docs: Add type system docs and add details to data source docs (feast…

…-dev#3108) * Data source docs Signed-off-by: Felix Wang <wangfelix98@gmail.com> * Type system docs Signed-off-by: Felix Wang <wangfelix98@gmail.com> * Update data source docs Signed-off-by: Felix Wang <wangfelix98@gmail.com> * Update docs Signed-off-by: Felix Wang <wangfelix98@gmail.com> Signed-off-by: Felix Wang <wangfelix98@gmail.com>
srausser · Aug 23, 2022 · fad45ca · fad45ca
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diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
@@ -62,7 +62,9 @@
 ## Reference
 
 * [Codebase Structure](reference/codebase-structure.md)
+* [Type System](reference/type-system.md)
 * [Data sources](reference/data-sources/README.md)
+  * [Overview](reference/data-sources/overview.md)
   * [File](reference/data-sources/file.md)
   * [Snowflake](reference/data-sources/snowflake.md)
   * [BigQuery](reference/data-sources/bigquery.md)

diff --git a/docs/reference/data-sources/README.md b/docs/reference/data-sources/README.md
@@ -1,6 +1,10 @@
 # Data sources
 
-Please see [Data Source](../../getting-started/concepts/data-ingestion.md) for an explanation of data sources.
+Please see [Data Source](../../getting-started/concepts/data-ingestion.md) for a conceptual explanation of data sources.
+
+{% content-ref url="overview.md" %}
+[overview.md](overview.md)
+{% endcontent-ref %}
 
 {% content-ref url="file.md" %}
 [file.md](file.md)

diff --git a/docs/reference/data-sources/bigquery.md b/docs/reference/data-sources/bigquery.md
@@ -30,3 +30,8 @@ BigQuerySource(
 ```
 
 The full set of configuration options is available [here](https://rtd.feast.dev/en/latest/index.html#feast.infra.offline_stores.bigquery_source.BigQuerySource).
+
+## Supported Types
+
+BigQuery data sources support all eight primitive types and their corresponding array types.
+For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
diff --git a/docs/reference/data-sources/file.md b/docs/reference/data-sources/file.md
@@ -22,3 +22,8 @@ parquet_file_source = FileSource(
 ```
 
 The full set of configuration options is available [here](https://rtd.feast.dev/en/latest/index.html#feast.infra.offline_stores.file_source.FileSource).
+
+## Supported Types
+
+File data sources support all eight primitive types and their corresponding array types.
+For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
diff --git a/docs/reference/data-sources/overview.md b/docs/reference/data-sources/overview.md
@@ -0,0 +1,31 @@
+# Overview
+
+## Functionality
+
+In Feast, each batch data source is associated with a corresponding offline store.
+For example, a `SnowflakeSource` can only be processed by the Snowflake offline store.
+Otherwise, the primary difference between batch data sources is the set of supported types.
+Feast has an internal type system, and aims to support eight primitive types (`bytes`, `string`, `int32`, `int64`, `float32`, `float64`, `bool`, and `timestamp`) along with the corresponding array types.
+However, not every batch data source supports all of these types.
+
+For more details on the Feast type system, see [here](../type-system.md).
+
+## Functionality Matrix
+
+There are currently four core batch data source implementations: `FileSource`, `BigQuerySource`, `SnowflakeSource`, and `RedshiftSource`.
+There are several additional implementations contributed by the Feast community (`PostgreSQLSource`, `SparkSource`, and `TrinoSource`), which are not guaranteed to be stable or to match the functionality of the core implementations.
+Details for each specific data source can be found [here](README.md).
+
+Below is a matrix indicating which data sources support which types.
+
+| | File | BigQuery | Snowflake | Redshift | Postgres | Spark | Trino |
+| :-------------------------------- | :-- | :-- | :-- | :-- | :-- | :-- | :-- |
+| `bytes`     | yes | yes | yes | yes | yes | yes | yes |
+| `string`    | yes | yes | yes | yes | yes | yes | yes |
+| `int32`     | yes | yes | yes | yes | yes | yes | yes |
+| `int64`     | yes | yes | yes | yes | yes | yes | yes |
+| `float32`   | yes | yes | yes | yes | yes | yes | yes |
+| `float64`   | yes | yes | yes | yes | yes | yes | yes |
+| `bool`      | yes | yes | yes | yes | yes | yes | yes |
+| `timestamp` | yes | yes | yes | yes | yes | yes | yes |
+| array types | yes | yes | no  | no  | yes | yes | no  |
diff --git a/docs/reference/data-sources/postgres.md b/docs/reference/data-sources/postgres.md
@@ -28,3 +28,8 @@ driver_stats_source = PostgreSQLSource(
 ```
 
 The full set of configuration options is available [here](https://rtd.feast.dev/en/master/#feast.infra.offline_stores.contrib.postgres_offline_store.postgres_source.PostgreSQLSource).
+
+## Supported Types
+
+PostgreSQL data sources support all eight primitive types and their corresponding array types.
+For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
diff --git a/docs/reference/data-sources/redshift.md b/docs/reference/data-sources/redshift.md
@@ -30,3 +30,8 @@ my_redshift_source = RedshiftSource(
 ```
 
 The full set of configuration options is available [here](https://rtd.feast.dev/en/master/#feast.infra.offline_stores.redshift_source.RedshiftSource).
+
+## Supported Types
+
+Redshift data sources support all eight primitive types, but currently do not support array types.
+For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
diff --git a/docs/reference/data-sources/snowflake.md b/docs/reference/data-sources/snowflake.md
@@ -43,3 +43,8 @@ In particular, you can read more about quote identifiers [here](https://docs.sno
 {% endhint %}
 
 The full set of configuration options is available [here](https://rtd.feast.dev/en/latest/index.html#feast.infra.offline_stores.snowflake_source.SnowflakeSource).
+
+## Supported Types
+
+Snowflake data sources support all eight primitive types, but currently do not support array types.
+For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
diff --git a/docs/reference/data-sources/spark.md b/docs/reference/data-sources/spark.md
@@ -52,3 +52,8 @@ my_spark_source = SparkSource(
 ```
 
 The full set of configuration options is available [here](https://rtd.feast.dev/en/master/#feast.infra.offline_stores.contrib.spark_offline_store.spark_source.SparkSource).
+
+## Supported Types
+
+Spark data sources support all eight primitive types and their corresponding array types.
+For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
diff --git a/docs/reference/data-sources/trino.md b/docs/reference/data-sources/trino.md
@@ -27,3 +27,8 @@ driver_hourly_stats = TrinoSource(
 ```
 
 The full set of configuration options is available [here](https://rtd.feast.dev/en/master/#trino-source).
+
+## Supported Types
+
+Trino data sources support all eight primitive types, but currently do not support array types.
+For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
diff --git a/docs/reference/offline-stores/README.md b/docs/reference/offline-stores/README.md
@@ -2,8 +2,6 @@
 
 Please see [Offline Store](../../getting-started/architecture-and-components/offline-store.md) for a conceptual explanation of offline stores.
 
-## Reference
-
 {% content-ref url="overview.md" %}
 [overview.md](overview.md)
 {% endcontent-ref %}

diff --git a/docs/reference/type-system.md b/docs/reference/type-system.md
@@ -0,0 +1,41 @@
+# Type System
+
+## Motivation
+
+Feast uses an internal type system to provide guarantees on training and serving data.
+Feast currently supports eight primitive types - `INT32`, `INT64`, `FLOAT32`, `FLOAT64`, `STRING`, `BYTES`, `BOOL`, and `UNIX_TIMESTAMP` - and the corresponding array types.
+Null types are not supported, although the `UNIX_TIMESTAMP` type is nullable.
+The type system is controlled by [`Value.proto`](https://github.com/feast-dev/feast/blob/master/protos/feast/types/Value.proto) in protobuf and by [`types.py`](https://github.com/feast-dev/feast/blob/master/sdk/python/feast/types.py) in Python.
+Type conversion logic can be found in [`type_map.py`](https://github.com/feast-dev/feast/blob/master/sdk/python/feast/type_map.py).
+
+## Examples
+
+### Feature inference
+
+During `feast apply`, Feast runs schema inference on the data sources underlying feature views.
+For example, if the `schema` parameter is not specified for a feature view, Feast will examine the schema of the underlying data source to determine the event timestamp column, feature columns, and entity columns.
+Each of these columns must be associated with a Feast type, which requires conversion from the data source type system to the Feast type system.
+* The feature inference logic calls `_infer_features_and_entities`.
+* `_infer_features_and_entities` calls `source_datatype_to_feast_value_type`.
+* `source_datatype_to_feast_value_type` cals the appropriate method in `type_map.py`. For example, if a `SnowflakeSource` is being examined, `snowflake_python_type_to_feast_value_type` from `type_map.py` will be called.
+
+### Materialization
+
+Feast serves feature values as [`Value`](https://github.com/feast-dev/feast/blob/master/protos/feast/types/Value.proto) proto objects, which have a type corresponding to Feast types.
+Thus Feast must materialize feature values into the online store as `Value` proto objects.
+* The local materialization engine first pulls the latest historical features and converts it to pyarrow.
+* Then it calls `_convert_arrow_to_proto` to convert the pyarrow table to proto format.
+* This calls `python_values_to_proto_values` in `type_map.py` to perform the type conversion.
+
+### Historical feature retrieval
+
+The Feast type system is typically not necessary when retrieving historical features.
+A call to `get_historical_features` will return a `RetrievalJob` object, which allows the user to export the results to one of several possible locations: a Pandas dataframe, a pyarrow table, a data lake (e.g. S3 or GCS), or the offline store (e.g. a Snowflake table).
+In all of these cases, the type conversion is handled natively by the offline store.
+For example, a BigQuery query exposes a `to_dataframe` method that will automatically convert the result to a dataframe, without requiring any conversions within Feast.
+
+### Feature serving
+
+As mentioned above in the section on [materialization](#materialization), Feast persists feature values into the online store as `Value` proto objects.
+A call to `get_online_features` will return an `OnlineResponse` object, which essentially wraps a bunch of `Value` protos with some metadata.
+The `OnlineResponse` object can then be converted into a Python dictionary, which calls `feast_value_type_to_python_type` from `type_map.py`, a utility that converts the Feast internal types to Python native types.