docs/reference/online-stores/elasticsearch.md
The ElasticSearch online store provides support for materializing tabular feature values, as well as embedding feature vectors, into an ElasticSearch index for serving online features.
The embedding feature vectors are stored as dense vectors, and can be used for similarity search. More information on dense vectors can be found here.
In order to use this online store, you'll need to run pip install 'feast[elasticsearch]'. You can get started by then running feast init -t elasticsearch.
{% code title="feature_store.yaml" %}
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: elasticsearch
host: ES_HOST
port: ES_PORT
user: ES_USERNAME
password: ES_PASSWORD
write_batch_size: 1000
{% endcode %}
The full set of configuration options is available in ElasticsearchOnlineStoreConfig.
| Postgres | |
|---|---|
| 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 |
To compare this set of functionality against other online stores, please see the full functionality matrix.
The ElasticSearch online store supports retrieving document vectors for a given list of entity keys. The document vectors are returned as a dictionary where the key is the entity key and the value is the document vector. The document vector is a dense vector of floats.
{% code title="python" %}
from feast import FeatureStore
feature_store = FeatureStore(repo_path="feature_store.yaml")
query_vector = [1.0, 2.0, 3.0, 4.0, 5.0]
top_k = 5
# Retrieve the top k closest features to the query vector
feature_values = feature_store.retrieve_online_documents_v2(
features=["my_feature"],
query=query_vector,
top_k=top_k,
)
{% endcode %}
Currently, the indexing mapping in the ElasticSearch online store is configured as:
{% code title="indexing_mapping" %}
{
"dynamic_templates": [
{
"feature_objects": {
"match_mapping_type": "object",
"match": "*",
"mapping": {
"type": "object",
"properties": {
"feature_value": {"type": "binary"},
"value_text": {"type": "text"},
"vector_value": {
"type": "dense_vector",
"dims": vector_field_length,
"index": True,
"similarity": config.online_store.similarity,
},
},
},
}
}
],
"properties": {
"entity_key": {"type": "keyword"},
"timestamp": {"type": "date"},
"created_ts": {"type": "date"},
},
}
{% endcode %} And the online_read API mapping is configured as:
{% code title="online_read_mapping" %}
"query": {
"bool": {
"must": [
{"terms": {"entity_key": entity_keys}},
{"terms": {"feature_name": requested_features}},
]
}
},
{% endcode %}
And the similarity search API mapping is configured as:
{% code title="similarity_search_mapping" %}
{
"field": "vector_value",
"query_vector": embedding_vector,
"k": top_k,
}
{% endcode %}
These APIs are subject to change in future versions of Feast to improve performance and usability.