Search performance tuning in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Elasticsearch query?

Given the following Elasticsearch query, what will be the total number of hits returned?

Elasticsearch

{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "python" } },
        { "range": { "publish_year": { "gte": 2015 } } }
      ]
    }
  },
  "size": 5
}

A Returns 5 documents but ignores the 'range' filter

B Returns all documents matching 'python' in title regardless of publish_year

C Returns 0 documents because 'range' filter is invalid

D Returns the top 5 documents matching 'python' in title published from 2015 onwards

💡 Hint

Check how the bool query combines must clauses and the size parameter limits results.

The bool query with must clauses requires both conditions to be true. The size limits results to 5 documents matching both conditions.

🧠 Conceptual

intermediate

Which setting improves search speed by reducing scoring calculations?

In Elasticsearch, which setting can you use to improve search performance by skipping scoring calculations when relevance is not important?

A "track_total_hits": false

B "track_scores": false

C "terminate_after": 10

D "explain": true

💡 Hint

Think about early termination when you only need a small number of matching documents, not full relevance ranking.

Setting "track_scores": false disables scoring calculations, improving performance when relevance scoring is not needed.

🔧 Debug

advanced

Why does this Elasticsearch query cause slow performance?

Consider this query: { "query": { "wildcard": { "username": "*admin*" } } } Why might this query be slow on large datasets?

A Wildcard queries with leading * cause full index scans, slowing performance

B The field 'username' is not indexed, causing errors

C The wildcard pattern is too short and matches too few documents

D The query syntax is invalid and causes errors

💡 Hint

Leading wildcards are known to be expensive in search engines.

Wildcard queries starting with * require scanning many terms, causing slow queries on large indexes.

📝 Syntax

advanced

Which option correctly limits search results to documents with a specific field value and sorts by date descending?

Choose the correct Elasticsearch query syntax to filter documents where "status" is "active" and sort results by "created_date" descending.

A

{
  "query": { "term": { "status": "active" } },
  "sort": [{ "created_date": { "order": "desc" } }]
}

B

{
  "query": { "match": { "status": "active" } },
  "sort": { "created_date": "desc" }
}

C

{
  "filter": { "term": { "status": "active" } },
  "sort": [{ "created_date": "desc" }]
}

D

{
  "query": { "term": { "status": "active" } },
  "sort": [{ "created_date": { "order": "asc" } }]
}

💡 Hint

Check the correct use of term query and sort syntax with order key.

Option A uses a term query to filter exact matches and sorts by created_date descending correctly.

🚀 Application

expert

How to optimize a search for a large dataset with frequent updates?

You have an Elasticsearch index with millions of documents that update frequently. You want to optimize search performance while keeping data fresh. Which approach is best?

A Increase the number of shards to 100 to distribute load evenly

B Disable refresh interval completely to speed up indexing and search

C Use index aliases with write and read indices, and refresh the write index frequently while searching the read index

D Use wildcard queries with leading * to match updated documents quickly

💡 Hint

Think about separating indexing and searching to improve performance and freshness.

Using index aliases with separate write and read indices allows frequent refreshes on the write index and stable searching on the read index, balancing freshness and performance.

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