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.