),
        Field(name="driver_id", dtype=Int32),
    ],
    online=True,
    offline=False, # not supported yet
    source=data_source,
)

Feature build flow:

join with entity first if necessary
filter data based on entity event timestamp, ttl, and filter
feature aggregation based on provided aggregations
run transformation based on provided udf or feature_transformations
write data to online or offline

Current use case:

# Create retrieval task
task = MaterializationTask(
    project=spark_environment.project,
    feature_view=driver_stats_fv,
    start_time=now - timedelta(days=1),
    end_time=now,
    tqdm_builder=tqdm_builder,
)

# Create SparkComputeEngine
engine = SparkComputeEngine(
    repo_config=spark_environment.config,
    offline_store=SparkOfflineStore(),
    online_store=MagicMock(),
    registry=registry,
)

spark_materialize_job = engine.materialize(task)

TODOs:

Update Batch/Stream feature view to support batch_engine and stream_engine configurations.
Support offline write
Change materialization read node to the engine, and merge the flow with get_historical_features
Support filter by filter_expr in feature view
Use engine APIs in feature_store API.
Support compute engine in repo yaml config:
Join with Feature View or Data Source.
Update get_online_feature to be able to handle aggregated and transformed features
Research the Transformation, Aggregation, and Filter to merge into single API.

Which issue(s) this PR fixes:

Misc

Signed-off-by: HaoXuAI <[email protected]>

franciscojavierarceo

Nice! Left some small nits on naming.

franciscojavierarceo · 2025-04-04T23:44:05Z

sdk/python/feast/infra/compute_engines/dag/builder.py

+        raise NotImplementedError
+
+    @abstractmethod
+    def build_aggregation_node(self, input_node):


I think we should remove the 'node' suffix from all of there, as I think it's implicit.

yeah sounds good

On second thought, I think it's better to keep the 'node' suffix, as it makes the output more descriptive and clearly indicates that it's a node, which can then be chained in the build method

franciscojavierarceo · 2025-04-04T23:44:42Z

sdk/python/feast/infra/compute_engines/dag/builder.py

+    def build_validation_node(self, input_node):
+        raise
+
+    def build(self) -> ExecutionPlan:


not: build_dag(self)

I would suggest to keep build(), more consistent with the builder.build() pattern.

franciscojavierarceo · 2025-04-04T23:45:07Z

sdk/python/feast/infra/compute_engines/dag/model.py

+class DAGFormat(str, Enum):
+    SPARK = "spark"
+    PANDAS = "pandas"
+    ARROW = "arrow"


Native python?

Right, it's only a placeholder for now. The native python will be interesting as it works with on demand feature view. Will do it in the next PR

franciscojavierarceo · 2025-04-04T23:46:25Z

sdk/python/feast/infra/compute_engines/spark/compute.py

+        spark_df = result.data  # should be a Spark DataFrame
+
+        # ✅ 4. Return as Arrow
+        return spark_df.toPandas().to_arrow()


Serializing as pandas here could be expensive no?

Right, only to keep it consistent with the current get_historical_features API. But definitely needs some rework

franciscojavierarceo · 2025-04-04T23:47:26Z

sdk/python/feast/infra/compute_engines/spark/spark_dag_builder.py

+
+
+class SparkDAGBuilder(DAGBuilder):
+    def build_source_node(self):


Yeah just like above, don't think we need node suffix for these methods

sdk/python/feast/infra/compute_engines/spark/spark_dag_builder.py

Signed-off-by: HaoXuAI <[email protected]>

franciscojavierarceo · 2025-04-07T15:41:06Z

@HaoXuAI is this ready to review? Or still draft?

HaoXuAI · 2025-04-07T15:59:12Z

Not yet. Still needs some work but should be ready soon

Signed-off-by: HaoXuAI <[email protected]>

docs/getting-started/architecture/feature-transformation.md

docs/reference/compute-engine/README.md

franciscojavierarceo · 2025-04-15T17:33:29Z

sdk/python/feast/batch_feature_view.py

    udf_string: Optional[str]
    feature_transformation: Transformation
+    batch_engine: Optional[Field]
+    aggregations: Optional[List[Aggregation]]


If I'm to be honest, I don't love putting the transformation in the FeatureView.

I think it'd be more intuitive to put Aggregation under Transformation and make the FeatureViews purely represent schemas available for online or offline.

I agree. This is only to be consistent with the current StreamFeatureView and Aggregation class. It makes sense to have Aggregation one type of Transformation (as well as for Filter), but that will need some work to refactor.
For Tecton, they put Aggregate in feature (what Feast called schema) as

features=[ Aggregate( input_column=Field("transaction", Int64), function="count", time_window=TimeWindow(window_size=timedelta(days=1)), ) ],

In some way that also make sense but for Feast it will needs much more work to refactor the schema API.

For Chrono they put it into a GroupBy API, which is similar to our FeatureView:

v1 = GroupBy( sources=[source], keys=["user_id"], # We are aggregating by user online=True, aggregations=[Aggregation( input_column="refund_amt", operation=Operation.SUM, windows=window_sizes ), # The sum of purchases prices in various windows ], )

I can look into how to merge Aggregation with Transformation together in the next PR, and added it to a TODO for now.

franciscojavierarceo · 2025-04-15T17:34:59Z

sdk/python/feast/infra/compute_engines/feature_builder.py

+from feast.infra.materialization.batch_materialization_engine import MaterializationTask
+
+
+class FeatureBuilder(ABC):


I suppose my concern with calling it FeatureBuilder rather than DAGBuilder is because users will say that this isn't a fit for RAG.

I think RAG or embedding is still a type of feature, so that should still work?
Tecton defines Embedding into feature directly: https://docs.tecton.ai/docs/beta/defining-features/feature-views/embeddings which is also interesting.
For feast, RAG or embedding can be either custom udf in Transformation, and maybe like Aggregation a separate API (Or merge into a special type of Transformation).

Yeah, I don't think Tecton has captured the audience there candidly. But maybe that's fine for now and we can revisit later so no worries.

franciscojavierarceo

in this:

def transform_feature(df: DataFrame) -> DataFrame:
    df = df.withColumn("sum_conv_rate", df["sum_conv_rate"] * 2)
    df = df.withColumn("avg_acc_rate", df["avg_acc_rate"] * 2)
    return df

driver_stats_fv = BatchFeatureView(
    name="driver_hourly_stats",
    entities=[driver],
    mode="python",
    batch_engine=Field(...), # Not supported yet.
    aggregations=[
        Aggregation(column="conv_rate", function="sum"),
        Aggregation(column="acc_rate", function="avg"),
    ],
    join=[...], # Not supported yet
    udf=transform_feature,
    udf_string="transform_feature",
    ttl=timedelta(days=3), # Not supported for materialization yet
    filter="filter_expr", # Not fully support
    schema=[
        Field(name="conv_rate", dtype=Float32),
        Field(name="acc_rate", dtype=Float32),
        Field(name="avg_daily_trips", dtype=Int64),
        Field(name="driver_id", dtype=Int32),
    ],
    online=True,
    offline=False, # not supported yet
    source=data_source,
)

What is the point of the Aggregations in the Feature View?

    aggregations=[
        Aggregation(column="conv_rate", function="sum"),
        Aggregation(column="acc_rate", function="avg"),
    ],

HaoXuAI · 2025-04-15T22:11:54Z

in this:

def transform_feature(df: DataFrame) -> DataFrame:
    df = df.withColumn("sum_conv_rate", df["sum_conv_rate"] * 2)
    df = df.withColumn("avg_acc_rate", df["avg_acc_rate"] * 2)
    return df

driver_stats_fv = BatchFeatureView(
    name="driver_hourly_stats",
    entities=[driver],
    mode="python",
    batch_engine=Field(...), # Not supported yet.
    aggregations=[
        Aggregation(column="conv_rate", function="sum"),
        Aggregation(column="acc_rate", function="avg"),
    ],
    join=[...], # Not supported yet
    udf=transform_feature,
    udf_string="transform_feature",
    ttl=timedelta(days=3), # Not supported for materialization yet
    filter="filter_expr", # Not fully support
    schema=[
        Field(name="conv_rate", dtype=Float32),
        Field(name="acc_rate", dtype=Float32),
        Field(name="avg_daily_trips", dtype=Int64),
        Field(name="driver_id", dtype=Int32),
    ],
    online=True,
    offline=False, # not supported yet
    source=data_source,
)

What is the point of the Aggregations in the Feature View?

    aggregations=[
        Aggregation(column="conv_rate", function="sum"),
        Aggregation(column="acc_rate", function="avg"),
    ],

aggregation from the Design perspective is to simplify defining aggregation features with OOTB API. There are some benefits listed in Tecton: https://docs.tecton.ai/docs/beta/defining-features/feature-views/aggregation-engine. What I think important is the lifetime window support. For the Aggregation you can define
Aggregation(column="conv_rate", function="sum", time_window=timedelta(days=1)) where the compute will handle the window logic for you. For stream compute it is pretty helpful since the time window concept is bit complex for end users to understand such as the Flink window (https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/windows/) we used before

Signed-off-by: HaoXuAI <[email protected]>

franciscojavierarceo · 2025-04-16T00:39:53Z

For the Aggregation you can define
Aggregation(column="conv_rate", function="sum", time_window=timedelta(days=1)) where the compute will handle the window logic for you. For stream compute it is pretty helpful since the time window concept is bit complex for end users to understand such as the Flink window (https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/windows/) we used before

Yeah that makes a lot of sense, I'm good with this.

HaoXuAI · 2025-04-16T00:51:00Z

For the Aggregation you can define
Aggregation(column="conv_rate", function="sum", time_window=timedelta(days=1)) where the compute will handle the window logic for you. For stream compute it is pretty helpful since the time window concept is bit complex for end users to understand such as the Flink window (https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/windows/) we used before

Yeah that makes a lot of sense, I'm good with this.

This is helpful and a really good discussion. And I agree the interface is still immature and definitely subject to change.

franciscojavierarceo

Let's go! 🚀🚀🚀

# [0.49.0](v0.48.0...v0.49.0) (2025-04-29) ### Bug Fixes * Adding brackets to unit tests ([c46fea3](c46fea3)) * Adding logic back for a step ([2bb240b](2bb240b)) * Adjustment for unit test action ([a6f78ae](a6f78ae)) * Allow get_historical_features with only On Demand Feature View ([#5256](#5256)) ([0752795](0752795)) * CI adjustment ([3850643](3850643)) * Embed Query configuration breaks when switching between DataFrame and SQL ([#5257](#5257)) ([32375a5](32375a5)) * Fix for proto issue in utils ([1b291b2](1b291b2)) * Fix milvus online_read ([#5233](#5233)) ([4b91f26](4b91f26)) * Fix tests ([431d9b8](431d9b8)) * Fixed Permissions object parameter in example ([#5259](#5259)) ([045c100](045c100)) * Java CI [#12](#12) ([d7e44ac](d7e44ac)) * Java PR [#15](#15) ([a5da3bb](a5da3bb)) * Java PR [#16](#16) ([e0320fe](e0320fe)) * Java PR [#17](#17) ([49da810](49da810)) * Materialization logs ([#5243](#5243)) ([4aa2f49](4aa2f49)) * Moving to custom github action for checking skip tests ([caf312e](caf312e)) * Operator - remove default replicas setting from Feast Deployment ([#5294](#5294)) ([e416d01](e416d01)) * Patch java pr [#14](#14) ([592526c](592526c)) * Patch update for test ([a3e8967](a3e8967)) * Remove conditional from steps ([995307f](995307f)) * Remove misleading HTTP prefix from gRPC endpoints in logs and doc ([#5280](#5280)) ([0ee3a1e](0ee3a1e)) * removing id ([268ade2](268ade2)) * Renaming workflow file ([5f46279](5f46279)) * Resolve `no pq wrapper` import issue ([#5240](#5240)) ([d5906f1](d5906f1)) * Update actions to remove check skip tests ([#5275](#5275)) ([b976f27](b976f27)) * Update docling demo ([446efea](446efea)) * Update java pr [#13](#13) ([fda7db7](fda7db7)) * Update java_pr ([fa138f4](fa138f4)) * Update repo_config.py ([6a59815](6a59815)) * Update unit tests workflow ([06486a0](06486a0)) * Updated docs for docling demo ([768e6cc](768e6cc)) * Updating action for unit tests ([0996c28](0996c28)) * Updating github actions to filter at job level ([0a09622](0a09622)) * Updating Java CI ([c7c3a3c](c7c3a3c)) * Updating java pr to skip tests ([e997dd9](e997dd9)) * Updating workflows ([c66bcd2](c66bcd2)) ### Features * Add date_partition_column_format for spark source ([#5273](#5273)) ([7a61d6f](7a61d6f)) * Add Milvus tutorial with Feast integration ([#5292](#5292)) ([a1388a5](a1388a5)) * Add pgvector tutorial with PostgreSQL integration ([#5290](#5290)) ([bb1cbea](bb1cbea)) * Add ReactFlow visualization for Feast registry metadata ([#5297](#5297)) ([9768970](9768970)) * Add retrieve online documents v2 method into pgvector ([#5253](#5253)) ([6770ee6](6770ee6)) * Compute Engine Initial Implementation ([#5223](#5223)) ([64bdafd](64bdafd)) * Enable write node for compute engine ([#5287](#5287)) ([f9baf97](f9baf97)) * Local compute engine ([#5278](#5278)) ([8e06dfe](8e06dfe)) * Make transform on writes configurable for ingestion ([#5283](#5283)) ([ecad170](ecad170)) * Offline store update pull_all_from_table_or_query to make timestampfield optional ([#5281](#5281)) ([4b94608](4b94608)) * Serialization version 2 deprecation notice ([#5248](#5248)) ([327d99d](327d99d)) * Vector length definition moved to Feature View from Config ([#5289](#5289)) ([d8f1c97](d8f1c97))

* add compute engine Signed-off-by: HaoXuAI <[email protected]> * fix linting Signed-off-by: HaoXuAI <[email protected]> * fix linting Signed-off-by: HaoXuAI <[email protected]> * fix linting Signed-off-by: HaoXuAI <[email protected]> * fix linting Signed-off-by: HaoXuAI <[email protected]> * add doc Signed-off-by: HaoXuAI <[email protected]> * add test Signed-off-by: HaoXuAI <[email protected]> * add integration test Signed-off-by: HaoXuAI <[email protected]> * update API Signed-off-by: HaoXuAI <[email protected]> * update API Signed-off-by: HaoXuAI <[email protected]> * update API Signed-off-by: HaoXuAI <[email protected]> * update API Signed-off-by: HaoXuAI <[email protected]> * update API Signed-off-by: HaoXuAI <[email protected]> * fix linting Signed-off-by: HaoXuAI <[email protected]> * update doc Signed-off-by: HaoXuAI <[email protected]> * update doc Signed-off-by: HaoXuAI <[email protected]> * update test Signed-off-by: HaoXuAI <[email protected]> * update doc Signed-off-by: HaoXuAI <[email protected]> --------- Signed-off-by: HaoXuAI <[email protected]> Signed-off-by: Jacob Weinhold <[email protected]>

# [0.49.0](feast-dev/feast@v0.48.0...v0.49.0) (2025-04-29) ### Bug Fixes * Adding brackets to unit tests ([c46fea3](feast-dev@c46fea3)) * Adding logic back for a step ([2bb240b](feast-dev@2bb240b)) * Adjustment for unit test action ([a6f78ae](feast-dev@a6f78ae)) * Allow get_historical_features with only On Demand Feature View ([feast-dev#5256](feast-dev#5256)) ([0752795](feast-dev@0752795)) * CI adjustment ([3850643](feast-dev@3850643)) * Embed Query configuration breaks when switching between DataFrame and SQL ([feast-dev#5257](feast-dev#5257)) ([32375a5](feast-dev@32375a5)) * Fix for proto issue in utils ([1b291b2](feast-dev@1b291b2)) * Fix milvus online_read ([feast-dev#5233](feast-dev#5233)) ([4b91f26](feast-dev@4b91f26)) * Fix tests ([431d9b8](feast-dev@431d9b8)) * Fixed Permissions object parameter in example ([feast-dev#5259](feast-dev#5259)) ([045c100](feast-dev@045c100)) * Java CI [feast-dev#12](feast-dev#12) ([d7e44ac](feast-dev@d7e44ac)) * Java PR [feast-dev#15](feast-dev#15) ([a5da3bb](feast-dev@a5da3bb)) * Java PR [feast-dev#16](feast-dev#16) ([e0320fe](feast-dev@e0320fe)) * Java PR [feast-dev#17](feast-dev#17) ([49da810](feast-dev@49da810)) * Materialization logs ([feast-dev#5243](feast-dev#5243)) ([4aa2f49](feast-dev@4aa2f49)) * Moving to custom github action for checking skip tests ([caf312e](feast-dev@caf312e)) * Operator - remove default replicas setting from Feast Deployment ([feast-dev#5294](feast-dev#5294)) ([e416d01](feast-dev@e416d01)) * Patch java pr [feast-dev#14](feast-dev#14) ([592526c](feast-dev@592526c)) * Patch update for test ([a3e8967](feast-dev@a3e8967)) * Remove conditional from steps ([995307f](feast-dev@995307f)) * Remove misleading HTTP prefix from gRPC endpoints in logs and doc ([feast-dev#5280](feast-dev#5280)) ([0ee3a1e](feast-dev@0ee3a1e)) * removing id ([268ade2](feast-dev@268ade2)) * Renaming workflow file ([5f46279](feast-dev@5f46279)) * Resolve `no pq wrapper` import issue ([feast-dev#5240](feast-dev#5240)) ([d5906f1](feast-dev@d5906f1)) * Update actions to remove check skip tests ([feast-dev#5275](feast-dev#5275)) ([b976f27](feast-dev@b976f27)) * Update docling demo ([446efea](feast-dev@446efea)) * Update java pr [feast-dev#13](feast-dev#13) ([fda7db7](feast-dev@fda7db7)) * Update java_pr ([fa138f4](feast-dev@fa138f4)) * Update repo_config.py ([6a59815](feast-dev@6a59815)) * Update unit tests workflow ([06486a0](feast-dev@06486a0)) * Updated docs for docling demo ([768e6cc](feast-dev@768e6cc)) * Updating action for unit tests ([0996c28](feast-dev@0996c28)) * Updating github actions to filter at job level ([0a09622](feast-dev@0a09622)) * Updating Java CI ([c7c3a3c](feast-dev@c7c3a3c)) * Updating java pr to skip tests ([e997dd9](feast-dev@e997dd9)) * Updating workflows ([c66bcd2](feast-dev@c66bcd2)) ### Features * Add date_partition_column_format for spark source ([feast-dev#5273](feast-dev#5273)) ([7a61d6f](feast-dev@7a61d6f)) * Add Milvus tutorial with Feast integration ([feast-dev#5292](feast-dev#5292)) ([a1388a5](feast-dev@a1388a5)) * Add pgvector tutorial with PostgreSQL integration ([feast-dev#5290](feast-dev#5290)) ([bb1cbea](feast-dev@bb1cbea)) * Add ReactFlow visualization for Feast registry metadata ([feast-dev#5297](feast-dev#5297)) ([9768970](feast-dev@9768970)) * Add retrieve online documents v2 method into pgvector ([feast-dev#5253](feast-dev#5253)) ([6770ee6](feast-dev@6770ee6)) * Compute Engine Initial Implementation ([feast-dev#5223](feast-dev#5223)) ([64bdafd](feast-dev@64bdafd)) * Enable write node for compute engine ([feast-dev#5287](feast-dev#5287)) ([f9baf97](feast-dev@f9baf97)) * Local compute engine ([feast-dev#5278](feast-dev#5278)) ([8e06dfe](feast-dev@8e06dfe)) * Make transform on writes configurable for ingestion ([feast-dev#5283](feast-dev#5283)) ([ecad170](feast-dev@ecad170)) * Offline store update pull_all_from_table_or_query to make timestampfield optional ([feast-dev#5281](feast-dev#5281)) ([4b94608](feast-dev@4b94608)) * Serialization version 2 deprecation notice ([feast-dev#5248](feast-dev#5248)) ([327d99d](feast-dev@327d99d)) * Vector length definition moved to Feature View from Config ([feast-dev#5289](feast-dev#5289)) ([d8f1c97](feast-dev@d8f1c97)) Signed-off-by: Jacob Weinhold <[email protected]>

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franciscojavierarceo mentioned this pull request Jan 14, 2026

Add blog post on new transformation framework #5856

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Copilot AI mentioned this pull request Jan 14, 2026

docs: Add blog post on unified transformation framework #5858

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		class SparkDAGBuilder(DAGBuilder):
		def build_source_node(self):

		from feast.infra.materialization.batch_materialization_engine import MaterializationTask


		class FeatureBuilder(ABC):

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HaoXuAI Apr 15, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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HaoXuAI commented Apr 15, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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HaoXuAI commented Apr 4, 2025 •

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