add elasticsearch documentation

docs/reference/online-stores/elasticsearch.md

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+# Elasticsearch online store
+
+## Description
+
+The [Elasticsearch](https://www.elastic.co/) online store provides support for materializing feature values into an Elasticsearch database for serving online features.
+
+Features:
+* Creating indexes and documents through parameters in FeatureView
+* Support for float and binary vector embeddings
+* Full support for HNSW indexing for available metric types in Elasticsearch
+
+## Getting started
+In order to use this online store, you'll need to install the elasticsearch extra:
+- `pip install 'feast[elasticsearch]'`
+
+## Example
+
+Connecting to an Elasticsearch instance:
+
+{% code title="feature_store.yaml" %}
+```yaml
+project: my_feature_repo
+registry: data/registry.db
+provider: local
+online_store:
+  type: elasticsearch
+  endpoint: ELASTICSEARCH_URL
+  username: ELASTICSEARCH_USERNAME
+  password: ELASTICSEARCH_PASSWORD
+```
+{% endcode %}
+
+The full set of configuration options is available in [ElasticsearchOnlineStoreConfig](https://github.com/ExpediaGroup/feast/blob/master/sdk/python/feast/expediagroup/vectordb/elasticsearch_online_store.py#L40).
+
+## Elasticsearch Online Store Format
+
+### Overview
+
+Elasticsearch is a document search engine with vector embedding indexing and knn support.
+
+Elasticsearch organizes data into _indexes_. An index contains a series of object documents. A document can contain one vector field one the top-level of a document. A vector can be either an array of floats or binary (bytes). The vector is indexed using Hierarchical Navigable Small World (HNSW) graph.
+
+### Managing Elasticsearch Resources Through Feature View
+
+An index will reflect the schema of a FeatureView. The name of the FeatureView will also be the name of the index in Elasticsearch. The online store implementation translates the schema of the _feature view_ into the equivalent schema of a document.
+
+Vector indexing is defined through the _field_ level tags. The following tags are expected:
+
+* index_type: must be `hnsw`. This tag is used to identify which field should be indexed as a vector embedding.
+* index_params: optional parameters to override the default values (m=16, ef_construction=100)
+  Example:
+```python
+    "index_params": """{
+        "m": 64,
+        "ef_construction": 512
+    }""",
+```
+* metric_type: type of metrics used to measure the similarity of vectors. Check the [documentation](https://www.elastic.co/guide/en/elasticsearch/reference/8.8/dense-vector.html) to learn which types exist. The value is expected to be a lowercase string.
+
+
+An example feature view:
+```python
+        FeatureView(
+            name="books",
+            schema= [
+                    Field(
+                    name="book_id",
+                    dtype=Int64,
+                ),
+                Field(
+                    name="book_embedding",
+                    dtype=Array(Float32),
+                    tags={
+                        "description": "book embedding of the content",
+                        "dimensions": 2200,
+                        "index_type": "hnsw",
+                        "index_params": {
+                            "m": 64,
+                            "ef_construction": 512,
+                        },
+                        "metric_type": "l2_norm",
+                    }
+                ),
+            ],
+            source=SOURCE,
+        )
+```
+
+This will create an index named _books_ with two fields: _book_id_ and _book_embedding_.
+
+_book_embedding_ is the vector field of the collection. This field will have the following properties:
+* float vectors with 2200 dimensions
+* index type is HNSW with 16 member nodes (_m_) and 100 nearest neighbor candidates (_ef_construction_)
+* metric type is Euclidian distance (_l2_norm_)
+
+## Functionality Matrix
+
+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 Elasticsearch online store.
+
+|                                                           | Elasticsearch |
+| :-------------------------------------------------------- |:--------------|
+| 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                          | no            |
+| readable by Python SDK                                    | yes           |
+| readable by Java                                          | no            |
+| readable by Go                                            | no            |
+| support for entityless feature views                      | no            |
+| support for concurrent writing to the same key            | yes           |
+| 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](overview.md#functionality-matrix).