It looks like Elasticsearch is currently missing support for concurrent searching and that this is a major prospective optimization to reduce latency.

Digression: this is not the topic at hand but I personally consider the fork OpenSearch to be an historical tragedy of duplication of work and a segregation of improvements. What would be even more anti-utilitarist would be to blindly *ignore* each other advances towards making better search.

Lucene concurrent search APIs are probably among the most important feature/optimization currently not leveraged by Elasticsearch.
The fork OpenSearch has a [pull request](https://github.com/opensearch-project/OpenSearch/pull/1500) implementing support for it.

See also the [corresponding issue](https://github.com/opensearch-project/OpenSearch/issues/1286) 

I believe it would be great for end users if you could take inspiration from the PR and work on this feature.

Great blog about the Lucene feature:
https://blog.mikemccandless.com/2019/10/concurrent-query-execution-in-apache.html?m=1
See also those performance results:
https://engineeringblog.yelp.com/2021/09/nrtsearch-yelps-fast-scalable-and-cost-effective-search-engine.html
test/framework/src/main/java/org/elasticsearch/test/ESIntegTestCase.java
}
        return client;
    }
    public static Client dataNodeClient() {
        Client client = internalCluster().dataNodeClient();
        if (frequently()) {
            client = new RandomizingClient(client, random());
        }
        return client;
    }
    public static Iterable<Client> clients() {
        return cluster().getClients();
    }
    protected int minimumNumberOfShards() {
        return DEFAULT_MIN_NUM_SHARDS;
    }
    protected int maximumNumberOfShards() {
        return DEFAULT_MAX_NUM_SHARDS;
    }
    protected int numberOfShards() {
        return between(minimumNumberOfShards(), maximumNumberOfShards());
    }
    protected int minimumNumberOfReplicas() {
        return 0;
    }
    protected int maximumNumberOfReplicas() {
        
        int maxNumReplicas = Math.max(0, cluster().numDataNodes() - 1);
        return frequently() ? Math.min(1, maxNumReplicas) : maxNumReplicas;
    }