Introduction

Elasticsearch provides fast and scalable search capabilities, but improper indexing strategies, inefficient queries, and unbalanced cluster configurations can lead to slow performance and high resource utilization. Common pitfalls include overloading individual nodes due to poor shard distribution, using expensive wildcard searches, failing to optimize field mappings, and excessive full-text indexing. These issues become particularly problematic in high-scale logging systems, enterprise search applications, and real-time analytics environments where speed and efficiency are critical. This article explores advanced Elasticsearch troubleshooting techniques, query optimization strategies, and best practices.

Common Causes of Slow Queries and High Resource Usage in Elasticsearch

1. Inefficient Index Mapping and Poorly Defined Fields

Incorrect field mappings can cause unnecessary full-text indexing and slow down searches.

Problematic Scenario

# Default mapping causing excessive indexing
PUT my_index
{
  "mappings": {
    "properties": {
      "user_id": { "type": "text" },
      "timestamp": { "type": "text" }
    }
  }
}

Using `text` type for numeric or timestamp fields leads to unnecessary tokenization.

Solution: Use Appropriate Data Types

# Optimized field mapping
PUT my_index
{
  "mappings": {
    "properties": {
      "user_id": { "type": "keyword" },
      "timestamp": { "type": "date" }
    }
  }
}

Using `keyword` for IDs and `date` for timestamps improves query performance.

2. Poor Shard Allocation Leading to Unbalanced Load

Incorrect shard allocation can cause uneven indexing and slow queries.

Problematic Scenario

# Index with too many shards
PUT /my_index
{
  "settings": {
    "number_of_shards": 10,
    "number_of_replicas": 1
  }
}

Too many shards per node increases overhead and slows down queries.

Solution: Use an Appropriate Number of Shards

# Optimized shard allocation
PUT /my_index
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1
  }
}

Reducing shards ensures efficient resource utilization.

3. Expensive Queries Causing High CPU Usage

Using wildcard searches and deeply nested queries increases query time.

Problematic Scenario

# Slow wildcard query
GET my_index/_search
{
  "query": {
    "wildcard": {
      "username": "*smith*"
    }
  }
}

Wildcard queries are expensive and force a full index scan.

Solution: Use Prefix or `keyword` Searches

# Optimized keyword search
GET my_index/_search
{
  "query": {
    "match": {
      "username": "smith"
    }
  }
}

Using `match` or `prefix` queries improves search speed.

4. Unoptimized Bulk Indexing Slowing Down Data Ingestion

Failing to batch documents efficiently causes indexing bottlenecks.

Problematic Scenario

# Inefficient single-document indexing
POST my_index/_doc/
{
  "user": "john",
  "action": "login"
}

Indexing one document at a time causes performance degradation.

Solution: Use Bulk Indexing

# Optimized bulk indexing
POST _bulk
{
  "index": { "_index": "my_index" }
}
{"user": "john", "action": "login"}
{"index": { "_index": "my_index" }}
{"user": "jane", "action": "logout"}

Batching multiple documents reduces indexing overhead.

5. High Memory Usage Due to Improper Cache Configuration

Failing to configure caching properly increases memory consumption.

Problematic Scenario

# Default caching settings causing frequent cache evictions
GET _nodes/stats/indices/query_cache

Frequent cache misses lead to slow queries.

Solution: Tune Query Cache Size

# Increase query cache size
PUT /_cluster/settings
{
  "persistent": {
    "indices.queries.cache.size": "20%"
  }
}

Increasing query cache size improves performance for repeated queries.

Best Practices for Optimizing Elasticsearch Performance

1. Optimize Index Mapping

Use `keyword` for exact matches and `text` for full-text search.

2. Tune Shard Allocation

Balance the number of shards per node to avoid overload.

3. Avoid Expensive Queries

Use term queries instead of wildcards to reduce CPU usage.

4. Batch Indexing Operations

Use `_bulk` API to speed up document ingestion.

5. Configure Query Caching

Increase cache size for frequently accessed queries.

Conclusion

Elasticsearch clusters can suffer from slow query performance, high memory consumption, and inefficient indexing due to improper mappings, excessive shard counts, expensive queries, and unoptimized caching. By refining index settings, balancing shard allocation, avoiding wildcard searches, using bulk indexing, and tuning query cache configurations, developers can significantly improve Elasticsearch performance. Regular monitoring using `/_cat/nodes` and `/_cat/indices` APIs helps detect and resolve inefficiencies proactively.