# 98.9% faster queries, 4x more indexing throughput: a systematic Elasticsearch performance diagnosis

DevFeed: [98.9% faster queries, 4x more indexing throughput: a systematic Elasticsearch performance diagnosis](<https://devfeed.tech/articles/98-9-faster-queries-4x-more-indexing-throughput-a-systematic-elasticsearch-performance-diagnosis-78802.md>)

Original publisher: [Read original article](<https://www.elastic.co/search-labs/blog/elasticsearch-performance-diagnosis>)

Author: Aleksandar Panov

Published: 2026-07-15T00:00:00Z

Content type: tutorial

Language: en

Sources: [Elasticsearch Labs](<https://devfeed.tech/sources/elasticsearch-labs.md>)

Topics: [elasticsearch](<https://devfeed.tech/topics/elasticsearch.md>), [performance-engineering](<https://devfeed.tech/topics/performance-engineering.md>), [Rockset](<https://devfeed.tech/topics/rockset.md>)

Tags: [autoops](<https://devfeed.tech/tags/autoops.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [indexing](<https://devfeed.tech/tags/indexing.md>), [latency](<https://devfeed.tech/tags/latency.md>), [operations](<https://devfeed.tech/tags/operations.md>), [performance](<https://devfeed.tech/tags/performance.md>)

## AI overview

The article presents a workflow for diagnosing Elasticsearch performance issues with AutoOps, the Profile API, and ES Rally. In a delivery logistics example, it traces search latency to shard imbalance and deep pagination, then shows how search_after reduced query time and index-setting changes improved bulk indexing throughput.

## Source excerpt

Use AutoOps, the Profile API and ES Rally together to find cluster hotspots, slow queries and index bottlenecks, with real benchmarks showing a 98.9% latency cut and 4x indexing gain.