Elasticsearch is a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data for lightning fast search, fine‑tuned relevancy, and powerful analytics.
Elasticsearch Anti-Patterns Overview
Elasticsearch is a powerful search and analytics engine, but using it effectively requires understanding its distributed nature and avoiding common anti-patterns. Here are the most important anti-patterns to avoid when working with Elasticsearch.
Using Dynamic Mapping in Production
Relying on dynamic mapping in production can lead to mapping explosions, unexpected field types, and poor performance. Always define explicit mappings for your indices, specifying the appropriate field types and analyzers for your use case.
Using Too Many Fields in a Document
Having too many fields in a document can lead to mapping explosion and memory issues. Elasticsearch has a default limit of 1000 fields per index. Group related fields into objects and consider whether all fields need to be indexed.
Using Deeply Nested Objects
Deeply nested objects in Elasticsearch can lead to complex queries, mapping explosion, and performance issues. Flatten your document structure where possible, and use nested fields only when necessary for maintaining relationships between fields.
Not Using Bulk Operations
Indexing documents individually creates unnecessary network overhead and reduces throughput. Use bulk operations when indexing, updating, or deleting multiple documents to significantly improve performance.
Using Wildcard Queries Inefficiently
Wildcard queries, especially with leading wildcards (e.g., *phone
), are very inefficient as they require scanning all values in the index. Use n-grams or edge n-grams for prefix/suffix matching instead.
Not Using Pagination Properly
Using from
and size
for deep pagination (e.g., beyond 10,000 results) can cause performance issues and memory pressure. Use search_after
for deep pagination, or consider using the Scroll API for processing large result sets.
Using Inappropriate Shard Counts
Using too many shards can lead to the “small shard problem” with excessive overhead, while too few shards can limit scalability and cause performance issues with large datasets. Choose shard counts based on your data size and expected growth.
Not Using Index Aliases
Not using index aliases makes it difficult to reindex data or change mappings without downtime. Use aliases to decouple your application from the actual index names, allowing for zero-downtime reindexing and mapping changes.
Not Using Field Data Types Correctly
Using incorrect field data types can lead to unexpected search results and performance issues. Use text
for full-text search, keyword
for exact matching and aggregations, and appropriate numeric types for numeric fields.
Not Managing Refresh Intervals
Not managing refresh intervals appropriately can impact both indexing throughput and search latency. Adjust refresh intervals based on your use case, with longer intervals for write-heavy workloads and shorter intervals for search-heavy workloads.
Not Using Index Lifecycle Management
Manually managing time-based indices is error-prone and time-consuming. Use Index Lifecycle Management (ILM) to automate the management of indices through their lifecycle, from creation to deletion.
Not Monitoring Cluster Health
Not monitoring cluster health can lead to undetected issues and potential data loss. Implement comprehensive monitoring for your Elasticsearch cluster, including cluster health, node stats, and shard allocation.