Cache management (query, request, field data) in Elasticsearch - Step-by-Step Execution

Concept Flow - Cache management (query, request, field data)

Receive Query Request

↓

Check Query Cache

↓

Return Cached

↓

Return Cached

↓

Return Cached

↓

Return Data

When a query request arrives, Elasticsearch checks caches in order: query cache, request cache, then field data cache. If a cache hit occurs, it returns cached data; otherwise, it loads data and caches it.

Execution Sample

Elasticsearch

GET /my_index/_search
{
  "query": { "match": { "field": "value" } }
}

This query searches 'my_index' for documents where 'field' matches 'value'. Elasticsearch checks caches before executing.

Execution Table

Step	Cache Checked	Cache Hit?	Action Taken	Result
1	Query Cache	No	Check next cache	Continue to Request Cache
2	Request Cache	No	Check next cache	Continue to Field Data Cache
3	Field Data Cache	No	Load data and cache	Data loaded and cached
4	Return Data	N/A	Return loaded data	Query result returned
5	Query Cache	Yes	Return cached query result	Cached query result returned

💡 Execution stops when cached data is found or data is loaded and returned.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
query_cache	empty	miss	miss	miss	updated with query result
request_cache	empty	N/A	miss	miss	updated with request data
field_data_cache	empty	N/A	N/A	miss	updated with field data
response	none	none	none	loaded data	cached or loaded data

Key Moments - 3 Insights

Why does Elasticsearch check multiple caches instead of just one?

Because each cache stores different data types (query results, request results, field data), Elasticsearch checks them in order to find the fastest available data, as shown in steps 1-3 in the execution_table.

What happens if all caches miss?

Elasticsearch loads the data fresh from disk or memory, then updates the caches with this data, as shown in step 3 and 4 of the execution_table.

How does a cache hit improve performance?

A cache hit returns precomputed data immediately without re-executing the query or loading data again, as seen in step 5 where the query cache hit returns data quickly.

Visual Quiz - 3 Questions

Look at the execution_table, at which step does Elasticsearch load data and update caches?

A Step 1

B Step 5

C Step 3

D Step 2

💡 Hint

Refer to the 'Action Taken' column in step 3 where data is loaded and cached.

According to variable_tracker, what is the state of 'query_cache' after step 3?

A miss

B updated with query result

C empty

D N/A

💡 Hint

Check the 'query_cache' row under 'After Step 3' column.

If the request cache had a hit at step 2, what would happen next?

A Elasticsearch would load data fresh

B Elasticsearch would return cached request data immediately

C Elasticsearch would check the field data cache next

D Elasticsearch would ignore caches and execute query

💡 Hint

Look at step 2 in execution_table where a cache hit returns cached data.

Concept Snapshot

Cache management in Elasticsearch:
- Query cache stores full query results.
- Request cache stores results of filter and aggregation requests.
- Field data cache stores field values for sorting and aggregations.
- Elasticsearch checks caches in order: query, request, field data.
- Cache hits return data immediately; misses load and cache data.

Full Transcript

When Elasticsearch receives a search query, it first checks the query cache to see if the exact query result is stored. If not found, it checks the request cache, which stores results of filter and aggregation requests. If still not found, it checks the field data cache, which holds field values used for sorting and aggregations. If all caches miss, Elasticsearch loads the data fresh from disk or memory, then updates the caches with this data. This process speeds up repeated queries by returning cached data quickly. The execution table shows each step of cache checking and the actions taken. The variable tracker shows how cache states change after each step. Understanding this flow helps optimize Elasticsearch performance by leveraging caches effectively.

Practice

1. What is the primary purpose of cache management in Elasticsearch?

easy

A. To store recent search data and speed up query responses

B. To permanently save all search results for future use

C. To delete all data from the Elasticsearch index

D. To increase the size of the Elasticsearch cluster automatically

Solution

1. Step 1: Understand what cache does in Elasticsearch

   Cache temporarily stores recent search data to avoid repeating expensive operations.

2. Step 2: Identify the main benefit of caching

   By storing recent data, Elasticsearch can respond faster to repeated queries.

3. Final Answer:

   To store recent search data and speed up query responses -> Option A

4. Quick Check:

   Cache speeds up queries = A [OK]

Hint: Cache is for speed, not permanent storage [OK]

Common Mistakes:

• Thinking cache saves data permanently
• Confusing cache with index storage
• Assuming cache increases cluster size

2. Which of the following is the correct syntax to clear the query cache for an index named products using Elasticsearch REST API?

easy

A. POST /products/_cache/clear?query=true

B. POST /products/_cache/clear/query

C. POST /products/_cache/clear/fielddata

D. POST /products/_clear_cache/query

Solution

1. Step 1: Recall the correct REST API endpoint for clearing cache

   Elasticsearch uses _cache/clear with query parameters to specify cache types.

2. Step 2: Identify the correct parameter for query cache

   The query cache is cleared by adding ?query=true to the endpoint.

3. Final Answer:

   POST /products/_cache/clear?query=true -> Option A

4. Quick Check:

   Use query=true parameter to clear query cache [OK]

Hint: Use query=true parameter to clear query cache [OK]

Common Mistakes:

• Using wrong endpoint like _clear_cache
• Confusing fielddata cache with query cache
• Missing query parameter in URL

3. Given this Elasticsearch request to clear caches:

POST /myindex/_cache/clear
{
  "fielddata": true,
  "query": true
}

What caches will be cleared?

medium

A. Only the query cache

B. Only the fielddata cache

C. Request cache only

D. Both query and fielddata caches

Solution

1. Step 1: Analyze the JSON body parameters

   The request sets both fielddata and query to true, indicating both caches should be cleared.

2. Step 2: Understand cache clearing behavior

   When multiple cache types are true, Elasticsearch clears all specified caches.

3. Final Answer:

   Both query and fielddata caches -> Option D

4. Quick Check:

   fielddata=true + query=true clears both caches [OK]

Hint: True flags clear all specified caches together [OK]

Common Mistakes:

• Assuming only one cache clears at a time
• Confusing request cache with fielddata cache
• Ignoring JSON body parameters

4. You run this command to clear the request cache:

POST /logs/_cache/clear
{
  "request": true
}

But the cache is not cleared. What is the likely problem?

medium

A. The index name logs is invalid for cache clearing

B. The syntax is incorrect; request cache cannot be cleared this way

C. Request cache is disabled by default and must be enabled first

D. You must specify fielddata as true to clear request cache

Solution

1. Step 1: Understand request cache behavior

   Request cache is off by default and must be enabled in index settings to be used and cleared.

2. Step 2: Identify why clearing fails

   If request cache is disabled, clearing it has no effect, so the command appears to do nothing.

3. Final Answer:

   Request cache is disabled by default and must be enabled first -> Option C

4. Quick Check:

   Request cache off by default = no clearing effect [OK]

Hint: Enable request cache before clearing it [OK]

Common Mistakes:

• Assuming request cache is always enabled
• Using wrong syntax to clear caches
• Thinking fielddata cache affects request cache

5. You want to optimize Elasticsearch performance by managing caches. Which strategy correctly balances cache clearing and system stability?

hard

A. Clear all caches frequently to free memory, even during heavy query load

B. Clear query and request caches during low traffic, and monitor fielddata cache size

C. Never clear caches to keep all data in memory permanently

D. Disable all caches to avoid stale data and improve stability

Solution

1. Step 1: Understand cache clearing impact

   Clearing caches frees memory but can slow queries if done too often or during heavy load.

2. Step 2: Identify best practice for cache management

   Clearing query and request caches during low traffic avoids performance hits; monitoring fielddata cache prevents memory issues.

3. Final Answer:

   Clear query and request caches during low traffic, and monitor fielddata cache size -> Option B

4. Quick Check:

   Clear caches smartly during low load + monitor fielddata [OK]

Hint: Clear caches during low load, monitor fielddata size [OK]

Common Mistakes:

• Clearing caches too often during heavy load
• Disabling caches completely
• Ignoring fielddata cache memory use