---
title: "ElasticsearchDocumentStore"
id: elasticsearch-document-store
slug: "/elasticsearch-document-store"
description: "Use an Elasticsearch database with Haystack."
---

# ElasticsearchDocumentStore

Use an Elasticsearch database with Haystack.

<div className="key-value-table">

|               |                                                                                                 |
| :------------ | :---------------------------------------------------------------------------------------------- |
| API reference | [Elasticsearch](/reference/integrations-elasticsearch)                                                 |
| GitHub link   | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/elasticsearch |

</div>

ElasticsearchDocumentStore is excellent if you want to evaluate the performance of different retrieval options (dense vs. sparse) and aim for a smooth transition from PoC to production.

It features the approximate nearest neighbours (ANN) search.

### Initialization

[Install](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html) Elasticsearch and then [start](https://www.elastic.co/guide/en/elasticsearch/reference/current/starting-elasticsearch.html) an instance. Haystack supports Elasticsearch 8.

If you have Docker set up, we recommend pulling the Docker image and running it.

```shell
docker pull docker.elastic.co/elasticsearch/elasticsearch:8.11.1
docker run -p 9200:9200 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms1024m -Xmx1024m" -e "xpack.security.enabled=false" elasticsearch:8.11.1
```

As an alternative, you can go to [Elasticsearch integration GitHub](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/elasticsearch) and start a Docker container running Elasticsearch using the provided `docker-compose.yml`:

```shell
docker compose up
```

Once you have a running Elasticsearch instance, install the `elasticsearch-haystack` integration:

```shell
pip install elasticsearch-haystack
```

Then, initialize an `ElasticsearchDocumentStore` object that’s connected to the Elasticsearch instance and writes documents to it:

```python
from haystack_integrations.document_stores.elasticsearch import (
    ElasticsearchDocumentStore,
)
from haystack import Document

document_store = ElasticsearchDocumentStore(hosts="http://localhost:9200")
document_store.write_documents(
    [Document(content="This is first"), Document(content="This is second")],
)
print(document_store.count_documents())
```

### Supported Retrievers

[`ElasticsearchBM25Retriever`](../pipeline-components/retrievers/elasticsearchbm25retriever.mdx): A keyword-based Retriever that fetches documents matching a query from the Document Store.

[`ElasticsearchEmbeddingRetriever`](../pipeline-components/retrievers/elasticsearchembeddingretriever.mdx): Compares the query and document embeddings and fetches the documents most relevant to the query.
