# Tune elasticsearch for searching speed

**URL:** <https://discuss.elastic.co/t/tune-elasticsearch-for-searching-speed/370506>\
**Category:** Elasticsearch\
**Tags:** vector-search\
**Created:** [November 13, 2024, 9:34pm UTC](https://discuss.elastic.co/t/tune-elasticsearch-for-searching-speed/370506 "2024-11-13T21:34:07Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![aldoorozco](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/aldoorozco/32/116647_2.png) [@aldoorozco](https://discuss.elastic.co/u/aldoorozco)\
**Post date:** [November 13, 2024, 9:34pm UTC](https://discuss.elastic.co/t/tune-elasticsearch-for-searching-speed/370506/1 "2024-11-13T21:34:07Z")

</div>

Hi community,

Looking for some ideas on ways to improve searching performance of vector searches on `Elasticsearch v8.15.0`.

The index I'm running searches on contains 150M documents (they are only loaded once, and never refreshed afterwards), with just 3 fields: `id`, `name`, and `embeddings` (a 128-dimension vector). The Elasticsearch deployment (ECK) is running on a Kubernetes cluster (GKE) and has 40 data nodes with 30 CPUs and 220 GB of memory each, plus 2 masters and 2 coordinators. The index is configured with 40 primary shards (1 per data node, to speed up initial indexing) and 20 replicas each. The index stats look as follows:

```auto
health status index uuid pri rep docs.count docs.deleted store.size pri.store.size dataset.size
green open myindex _F_ZALb-TgeO9CzXD101Hw 40 20 149410256 0 14.4tb 702.2gb 702.2gb

```

The mapping of the `embeddings` field looks like this:

```auto
{
    "type": "dense_vector",
    "dims": 128,
    "index": True,
    "similarity": "cosine",
    "index_options": {
        "type": "hnsw",
        "ef_construction": 150,
        "m": 24,
    },
}

```

Now, I have another dataset, also 150M rows, that I'm reading from BigQuery using Apache Spark with 5 nodes 8 cores each, and I'm iterating over each partition and sending multi search requests with a batch of 50 queries per msearch (8 tasks per node x 5 nodes x 50 simultaneous queries = 1250 concurrent searches).

I'm using the following query:

```auto
{
    "query": {
        "bool": {
            "should": [{
                "knn": {
                    "field": "embeddings",
                    "query_vector": [1.0, 0.54, 0.01, 1.5, ...],,
                    "k": 10,
                    "num_candidates": 100,
                }
            }]
        }
    },
    "sort": {"_score", "desc"},
    "fields": ["id"],
    "size": 10,
    "_source": false
}

```

As you can imagine, searches take a super long time. I did some estimates and searching 1M rows takes around 2 hours. So, `are there things I can change to improve the search performance?`

Thanks,  
Aldo

---

<div class="post-metadata">

**Author:** ![dadoonet](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/dadoonet/32/137187_2.png) [@dadoonet](https://discuss.elastic.co/u/dadoonet)\
**Post date:** [November 13, 2024, 9:54pm UTC](https://discuss.elastic.co/t/tune-elasticsearch-for-searching-speed/370506/2 "2024-11-13T21:54:38Z")

</div>

Could you upgrade to 8.16.0 and use the new BBQ format? See [🎉 What’s new in Elastic 8.16](https://discuss.elastic.co/t/what-s-new-in-elastic-8-16/370418)

---

<div class="post-metadata">

**Author:** ![aldoorozco](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/aldoorozco/32/116647_2.png) [@aldoorozco](https://discuss.elastic.co/u/aldoorozco)\
**Post date:** [November 13, 2024, 10:18pm UTC](https://discuss.elastic.co/t/tune-elasticsearch-for-searching-speed/370506/3 "2024-11-13T22:18:37Z")

</div>

Wow, fresh out the oven. Will give it a try @dadoonet, thanks.

---

<div class="post-metadata">

**Author:** ![aldoorozco](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/aldoorozco/32/116647_2.png) [@aldoorozco](https://discuss.elastic.co/u/aldoorozco)\
**Post date:** [November 14, 2024, 5:41pm UTC](https://discuss.elastic.co/t/tune-elasticsearch-for-searching-speed/370506/4 "2024-11-14T17:41:57Z")

</div>

@dadoonet unfortunately it's still about the same searching speed with your suggestion. I tried with 1M fields again, and the time is ~1.7 hours. A slight improvement, but nothing significant unfortunately 🙁. Any other suggestions?

---

<div class="post-metadata">

**Author:** ![BenTrent](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/bentrent/32/33915_2.png) [@BenTrent](https://discuss.elastic.co/u/BenTrent)\
**Post date:** [November 14, 2024, 6:17pm UTC](https://discuss.elastic.co/t/tune-elasticsearch-for-searching-speed/370506/5 "2024-11-14T18:17:03Z")

</div>

> and I'm iterating over each partition and sending multi search requests with a batch of 50 queries per msearch

So, you are wanting to do about 150M individual searches over all the vectors?

At your current set up, I would imagine you should be able to do way more than 1k QPS.

What sort of QPS are you measuring with Elasticsearch right now? Note, utilizing single search latency as a stand in for this wouldn't work as you should be able to serve multiple 1000s of search requests at the same time.

I would consider:

- Reducing the number of primaries. If using BBQ, reduce this to 5 or fewer. I would at least switch to `int4_hnsw`.
- Increasing replica count
- Force Merging segments down to below 10 segments