30 Elasticsearch Interview Questions and Answers

Elasticsearch is a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. It is commonly used for log and event data analysis, full-text search, security intelligence, and business analytics.

Elasticsearch stores data as JSON documents within an index. Each document is a collection of fields, and indexes are logical namespaces that group similar documents together. Data is distributed across shards and replicas for scalability and reliability.

An index in Elasticsearch is similar to a database in relational systems, grouping related documents. Unlike a table, an index can store documents with varying structures, as Elasticsearch is schema-less and flexible.

Shards are subdivisions of an index that allow Elasticsearch to distribute data across multiple nodes, improving performance and scalability. Replicas are copies of shards that provide redundancy and high availability.

Elasticsearch uses an inverted index to perform full-text searches efficiently. It tokenizes and analyzes text fields, creating a mapping from terms to document locations, enabling fast and relevant search results.

A node is a single server that stores data and participates in the cluster’s indexing and search capabilities. A cluster is a collection of nodes that work together to store data and provide distributed search and analytics.

Data can be ingested into Elasticsearch using REST APIs, Logstash, Beats, or custom applications. Data is typically sent as JSON documents via HTTP requests to the Elasticsearch cluster.

Kibana is a visualization tool that works with Elasticsearch. It allows users to explore, visualize, and analyze data stored in Elasticsearch through dashboards, charts, and graphs.

Elasticsearch uses mappings to define how documents and their fields are stored and indexed. Mappings specify data types and field properties, but Elasticsearch can also dynamically detect and assign types if not explicitly defined.

Common use cases include log and event data analysis, application performance monitoring, security analytics, e-commerce product search, and business intelligence dashboards.

A mapping in Elasticsearch defines how documents and their fields are stored and indexed, including data types and analyzers. Unlike rigid schemas in traditional databases, mappings are flexible and can be updated dynamically, allowing for schema evolution as new data is ingested.

Elasticsearch achieves high availability through the use of replicas, which are copies of primary shards distributed across different nodes. If a node fails, the cluster can promote a replica to a primary shard, ensuring data remains accessible and the cluster continues to function.

Index Lifecycle Management (ILM) automates the management of index lifecycles, such as rollover, deletion, and migration to different storage tiers. ILM helps optimize storage costs and performance by applying policies based on index age, size, or other criteria.

Analyzers process text fields during indexing and searching by breaking text into tokens and applying filters (like lowercasing or stemming). The choice of analyzer affects how queries match documents, influencing search relevance and accuracy.

Term-level queries (like term, terms, range) operate on exact values and are not analyzed, making them suitable for structured data. Full-text queries (like match, multi_match) analyze input text and are used for searching unstructured text fields.

When a document is updated or deleted, Elasticsearch marks the old version as deleted and writes a new version. Actual removal happens during segment merging, a background process that reclaims disk space and optimizes index performance.

A search template is a reusable query structure that can accept parameters at runtime. Templates are useful for dynamic queries where only certain values change, improving maintainability and reducing code duplication.

Query optimization involves using filters for exact matches, limiting the number of fields returned, paginating results, using appropriate analyzers, and leveraging caching. Monitoring query performance and analyzing slow logs also help identify bottlenecks.

Aggregations allow you to compute metrics, statistics, and summaries over your data, such as counts, averages, or histograms. For example, you can use aggregations to group log entries by status code and count occurrences for monitoring application health.

Best practices include enabling authentication and authorization (using X-Pack or Open Distro), encrypting data in transit with TLS, restricting network access, using firewalls, and regularly updating and patching the cluster to address vulnerabilities.

Elasticsearch uses an inverted index for full-text search, mapping terms to document locations, which enables fast text queries. For aggregations and analytics, it uses columnar data structures (doc values) that store field values in a column-oriented fashion, improving performance for sorting and aggregations. Understanding when each structure is used helps optimize queries for speed and resource usage.

Elasticsearch uses a primary-replica model with eventual consistency. By default, write operations are acknowledged once the primary shard processes them, and replicas are updated asynchronously. This can lead to temporary inconsistencies, but improves performance and availability. For critical data, you can adjust write consistency settings to require acknowledgment from replicas.

Reindexing is required when changing mappings, analyzers, or upgrading versions. It involves copying data from one index to another, which can be resource-intensive and may impact cluster performance. Best practices include reindexing during low-traffic periods, using the _reindex API, and monitoring cluster health throughout the process.

Elasticsearch can ingest large volumes of data via bulk APIs, Logstash, or Beats. To prevent overload, use bulk indexing with appropriate batch sizes, throttle ingestion rates, monitor node resources, and use index lifecycle management to manage old data. Scaling out the cluster and optimizing mappings also help handle high ingestion rates.

Elasticsearch's Query DSL is a JSON-based language designed for complex, nested, and full-text queries, offering fine-grained control over search behavior. SQL support in Elasticsearch is more limited, suitable for users familiar with relational queries and for simple aggregations. Use Query DSL for advanced search features and SQL for straightforward analytics or integration with BI tools.

Shard allocation issues can arise from disk space shortages, node failures, or misconfigured allocation settings. Troubleshooting involves checking cluster health, reviewing allocation explanations (_cluster/allocation/explain), ensuring sufficient resources, and adjusting allocation filters or disk watermarks. Rebalancing shards and adding nodes may be necessary for resolution.

Circuit breakers monitor memory usage for various operations (like field data, requests, or parent memory) and prevent actions that could cause out-of-memory errors. When a limit is reached, Elasticsearch rejects requests to maintain cluster stability. Tuning circuit breaker settings helps balance performance and reliability.

Exposing Elasticsearch to the public internet can lead to unauthorized access, data breaches, and denial-of-service attacks. To mitigate risks, always restrict access using firewalls, enable authentication and authorization, use TLS encryption, disable dangerous APIs, and regularly audit security settings.

Cluster state contains metadata about indices, mappings, and shard allocation, and is managed by the elected master node. Large cluster states (due to many indices or fields) can slow down updates, increase memory usage, and cause instability. Best practices include limiting the number of indices and fields, using index templates, and monitoring cluster state size.

Mapping explosions occur when too many unique fields or dynamic mappings are created, leading to large cluster states and degraded performance. Prevention strategies include disabling dynamic mapping where possible, using strict mappings, consolidating similar fields, and monitoring field counts. Regularly review and clean up unused indices to avoid mapping bloat.