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#To use Athena as an offline store, you need to create an Athena database and an S3 bucket on AWS. https://docs.aws.amazon.com/athena/latest/ug/getting-started.html
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#Modify environment variables ATHENA_DATA_SOURCE, ATHENA_DATABASE, ATHENA_S3_BUCKET_NAME if you want to change the data source, database, and bucket name of S3 to use.
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#If tests fail with the pytest -n 8 option, change the number to 1.
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not test_historical_features_persisting and \
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not test_historical_retrieval_fails_on_validation and \
Copy file name to clipboardExpand all lines: docs/getting-started/concepts/registry.md
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# Registry
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Feast uses a registry to store all applied Feast objects (e.g. Feature views, entities, etc). The registry exposes
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Feast uses a registry to store all applied Feast objects (e.g. Feature views, entities, etc). The registry exposes
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methods to apply, list, retrieve and delete these objects, and is an abstraction with multiple implementations.
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### Options for registry implementations
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#### File-based registry
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By default, Feast uses a file-based registry implementation, which stores the protobuf representation of the registry as
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a serialized file. This registry file can be stored in a local file system, or in cloud storage (in, say, S3 or GCS).
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By default, Feast uses a file-based registry implementation, which stores the protobuf representation of the registry as
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a serialized file. This registry file can be stored in a local file system, or in cloud storage (in, say, S3 or GCS, or Azure).
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The quickstart guides that use `feast init` will use a registry on a local file system. To allow Feast to configure
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The quickstart guides that use `feast init` will use a registry on a local file system. To allow Feast to configure
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a remote file registry, you need to create a GCS / S3 bucket that Feast can understand:
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{% tabs %}
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{% tab title="Example S3 file registry" %}
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{% endtab %}
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{% endtabs %}
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However, there are inherent limitations with a file-based registry, since changing a single field in the registry
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requires re-writing the whole registry file. With multiple concurrent writers, this presents a risk of data loss, or
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bottlenecks writes to the registry since all changes have to be serialized (e.g. when running materialization for
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However, there are inherent limitations with a file-based registry, since changing a single field in the registry
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requires re-writing the whole registry file. With multiple concurrent writers, this presents a risk of data loss, or
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bottlenecks writes to the registry since all changes have to be serialized (e.g. when running materialization for
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multiple feature views or time ranges concurrently).
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#### SQL Registry
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### Updating the registry
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We recommend users store their Feast feature definitions in a version controlled repository, which then via CI/CD
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automatically stays synced with the registry. Users will often also want multiple registries to correspond to
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different environments (e.g. dev vs staging vs prod), with staging and production registries with locked down write
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We recommend users store their Feast feature definitions in a version controlled repository, which then via CI/CD
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automatically stays synced with the registry. Users will often also want multiple registries to correspond to
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different environments (e.g. dev vs staging vs prod), with staging and production registries with locked down write
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access since they can impact real user traffic. See [Running Feast in Production](../../how-to-guides/running-feast-in-production.md#1.-automatically-deploying-changes-to-your-feature-definitions) for details on how to set this up.
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### Accessing the registry from clients
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Users can specify the registry through a `feature_store.yaml` config file, or programmatically. We often see teams
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Users can specify the registry through a `feature_store.yaml` config file, or programmatically. We often see teams
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preferring the programmatic approach because it makes notebook driven development very easy:
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#### Option 1: programmatically specifying the registry
The MsSQL offline store provides support for reading [MsSQL Sources](../data-sources/mssql.md). Specifically, it is developed to read from [Synapse SQL](https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/overview-features) on Microsoft Azure
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* 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 MsSQL offline store does not achieve full test coverage.
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