As data volumes increase and query complexity grows, users often face a fundamental trade-off: speed vs. accuracy. Achieving low-latency results while maintaining high relevance and precision requires careful tuning of your Elasticsearch setup.
Whether you’re managing a real-time analytics platform, an e-commerce search experience, or a log aggregation system, understanding this balance is key to maximizing the value of Elasticsearch. In this article, we explore how to fine-tune Elasticsearch performance without compromising on data accuracy. Or vice versa.
In Elasticsearch, performance and relevance are deeply interconnected:
Speed focuses on how quickly results are returned, often optimized by limiting data, pre-filtering, caching, or reducing query complexity.
Accuracy emphasizes the precision of results based on scoring, ranking, and deep analysis of content and relevance.
Focusing solely on one can degrade the other:
Over-optimizing for speed can skip important scoring processes.
Prioritizing accuracy may lead to slower queries, especially at scale.
The challenge is to find the optimal balance that aligns with your specific use case.
Speed tip: Use filter context instead of query context when relevance scoring is not needed.
Filters are cached and executed faster, while queries are scored, impacting performance.
Accuracy tip: Use match, multi_match, or dis_max queries to improve relevance scoring, especially when working with full-text search.
Improper sharding can severely affect both speed and accuracy.
Use fewer, larger shards to reduce overhead for small indices.
For large datasets, balance shard count across nodes to prevent hot spots.
Monitor with the _cat/shards and _cluster/health APIs.
Related article: Scaling your search with Elasticsearch
Speed tip: Avoid unnecessary fields in the index, disable _all, and use doc_values only where needed.
Accuracy tip: Choose the right analyzer for your language and domain. For example:
Use english analyzer for stemming.
Use custom analyzers for domain-specific vocabularies.
Well-configured analyzers improve search precision without adding performance overhead.
Complex scoring can impact performance but improves result quality.
Function score queries allow dynamic relevance tuning.
Use scripted scoring only when absolutely necessary.
Consider rank features if you’re implementing machine learning-driven ranking.
If your use case is search relevance sensitive (e.g., product search), it's worth the performance cost. If not (e.g., log analysis), skip scoring where possible.
Related article: How Elasticsearch Powers Real-Time Analytics
Elasticsearch supports multiple levels of caching:
Filter cache (automatically applied)
Query result cache (for aggregations)
Shard request cache
Speed tip: Use caching for repetitive, read-heavy queries, especially in dashboards or monitoring systems.
Accuracy trade-off: Cached results may reflect slightly outdated data. If real-time accuracy is essential (e.g., in fraud detection), use fresh queries.
The default from + size pagination can be inefficient at large depths.
For deep pagination, use search_after instead.
Limit max_result_window to reduce memory usage.
Speed tip: Avoid deep pagination altogether. Redesign UX to load more relevant results earlier.
Heavy aggregations are resource-intensive.
Use composite aggregations for large sets.
Prefer runtime fields only when dynamic calculations are unavoidable.
Pre-aggregate data if possible and store results in separate indices.
Related article: OpenSearch vs Elasticsearch: What are the differences and how to choose?
Speed is prioritized over accuracy. Relevance scoring is unnecessary, filters and timestamp-based queries dominate. This allows for faster dashboards and faster incident detection.
Accuracy is key. Relevance, synonyms, typo tolerance and ranking have a direct business impact. Speed still matters, but user experience relies on precision.
Use these tools to measure and adjust performance:
Kibana Monitoring: View indexing rate, search latency, node usage.
Elasticsearch Profiler API: Understand where time is spent in your queries.
Hot Threads API: Detect performance bottlenecks in the JVM.
For ongoing tuning, integrate with observability platforms like Elastic APM or Zabbix.
Avoid unnecessary fields in mappings
Use filters over queries when scoring isn’t needed
Keep shard sizes between 10–50 GB
Optimize analyzers and tokenizers
Monitor slow logs and profile queries
Cache frequent aggregations and filters
Tune JVM heap size and garbage collection
As a recognized Elastic partner and Open Source Competence Center, Syone offers deep expertise in designing, implementing and tuning Elasticsearch architectures for performance and precision. Whether you're running a large-scale analytics platform or a high-relevance search engine, our team provides:
Architecture design tailored to your data and use cases
Performance audits and optimization plans
Integration with ingestion pipelines (Beats, Logstash, Kafka, etc.)
Managed services and real-time monitoring
Ongoing support and training to empower your teams
Our mission is to help you get the most out of Elasticsearch, securely, efficiently, and at scale.
Learn more about our Elastic services or, if you have a case in mind, Contact us and discover how we can improve your company's Elasticsearch performance.