Cache management (query, request, field data) in Elasticsearch - Deep Dive

Overview - Cache management (query, request, field data)

What is it?

Cache management in Elasticsearch means storing certain data temporarily so that future searches or requests can be answered faster. It involves saving query results, request information, and field data in memory. This helps reduce the time and resources needed to get the same information again. Caches are automatically managed but can also be tuned for better performance.

Why it matters

Without cache management, Elasticsearch would have to process every search or request from scratch, which would be slow and costly. This would make applications using Elasticsearch feel sluggish and less responsive. Good cache management speeds up data retrieval, reduces server load, and improves user experience by delivering results quickly.

Where it fits

Before learning cache management, you should understand basic Elasticsearch concepts like indexing, searching, and how queries work. After mastering cache management, you can explore advanced performance tuning, cluster scaling, and monitoring Elasticsearch clusters for health and efficiency.

Mental Model

Core Idea

Cache management in Elasticsearch temporarily stores query results, request data, and field data in memory to speed up repeated searches and reduce processing time.

Think of it like...

Imagine a library where popular books are kept on a special shelf near the entrance so visitors can grab them quickly instead of searching the whole library every time.

┌───────────────────────────────┐
│ Elasticsearch Cache Layers     │
├───────────────┬───────────────┤
│ Query Cache   │ Stores results│
│               │ of frequent   │
│               │ filter queries│
├───────────────┼───────────────┤
│ Request Cache │ Caches entire │
│               │ search results│
├───────────────┼───────────────┤
│ Field Data    │ Holds field   │
│ Cache         │ values in     │
│               │ memory for    │
│               │ fast sorting  │
└───────────────┴───────────────┘

Build-Up - 7 Steps

1

Foundation

What is Cache in Elasticsearch

Concept: Introduce the basic idea of cache as temporary storage to speed up repeated data access.

Cache is a place where Elasticsearch keeps data it has already processed so it can reuse it quickly. Instead of searching through all data every time, it remembers answers to recent or frequent queries. This saves time and computer power.

Result

You understand that cache helps Elasticsearch respond faster by reusing stored data.

Understanding cache as temporary memory helps you see why Elasticsearch can be fast even with large data.

2

Foundation

Types of Cache in Elasticsearch

Concept: Explain the three main cache types: query cache, request cache, and field data cache.

Elasticsearch uses different caches for different purposes: - Query Cache: Saves results of frequent filter queries. - Request Cache: Stores entire search responses. - Field Data Cache: Keeps field values in memory for sorting and aggregations. Each cache helps speed up specific parts of searching.

Result

You can identify which cache is used for what purpose in Elasticsearch.

Knowing the types of cache clarifies how Elasticsearch optimizes different search tasks.

3

Intermediate

How Query Cache Works

🤔Before reading on: do you think query cache stores every query result or only some? Commit to your answer.

Concept: Detail how query cache stores results of frequent queries and when it is used.

Query cache stores the results of queries that are run often and are cacheable. It only caches filters (parts of queries that don’t change the score) and not full queries with scoring. When a cached query runs again, Elasticsearch returns the stored result instead of searching again.

Result

Frequent filter queries run faster because Elasticsearch reuses cached results.

Understanding that query cache only stores filter results prevents confusion about why some queries are not cached.

4

Intermediate

Role of Request Cache

🤔Before reading on: do you think request cache stores partial or full search results? Commit to your answer.

Concept: Explain that request cache stores entire search responses for repeated identical requests.

Request cache saves the full response of a search request, including hits and aggregations. It is useful for dashboards or repeated identical searches. When the same request comes again, Elasticsearch returns the cached response quickly without re-executing the search.

Result

Repeated identical search requests return results faster using request cache.

Knowing request cache stores full responses helps optimize repeated dashboard queries.

5

Intermediate

Understanding Field Data Cache

Concept: Describe how field data cache holds field values in memory for sorting and aggregations.

Field data cache loads field values into memory so Elasticsearch can quickly sort or aggregate on those fields. This cache is built on demand and can consume a lot of memory if many fields or large datasets are involved. It is important to monitor and manage this cache to avoid memory issues.

Result

Sorting and aggregations on cached fields run faster due to in-memory data.

Recognizing the memory cost of field data cache helps prevent performance problems.

6

Advanced

Cache Eviction and Expiry

🤔Before reading on: do you think cache entries stay forever or get removed? Commit to your answer.

Concept: Explain how Elasticsearch removes old or unused cache entries to free memory.

Caches have limited size. Elasticsearch evicts (removes) old or least used entries when space is needed. For example, query cache entries expire after a time or when the index changes. This keeps cache fresh and prevents memory overload.

Result

Cache stays efficient by removing outdated data automatically.

Understanding eviction prevents confusion when cached results disappear unexpectedly.

7

Expert

Tuning Cache for Production Performance

🤔Before reading on: do you think more cache always means better performance? Commit to your answer.

Concept: Discuss how to configure cache sizes and policies to balance speed and memory use in real systems.

In production, you must tune cache settings carefully. Too small cache means frequent recomputing; too large cache wastes memory and can cause slowdowns. Elasticsearch allows configuring cache sizes and enabling/disabling caches per index or query. Monitoring cache hit rates and memory usage guides tuning decisions.

Result

Optimized cache settings improve search speed without risking memory problems.

Knowing that cache tuning is a balance helps avoid common performance pitfalls in Elasticsearch.

Under the Hood

Elasticsearch caches work by storing serialized data structures in memory or off-heap memory. Query cache stores bitsets representing matching documents for filters. Request cache stores full search responses as byte arrays. Field data cache loads field values into JVM heap memory as arrays for fast access. Cache lookups check if a request or query matches a stored entry, returning cached data if found. Eviction uses least-recently-used (LRU) or time-based policies to remove stale entries.

Why designed this way?

Caches were designed to reduce expensive disk reads and CPU work for repeated queries. Using bitsets for query cache is efficient for filters. Request cache stores full responses to speed up dashboards. Field data cache uses JVM heap for fast sorting but requires careful memory management. Alternatives like no caching would cause slow searches; caching balances speed and resource use.

┌───────────────┐
│ Search Request│
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Check Request │
│ Cache        │
└──────┬────────┘
       │ hit/no
       │
   ┌───▼────┐   no
   │ Check   │─────────┐
   │ Query   │         │
   │ Cache   │         │
   └───┬────┘         │
       │ hit/no        │
       │              │
   ┌───▼────┐         │
   │ Execute│         │
   │ Search │         │
   └───┬────┘         │
       │              │
       ▼              │
┌───────────────┐     │
│ Store Result  │◄────┘
│ in Caches     │
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does query cache store results for all queries or only some? Commit to your answer.

Common Belief: Query cache stores results for every query automatically.

Reality: Query cache only stores results for filter queries that are cacheable, not all queries.

Why it matters: Believing all queries are cached can lead to confusion when some queries run slowly despite caching.

Quick: Does request cache speed up all searches or only repeated identical ones? Commit to your answer.

Common Belief: Request cache speeds up every search request.

Reality: Request cache only speeds up repeated identical search requests, not unique or different ones.

Why it matters: Misunderstanding this can cause wasted effort trying to cache unique queries that won't benefit.

Quick: Does increasing cache size always improve performance? Commit to your answer.

Common Belief: Bigger cache size always means faster Elasticsearch performance.

Reality: Too large caches can cause memory pressure and slow down Elasticsearch due to garbage collection or swapping.

Why it matters: Ignoring this can cause severe performance degradation and crashes in production.

Quick: Is field data cache free of memory cost? Commit to your answer.

Common Belief: Field data cache is small and has no significant memory impact.

Reality: Field data cache can consume large amounts of JVM heap memory, risking out-of-memory errors if unmanaged.

Why it matters: Underestimating field data cache memory use can cause unexpected crashes and downtime.

Expert Zone

1

Query cache effectiveness depends heavily on query patterns and index updates; frequent index changes invalidate cache entries quickly.

2

Field data cache can be replaced by doc values in newer Elasticsearch versions, which store field data on disk to reduce heap usage.

3

Request cache is disabled by default for queries with aggregations because aggregations often change and caching them can be inefficient.

When NOT to use

Cache management is less effective for highly dynamic data or queries that rarely repeat. In such cases, relying on real-time search without caching or using specialized caching layers like Redis may be better.

Production Patterns

In production, teams monitor cache hit rates and memory usage using Elasticsearch monitoring tools. They tune cache sizes per index and disable query cache for write-heavy indices. Dashboards use request cache to speed up repeated visualizations. Field data cache is minimized by using doc values and careful field mapping.

Connections

Operating System Page Cache

Both cache frequently accessed data in memory to speed up access.

Understanding OS page cache helps grasp why Elasticsearch caching reduces disk reads and improves performance.

Web Browser Cache

Both store previous responses to avoid re-fetching data over the network.

Knowing browser cache behavior clarifies how request cache speeds up repeated searches by reusing full responses.

Human Memory Recall

Both rely on storing recent or frequent information to quickly recall it when needed.

Recognizing this similarity helps appreciate why caching improves speed by avoiding repeated work.

Common Pitfalls

#1 Expecting query cache to speed up all queries including scoring queries.

Wrong approach:

Using query cache for queries with scoring and complex functions expecting fast results.

Correct approach:

Use query cache only for filter queries without scoring to benefit from caching.

Root cause:

Misunderstanding that query cache only supports filter queries and not full scoring queries.

#2 Not monitoring field data cache memory usage leading to out-of-memory errors.

Wrong approach:

Ignoring field data cache size and letting it grow unchecked in production.

Correct approach:

Monitor and limit field data cache size; use doc values to reduce heap usage.

Root cause:

Lack of awareness about field data cache memory consumption and its impact.

#3 Enabling request cache for queries with frequent index updates causing stale results.

Wrong approach:

Turning on request cache for all queries regardless of index update frequency.

Correct approach:

Disable request cache for write-heavy indices or queries with changing data.

Root cause:

Not understanding that cache invalidation happens on index changes, making cached data stale.

Key Takeaways

Cache management in Elasticsearch speeds up repeated queries by storing results and field data temporarily in memory.

There are three main caches: query cache for filters, request cache for full responses, and field data cache for sorting and aggregations.

Caches have limits and eviction policies to keep memory use balanced and data fresh.

Effective cache tuning requires understanding query patterns, index update frequency, and memory constraints.

Misusing caches or ignoring their limits can cause slowdowns, stale data, or crashes in production.