Search performance tuning in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of using filters instead of queries in Elasticsearch for search performance?

Filters are faster because they cache results and do not calculate relevance scores, making repeated searches quicker.

intermediate

How does the _source field affect search performance in Elasticsearch?

Disabling or limiting the _source field reduces the amount of data retrieved, improving search speed and reducing network load.

intermediate

What is the benefit of using doc_values for fields in Elasticsearch?

doc_values store field values on disk in a columnar format, making sorting and aggregations faster and more memory efficient.

advanced

Why should you avoid using scripted fields in search queries for performance tuning?

Scripted fields run custom code at query time, which is slower and uses more CPU, so avoiding them improves search speed.

intermediate

How does setting index.refresh_interval affect search performance?

Increasing index.refresh_interval reduces how often Elasticsearch refreshes the index, improving indexing speed but delaying search visibility of new data.

Which Elasticsearch feature caches filter results to speed up repeated searches?

A Aggregations

B Filters

C Scripted fields

D Queries

Filters cache their results, making repeated searches faster by avoiding recalculations.

What does disabling the _source field do?

A Improves search speed by reducing data retrieval

B Increases relevance scoring

C Enables scripted fields

D Improves indexing speed only

Disabling _source reduces the data sent back, improving search speed.

Why are doc_values important for sorting and aggregations?

A They increase indexing speed

B They cache query results

C They disable scoring

D They store data in a columnar format on disk

doc_values store field data efficiently for fast sorting and aggregation.

What is a downside of using scripted fields in queries?

A They slow down queries due to runtime code execution

B They disable caching

C They reduce index size

D They improve search speed

Scripted fields run code during queries, which uses more CPU and slows performance.

Increasing index.refresh_interval will:

A Disable caching

B Make searches faster immediately

C Improve indexing speed but delay new data visibility in searches

D Reduce index size

A longer refresh interval means fewer refreshes, speeding indexing but delaying search updates.

Explain how caching filters improves Elasticsearch search performance.

Think about how remembering answers helps you answer faster next time.

Describe the trade-off involved when increasing the index.refresh_interval setting.

Consider how often you update a notice board affects how fresh the info is.

Practice

1. Which of the following is a common way to improve search performance in Elasticsearch?

easy

A. Limit the number of results returned using size parameter

B. Increase the number of shards without limit

C. Disable caching completely

D. Use wildcard queries on all fields

Solution

1. Step 1: Understand result limiting

   Limiting results with size reduces data processed and returned, speeding up queries.

2. Step 2: Evaluate other options

   Increasing shards without limit can hurt performance, disabling cache reduces speed, and wildcard queries are slow.

3. Final Answer:

   Limit the number of results returned using size parameter -> Option A

4. Quick Check:

   Limiting results = faster search [OK]

Hint: Use size to limit results for faster queries [OK]

Common Mistakes:

• Thinking more shards always improve speed
• Ignoring caching benefits
• Using wildcard queries on all fields

2. Which Elasticsearch query syntax correctly limits the returned fields to only title and author?

easy

A. {"return_fields": ["title", "author"], "query": {"match_all": {}}}

B. {"fields": ["title", "author"], "query": {"match_all": {}}}

C. {"select": ["title", "author"], "query": {"match_all": {}}}

D. {"_source": ["title", "author"], "query": {"match_all": {}}}

Solution

1. Step 1: Identify correct field limiting syntax

   Elasticsearch uses _source to specify which fields to return.

2. Step 2: Check other options

   fields, select, and return_fields are not valid for limiting returned fields in this context.

3. Final Answer:

   {"_source": ["title", "author"], "query": {"match_all": {}}} -> Option D

4. Quick Check:

   Use _source to limit fields [OK]

Hint: Use _source to specify returned fields [OK]

Common Mistakes:

• Using fields instead of _source
• Trying SQL-like select syntax
• Using unsupported keys like return_fields

3. Given this Elasticsearch query, what will be the effect of adding "timeout": "2s"?

{
  "query": {"match": {"content": "fast search"}},
  "timeout": "2s"
}

medium

A. The query will fail if it takes longer than 2 seconds

B. The query will cache results for 2 seconds

C. The query will return partial results after 2 seconds

D. The query will wait 2 seconds before starting

Solution

1. Step 1: Understand timeout behavior

   Elasticsearch's timeout stops the query after the specified time and returns partial results if available.

2. Step 2: Evaluate other options

   It does not fail immediately, does not delay start, and does not control caching.

3. Final Answer:

   The query will return partial results after 2 seconds -> Option C

4. Quick Check:

   timeout returns partial results [OK]

Hint: Timeout returns partial results if query is slow [OK]

Common Mistakes:

• Assuming timeout causes query failure
• Thinking timeout delays query start
• Confusing timeout with caching duration

4. You have this query to limit results and fields:

{
  "size": 10,
  "query": {
    "_source": ["title", "date"],
    "match_all": {}
  }
}

But the query returns all fields. What is the likely mistake?

medium

A. Using size instead of limit

B. Using _source inside the query body instead of top-level

C. Missing fields parameter to limit fields

D. The match_all query ignores field limits

Solution

1. Step 1: Check placement of _source

   _source must be at the top level of the query JSON, not inside query.

2. Step 2: Review other options

   fields is deprecated for this purpose, size is correct, and match_all does not ignore field limits.

3. Final Answer:

   Using _source inside the query body instead of top-level -> Option B

4. Quick Check:

   _source must be top-level [OK]

Hint: Place _source at top level, not inside query [OK]

Common Mistakes:

• Putting _source inside query
• Confusing size with limit
• Assuming match_all ignores field filtering

5. You want to optimize a search that returns many documents but only needs the id and summary fields, and must respond within 1 second. Which combination of settings best improves performance?

hard

A. Set size to a low number, use _source to limit fields, and add timeout of 1s

B. Set size high, disable _source, and remove timeout

C. Use wildcard queries on all fields and set timeout to 5s

D. Increase shards count and use fields to limit fields

Solution

1. Step 1: Limit results and fields

   Setting size low reduces returned documents; _source limits fields to needed ones.

2. Step 2: Use timeout to keep response fast

   Adding timeout of 1 second ensures query won't hang and keeps system responsive.

3. Step 3: Evaluate other options

   High size and disabling _source increase load; wildcard queries are slow; increasing shards without need can hurt performance.

4. Final Answer:

   Set size to a low number, use _source to limit fields, and add timeout of 1s -> Option A

5. Quick Check:

   Limit size + fields + timeout = best performance [OK]

Hint: Limit size, fields, and add timeout for fast, efficient search [OK]

Common Mistakes:

• Setting size too high
• Disabling field filtering
• Ignoring timeout setting
• Increasing shards unnecessarily