Top ElasticSearch Interview Questions for Freshers and Experienced Developers
Prepare for ElasticSearch interviews with a realistic practice experience. Solve curated questions, explore concise explanations, and evaluate your performance instantly.
45 Questions
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1
What is a pipeline in ElasticSearch ingest?
medium
ingest pipeline
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Answer
Processes documents before indexing.
Key concept: Pre-processing via processors.
Example: add timestamp.
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2
How do you secure an ElasticSearch cluster?
medium
security auth
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Answer
Use TLS, authentication, role-based access.
Key concept: X-Pack security.
Restrict APIs.
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3
What causes split brain in ElasticSearch?
hard
cluster failure
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Answer
Multiple master nodes elected.
Key concept: Avoid via quorum settings.
Example: minimum master nodes.
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4
What is index lifecycle management (ILM)?
medium
ilm lifecycle
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Answer
Automates index aging, rollover, deletion.
Key concept: Data lifecycle control.
Example: hot-warm-cold phases.
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5
How do you debug slow queries in ElasticSearch?
hard
debugging performance
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Answer
Use slow logs, profile API.
Key concept: Identify bottlenecks.
Example: expensive aggregations.
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6
What is a nested field type?
medium
mapping nested
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Answer
Allows indexing arrays of objects.
Key concept: Maintains object relationships.
Example: user with multiple addresses.
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7
Difference between nested and object type?
hard
mapping nested
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Answer
Object flattens fields; nested keeps relationships.
Key concept: Nested avoids cross-object matching.
Important for accuracy.
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8
How does ElasticSearch handle versioning?
medium
versioning concurrency
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Answer
Uses internal version numbers.
Key concept: Optimistic concurrency control.
Prevents overwrite conflicts.
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9
What is optimistic concurrency control?
medium
concurrency update
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Answer
Prevents conflicting updates.
Key concept: Uses version checks.
Fails if version mismatch.
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10
How do you scale ElasticSearch horizontally?
easy
scaling cluster
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Answer
Add nodes and shards.
Key concept: Distributed architecture.
Rebalance data automatically.
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11
What is routing in ElasticSearch?
hard
routing sharding
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Answer
Controls which shard stores a document.
Key concept: Custom routing improves performance.
Example: userId routing.
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12
Explain the role of segment merging.
hard
segments indexing
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Answer
Combines smaller segments into larger ones.
Key concept: Improves search efficiency.
Triggered automatically.
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13
What is a translog?
hard
translog recovery
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Answer
Transaction log for durability.
Key concept: Helps recover data.
Written before commit.
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14
How do you handle large datasets efficiently?
medium
pagination search
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Answer
Use pagination, scroll API.
Key concept: Avoid deep pagination.
Example: search_after.
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15
What is search_after and when to use it?
hard
pagination search
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Answer
Efficient deep pagination method.
Key concept: Uses last sort values.
Better than from/size.
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16
What are aggregations in ElasticSearch?
medium
aggregation analytics
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Answer
Summarize data like SQL group by.
Key concept: Metrics and bucket aggregations.
Example: count per category.
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17
How do you optimize aggregations performance?
hard
aggregation performance
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Answer
Use keyword fields, doc_values.
Key concept: Avoid fielddata.
Reduce cardinality.
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18
What is a mapping explosion problem?
hard
mapping performance
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Answer
Too many fields in index.
Key concept: Impacts cluster state.
Avoid dynamic mapping abuse.
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19
How do you monitor ElasticSearch health?
easy
monitoring health
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Answer
Use cluster health API.
Key concept: green/yellow/red status.
Check shard allocation.
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20
What is snapshot and restore in ElasticSearch?
medium
backup snapshot
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Answer
Backup and restore data.
Key concept: Uses repository storage.
Example: S3 backup.
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21
How do you reduce index size in ElasticSearch?
hard
storage optimization
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Answer
Disable _source, use compression.
Key concept: Optimize mappings.
Remove unused fields.
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22
Explain the difference between match and term query.
medium
query search
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Answer
Match is analyzed; term is exact.
Key concept: Full-text vs exact match.
Use term for keyword fields.
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23
What is fuzzy search and how does it work?
medium
fuzzy search
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Answer
Finds approximate matches.
Key concept: Levenshtein distance.
Example: 'helo' matches 'hello'.
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24
How does ElasticSearch handle synonyms?
medium
analysis synonyms
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Answer
Via synonym token filters.
Key concept: Expand search terms.
Example: 'car' = 'automobile'.
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25
What is cluster rerouting?
hard
cluster routing
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Answer
Manually control shard allocation.
Key concept: Useful during failures.
Example: move shards.
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