Caching and Concurrency
Understand Elasticsearch's caching mechanisms and how to manage concurrency to handle high request volumes and improve query response times.
Caching and Concurrency is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Elasticsearch & Full Text Search Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Boost Performance with Caching
Welcome to Caching and Concurrency! In this lesson, we'll explore how Elasticsearch uses caching to speed up searches and how it manages many requests at once.
Caching is like remembering past answers. If you ask the same question repeatedly, it's faster to recall the answer than to figure it out every time.
Why Caching is Crucial
For search engines, performance is key. Without caching, every query, even identical ones, would require Elasticsearch to re-read data from disk and re-process it.
This leads to higher CPU usage, increased I/O operations, and slower response times. Caching helps reduce this overhead significantly.
The Node Query Cache
Elasticsearch uses several caches. One important one is the Node Query Cache. This cache stores the results of frequently used filter queries.
It operates at the node level and is great for speeding up queries that use common filters, like "status": "active", without re-evaluating them.
GET /my_index/_search
{
"query": {
"bool": {
"filter": {
"term": {
"category.keyword": "electronics"
}
}
}
}
}How Node Query Cache Works
The Node Query Cache stores the *bitsets* representing which documents match a filter. When a filter is used again, Elasticsearch can quickly retrieve this bitset instead of scanning all documents.
It's optimized for queries that are small, frequently run, and don't involve complex aggregations or full-text analysis.
The Request Cache
Another vital cache is the Request Cache. This cache stores the *entire JSON response* of a search request for a specific shard.
It's useful for queries that are identical, including their aggregations, and are run often. It offers a significant speed boost by returning the pre-computed response.
GET /my_index/_search?request_cache=true
{
"size": 0,
"aggs": {
"categories": {
"terms": {
"field": "category.keyword"
}
}
}
}Doc Values: Modern Field Data
Historically, Elasticsearch used a 'Field Data Cache' for sorting and aggregations on text fields. This could consume a lot of memory.
Today, Elasticsearch uses Doc Values by default for numeric, boolean, date, IP, and keyword fields. Doc Values are stored on disk in a column-oriented fashion, making them very efficient for aggregations and sorting without heavy memory use.
PUT /products
{
"mappings": {
"properties": {
"price": {
"type": "float"
},
"status": {
"type": "keyword"
}
}
}
}Cache Invalidation
Caches are great, but they must be up-to-date. When you index, update, or delete a document in an index, Elasticsearch automatically invalidates (clears) the relevant cached entries for that shard.
This ensures that new searches always reflect the latest data, preventing stale results from being served.
Concurrency: Handling Many Requests
Beyond caching, Elasticsearch needs to handle many users querying and indexing data simultaneously. This is called concurrency.
Elasticsearch achieves concurrency by using multiple threads and thread pools, allowing it to process several operations at the same time without waiting for each one to finish sequentially.
Elasticsearch Thread Pools
Elasticsearch organizes tasks using different thread pools. Each pool handles a specific type of operation:
- Search pool: For executing search queries.
- Index pool: For indexing and updating documents.
- Bulk pool: For handling bulk indexing requests.
These pools prevent one slow operation from blocking others.
GET /_cat/thread_pool?vQueues and Rejection
When a thread pool is busy, incoming requests are placed into a queue. If the queue becomes full, Elasticsearch will start rejecting new requests for that operation type.
Rejected requests result in an error (e.g., HTTP 429 Too Many Requests). This mechanism is crucial for preventing the cluster from becoming overloaded and unstable.
Cache & Concurrency Check
Test your understanding of caching and concurrency in Elasticsearch.
Recap: Caching & Concurrency
In this lesson, we explored how Elasticsearch optimizes performance through caching and concurrency:
- Caching: The Node Query Cache and Request Cache store query results to avoid re-computation.
- Doc Values: An efficient, disk-based structure for aggregations and sorting.
- Concurrency: Elasticsearch uses thread pools to manage many simultaneous requests for search, indexing, and bulk operations.
- Queues: Requests are queued when busy, with rejection as a safeguard against overload.
Understanding these mechanisms helps you build faster and more resilient search applications!
Frequently asked questions
Is the “Caching and Concurrency” lesson free?
Yes — the full text of “Caching and Concurrency” is free to read here on the web, and the Elasticsearch & Full Text Search Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Elasticsearch & Full Text Search Systems course, upgrade to CoddyKit PRO.
What will I learn in “Caching and Concurrency”?
Understand Elasticsearch's caching mechanisms and how to manage concurrency to handle high request volumes and improve query response times. You practise Elasticsearch & Full Text Search Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Elasticsearch & Full Text Search Systems?
No prior experience is required. Elasticsearch & Full Text Search Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Caching and Concurrency” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Elasticsearch & Full Text Search Systems lesson?
Yes. Every Elasticsearch & Full Text Search Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.