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| Original file line number | Diff line number | Diff line change |
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| # Athena source (contrib) | ||
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| ## Description | ||
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| Athena data sources are AWS Athena tables or views. | ||
| These can be specified either by a table reference or a SQL query. | ||
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| ## Disclaimer | ||
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| The Athena data source does not achieve full test coverage. | ||
| Please do not assume complete stability. | ||
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| ## Examples | ||
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| Defining an Athena source: | ||
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| ```python | ||
| from feast.infra.offline_stores.contrib.athena_offline_store.athena_source import ( | ||
| AthenaSource, | ||
| ) | ||
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| driver_stats_source = AthenaSource( | ||
| name="driver_hourly_stats", | ||
| table="driver_hourly_stats", | ||
| database="my_database", | ||
| data_source="AwsDataCatalog", | ||
| timestamp_field="event_timestamp", | ||
| created_timestamp_column="created", | ||
| ) | ||
| ``` | ||
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| The full set of configuration options is available [here](https://rtd.feast.dev/en/master/#feast.infra.offline_stores.contrib.athena_offline_store.athena_source.AthenaSource). | ||
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| ## Supported Types | ||
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| Athena data sources support standard Athena types mapped through the AWS Athena API. | ||
| For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix). |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,66 @@ | ||
| # Athena offline store (contrib) | ||
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| ## Description | ||
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| The Athena offline store provides support for reading [AthenaSources](../data-sources/athena.md). | ||
| * Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. | ||
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| ## Disclaimer | ||
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| The Athena offline store does not achieve full test coverage. | ||
| Please do not assume complete stability. | ||
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| ## Getting started | ||
| In order to use this offline store, you'll need to run `pip install 'feast[aws]'`. | ||
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| ## Example | ||
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| {% code title="feature_store.yaml" %} | ||
| ```yaml | ||
| project: my_project | ||
| registry: data/registry.db | ||
| provider: local | ||
| offline_store: | ||
| type: athena | ||
| data_source: AwsDataCatalog | ||
| region: us-east-1 | ||
| database: my_database | ||
| workgroup: primary | ||
| online_store: | ||
| path: data/online_store.db | ||
| ``` | ||
| {% endcode %} | ||
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| The full set of configuration options is available in [AthenaOfflineStoreConfig](https://rtd.feast.dev/en/master/#feast.infra.offline_stores.contrib.athena_offline_store.athena.AthenaOfflineStoreConfig). | ||
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| ## Functionality Matrix | ||
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| The set of functionality supported by offline stores is described in detail [here](overview.md#functionality). | ||
| Below is a matrix indicating which functionality is supported by the Athena offline store. | ||
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| | | Athena | | ||
| | :----------------------------------------------------------------- |:-------| | ||
| | `get_historical_features` (point-in-time correct join) | yes | | ||
| | `pull_latest_from_table_or_query` (retrieve latest feature values) | yes | | ||
| | `pull_all_from_table_or_query` (retrieve a saved dataset) | yes | | ||
| | `offline_write_batch` (persist dataframes to offline store) | no | | ||
| | `write_logged_features` (persist logged features to offline store) | yes | | ||
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| Below is a matrix indicating which functionality is supported by `AthenaRetrievalJob`. | ||
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| | | Athena | | ||
| | ----------------------------------------------------- |--------| | ||
| | export to dataframe | yes | | ||
| | export to arrow table | yes | | ||
| | export to arrow batches | no | | ||
| | export to SQL | yes | | ||
| | export to data lake (S3, GCS, etc.) | no | | ||
| | export to data warehouse | no | | ||
| | export as Spark dataframe | no | | ||
| | local execution of Python-based on-demand transforms | yes | | ||
| | remote execution of Python-based on-demand transforms | no | | ||
| | persist results in the offline store | yes | | ||
| | preview the query plan before execution | yes | | ||
| | read partitioned data | yes | | ||
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| To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix). |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,57 @@ | ||
| # Faiss online store | ||
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| ## Description | ||
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| The [Faiss](https://github.com/facebookresearch/faiss) online store provides support for materializing feature values and performing vector similarity search using Facebook AI Similarity Search (Faiss). Faiss is a library for efficient similarity search and clustering of dense vectors, making it well-suited for use cases involving embeddings and nearest-neighbor lookups. | ||
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| ## Getting started | ||
| In order to use this online store, you'll need to install the Faiss dependency. E.g. | ||
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| `pip install 'feast[faiss]'` | ||
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| ## Example | ||
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| {% code title="feature_store.yaml" %} | ||
| ```yaml | ||
| project: my_feature_repo | ||
| registry: data/registry.db | ||
| provider: local | ||
| online_store: | ||
| type: feast.infra.online_stores.faiss_online_store.FaissOnlineStore | ||
| dimension: 128 | ||
| index_path: data/faiss_index | ||
| index_type: IVFFlat # optional, default: IVFFlat | ||
| nlist: 100 # optional, default: 100 | ||
| ``` | ||
| {% endcode %} | ||
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| **Note:** Faiss is not registered as a named online store type. You must use the fully qualified class path as the `type` value. | ||
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| The full set of configuration options is available in [FaissOnlineStoreConfig](https://rtd.feast.dev/en/master/#feast.infra.online_stores.faiss_online_store.FaissOnlineStoreConfig). | ||
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| ## Functionality Matrix | ||
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| The set of functionality supported by online stores is described in detail [here](overview.md#functionality). | ||
| Below is a matrix indicating which functionality is supported by the Faiss online store. | ||
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| | | Faiss | | ||
| |:----------------------------------------------------------|:------| | ||
| | write feature values to the online store | yes | | ||
| | read feature values from the online store | yes | | ||
| | update infrastructure (e.g. tables) in the online store | yes | | ||
| | teardown infrastructure (e.g. tables) in the online store | yes | | ||
| | generate a plan of infrastructure changes | no | | ||
| | support for on-demand transforms | yes | | ||
| | readable by Python SDK | yes | | ||
| | readable by Java | no | | ||
| | readable by Go | no | | ||
| | support for entityless feature views | yes | | ||
| | support for concurrent writing to the same key | no | | ||
| | support for ttl (time to live) at retrieval | no | | ||
| | support for deleting expired data | no | | ||
| | collocated by feature view | yes | | ||
| | collocated by feature service | no | | ||
| | collocated by entity key | no | | ||
| | vector similarity search | yes | | ||
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| To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix). |
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| Original file line number | Diff line number | Diff line change |
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| # HBase online store | ||
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| ## Description | ||
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| The [HBase](https://hbase.apache.org/) online store provides support for materializing feature values into an Apache HBase database for serving online features in real-time. | ||
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| * Each feature view is mapped to an HBase table | ||
| * Connects to HBase via the Thrift server using [happybase](https://happybase.readthedocs.io/) | ||
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| ## Getting started | ||
| In order to use this online store, you'll need to run `pip install 'feast[hbase]'`. | ||
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| ## Example | ||
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| {% code title="feature_store.yaml" %} | ||
| ```yaml | ||
| project: my_feature_repo | ||
| registry: data/registry.db | ||
| provider: local | ||
| online_store: | ||
| type: hbase | ||
| host: localhost | ||
| port: "9090" | ||
| connection_pool_size: 4 # optional | ||
| protocol: binary # optional | ||
| transport: buffered # optional | ||
| ``` | ||
| {% endcode %} | ||
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| The full set of configuration options is available in [HbaseOnlineStoreConfig](https://rtd.feast.dev/en/master/#feast.infra.online_stores.hbase_online_store.hbase.HbaseOnlineStoreConfig). | ||
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| ## Functionality Matrix | ||
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| The set of functionality supported by online stores is described in detail [here](overview.md#functionality). | ||
| Below is a matrix indicating which functionality is supported by the HBase online store. | ||
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| | | HBase | | ||
| | :-------------------------------------------------------- | :---- | | ||
| | write feature values to the online store | yes | | ||
| | read feature values from the online store | yes | | ||
| | update infrastructure (e.g. tables) in the online store | yes | | ||
| | teardown infrastructure (e.g. tables) in the online store | yes | | ||
| | generate a plan of infrastructure changes | no | | ||
| | support for on-demand transforms | yes | | ||
| | readable by Python SDK | yes | | ||
| | readable by Java | no | | ||
| | readable by Go | no | | ||
| | support for entityless feature views | yes | | ||
| | support for concurrent writing to the same key | no | | ||
| | support for ttl (time to live) at retrieval | no | | ||
| | support for deleting expired data | no | | ||
| | collocated by feature view | yes | | ||
| | collocated by feature service | no | | ||
| | collocated by entity key | no | | ||
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| To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix). |
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