Couchbase Columnar (contrib)

Description

The Couchbase Columnar offline store provides support for reading CouchbaseColumnarSources. Note that Couchbase Columnar is available through Couchbase Capella.

• Entity dataframes can be provided as a SQL++ query or can be provided as a Pandas dataframe. A Pandas dataframe will be uploaded to Couchbase Capella Columnar as a collection.

Disclaimer

The Couchbase Columnar offline store does not achieve full test coverage. Please do not assume complete stability.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[couchbase]'. You can get started by then running feast init -t couchbase.

To get started with Couchbase Capella Columnar:

1. Sign up for a Couchbase Capella account

2. Deploy a Columnar cluster

3. Create an Access Control Account

   • This account should be able to read and write.

   • For testing purposes, it is recommended to assign all roles to avoid any permission issues.

4. Configure allowed IP addresses

   • You must allow the IP address of the machine running Feast.

Example

feature_store.yaml

project: my_project
registry: data/registry.db
provider: local
offline_store:
  type: couchbase.offline
  connection_string: COUCHBASE_COLUMNAR_CONNECTION_STRING # Copied from Settings > Connection String page in Capella Columnar console, starts with couchbases://
  user: COUCHBASE_COLUMNAR_USER # Couchbase cluster access name from Settings > Access Control page in Capella Columnar console
  password: COUCHBASE_COLUMNAR_PASSWORD # Couchbase password from Settings > Access Control page in Capella Columnar console
  timeout: 120 # Timeout in seconds for Columnar operations, optional
online_store:
    path: data/online_store.db

Note that timeoutis an optional parameter. The full set of configuration options is available in CouchbaseColumnarOfflineStoreConfig.

Functionality Matrix

The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the Couchbase Columnar offline store.

	Couchbase Columnar
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)	no

Below is a matrix indicating which functionality is supported by CouchbaseColumnarRetrievalJob.

	Couchbase Columnar
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.)	yes
export to data warehouse	yes
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

To compare this set of functionality against other offline stores, please see the full functionality matrix.