Boost Elasticsearch Performance with Caching

Elasticsearch, known for its powerful full-text search and analytics capabilities, relies heavily on efficient data retrieval. Caching plays a pivotal role in enhancing Elasticsearch's performance by reducing the need to repeatedly compute or fetch data.

I have been working with Elasticsearch for a long time, and was in awe with how efficient it was in repeated queries. Looking up the implementation, I discovered a major part of it is due to it’s inherent caching, which is enough for a normal case, but you can unlock so much more of it’s power if you understand the nitty-gritty of it in action. In this blog, we'll explore the various caching mechanisms in Elasticsearch, how they work, and best practices for using them to optimize it for your use case!

Why Caching Matters in Elasticsearch

Before going into how it works, we must understand what caching provides. Caching is crucial in Elasticsearch for the following reasons:

Performance Improvement: Caching minimizes the computational overhead of frequently executed queries.
Reduced Latency: Cached data can be accessed much faster than querying the underlying data sources.
Resource Optimization: It lowers the strain on Elasticsearch nodes by avoiding redundant computations.

Types of Caching in Elasticsearch

Elasticsearch employs multiple caching mechanisms, each designed for specific scenarios:

Query Cache
- Purpose: Stores the results of queries.
- Scope: Operates at the shard level.
- When Used: Primarily for filter queries (e.g., term, range, match queries) that are frequently repeated.
- Configuration: Query caching is enabled by default but can be fine-tuned or disabled using query context settings.
- Relevant Commands:
  - To check query cache stats:
```
  GET /_nodes/stats/indices/query_cache
```
  - To clear the query cache:
```
  POST /_cache/clear
```
  - In queries, ensure that the filter context is used for better caching:
```
  {
    "query": {
      "bool": {
        "filter": [
          { "term": { "status": "active" } }
        ]
      }
    }
  }
```
Request Cache
- Purpose: Caches the entire response of a search request.
- Scope: Operates at the node level.
- When Used: Useful for aggregations and searches that yield identical results for repeated requests.
- Configuration: Controlled by the request_cache setting.
- Relevant Commands:
  - To enable request caching for a specific query:
```
  {
    "request_cache": true,
    "query": {
      "match": {
        "field": "value"
      }
    }
  }
```
  - To check request cache stats:
```
  GET /_nodes/stats/indices/request_cache
```
  - To clear the request cache:
```
  POST /_cache/clear
```
Field Data Cache
- Purpose: Caches field values for use in sorting, aggregations, and script queries.
- Scope: Operates in memory at the shard level.
- When Used: For fields that are frequently used in sorting or aggregations.
- Configuration: Size can be controlled using the indices.fielddata.cache.size setting.
- Relevant Commands:
  - To check field data cache stats:
```
  GET /_nodes/stats/indices/fielddata
```
  - To clear field data cache:
```
  POST /_cache/clear
```
  - To optimize for aggregations, ensure fields use doc_values (set by default for most numeric and keyword fields):
```
  {
    "mappings": {
      "properties": {
        "price": {
          "type": "double",
          "doc_values": true
        }
      }
    }
  }
```
Node Query Cache
- Purpose: Helps in caching query results across nodes.
- Scope: Node level.
- When Used: Automatically managed by Elasticsearch.
- Relevant Commands: While there is no direct management for node query cache, monitoring and clearing overall cache stats can help:
```
  GET /_nodes/stats
```

How Elasticsearch Handles Cache Eviction

Caching is inherently limited by the available resources. As the cache size grows, the optimizations from caching might be easily countered. So, we need to employ a suitable cache eviction logic. Elasticsearch employs various eviction policies to manage cache:

Least Recently Used (LRU): Removes the least recently used entries when the cache exceeds its size limit.
Manual Clearing: Administrators can clear caches manually using Elasticsearch APIs, such as:
```
 POST /_cache/clear
```

Monitoring Cache Performance

Elasticsearch provides several tools to monitor caching:

Elasticsearch APIs:

Query cache statistics:

  GET /_nodes/stats/indices/query_cache

Request cache statistics:

  GET /_nodes/stats/indices/request_cache

Field data cache statistics:
```
  GET /_nodes/stats/indices/fielddata
```

Elasticsearch Management Tools: Use tools like Kibana to visualize cache usage and performance metrics.

Conclusion

Caching is a powerful feature in Elasticsearch that, when used wisely, can significantly enhance search performance. By understanding the different types of caches and adhering to best practices, you can optimize your Elasticsearch clusters for faster queries and better resource utilization.

Remember, while caching can provide substantial performance benefits, it’s not a silver bullet. A well-designed data model and efficient queries are equally important for achieving optimal performance in Elasticsearch.

Caching in Elasticsearch

Unlock superpowers while querying your data!

Why Caching Matters in Elasticsearch

Types of Caching in Elasticsearch

How Elasticsearch Handles Cache Eviction

Monitoring Cache Performance

Conclusion