Top Elasticsearch Interview Questions and Answers for 2023

Elasticsearch is a popular open-source search and analytics engine that is based on Apache Lucene. It is used for full-text search, log analytics, real-time application monitoring, and more. As Elasticsearch continues to grow in popularity, companies are looking for developers and admins with Elasticsearch skills. In this article, we’ll go over some of the most frequently asked Elasticsearch interview questions and example answers to help you prepare.

Common Elasticsearch Interview Questions

Here are some of the most common Elasticsearch interview questions that you may encounter

What is Elasticsearch and how does it work?

Elasticsearch is a search and analytics engine that is built on top of Apache Lucene. It provides a distributed, multitenant capable full-text search engine with an HTTP web interface and schema-free JSON documents.

Elasticsearch uses indexes to store related documents together for faster retrieval. Within an index, JSON documents contain fields and metadata. Elasticsearch analyzes and indexes the documents so they can be quickly searched. When a search request comes in, Elasticsearch looks at the index and provides fast results.

What are the key components of the ELK stack?

The ELK stack consists of three main components

• Elasticsearch – The search and analytics engine It indexes and stores data and enables fast search on that data

• Logstash – The data processing pipeline. It collects data from various sources, transforms it, and sends it to Elasticsearch.

• Kibana – The visualization layer. It provides UI to visualize data indexed in Elasticsearch.

Together, these three components enable collecting, storing, searching, analyzing, and visualizing data in an effective way.

What is a shard in Elasticsearch?

A shard is a single Lucene instance within an index. When you create an index in Elasticsearch, you specify the number of shards to split the index into. Each shard is a fully functional and independent index that can be hosted on any node in the cluster.

Sharding enables horizontal scaling and speeds up distributed search requests and operations across nodes. By default, each index in Elasticsearch is allocated 5 primary shards and 1 replica shard.

What is a node in Elasticsearch?

A node refers to a single server that is part of your Elasticsearch cluster. Your Elasticsearch cluster can contain multiple nodes, with each node storing one or more shards of your indexes.

A node can perform three main roles:

• Master node – The master node is responsible for lightweight cluster-wide actions like creating/deleting an index, tracking which nodes are part of the cluster, and deciding which shards to allocate to which nodes.

• Data node – Data nodes hold the shards that contain the documents you have indexed. Data-related operations like CRUD, search, and aggregations are handled by data nodes.

• Ingest node – Ingest nodes can pre-process documents before indexing. They can transform and enrich documents with additional data.

How does Elasticsearch handle faults and failures?

Elasticsearch has built-in resilience to failures through features like shard replication and automatic failover.

Each index’s primary shards can have one or more replicas, which are essentially copies of the shards on other nodes. In case a node holding a primary shard fails, Elasticsearch promotes the replica shard to be primary so queries can continue being served.

Elasticsearch also detects node failures and automatically reassigns shards held by the failed node to other available nodes. This helps prevent data loss and ensures continuity.

What are the different types of APIs supported by Elasticsearch?

Elasticsearch provides RESTful APIs using JSON over HTTP to perform operations. The main APIs include:

• Index APIs to manage indexes like create, delete, open, close etc.

• Document APIs to operate on documents like index, update, delete, bulk docs etc.

• Search APIs to search on indexed data with support for full-text, aggregations etc.

• Cluster APIs to monitor health, stats, pending tasks etc. about the cluster.

• Cat APIs to help monitor cluster, node, indices etc. in a readable format.

• Management APIs for snapshot/restore, index management and index aliases.

What is mapping in Elasticsearch?

Mapping defines how a document and its fields are stored and indexed. We can specify mapping for a field like its data type, format, and analyzer settings.

For example, mapping for a ‘content’ field could define it as a text field that needs to be analyzed a certain way. Mapping allows customizing how documents and their fields flow through Elasticsearch from indexing to searching.

How is Elasticsearch schema-free?

Elasticsearch is considered schema-free because documents with varying fields and structure can coexist within the same index. Unlike a traditional relational database, we don’t need to define the schema upfront for an Elasticsearch index.

When a new document is indexed, if its field is not mapped, Elasticsearch will infer and dynamically create mappings for it. We can still define mappings for consistency, but any new fields added will automatically be mapped and indexed.

How can you secure an Elasticsearch cluster?

Some ways to help secure an Elasticsearch cluster include:

• Enabling TLS for node-to-node and external communication

• Setting up proper firewall rules restricting network access

• Enabling authentication and role-based access controls

• Encrypting node-to-node traffic

• Using security plugins like Search Guard that provide authentication, field/document level security, and more

• Disabling dynamic scripting to prevent remote code execution

• Setting up proper file and directory permissions

• Using tools like ElastAlert to detect unusual traffic patterns

What are the advantages of using Elasticsearch?

Some key advantages of using Elasticsearch include:

• Fast and scalable full-text search capabilities out of the box.

• Powerful analytics through aggregations and querying.

• Schema-free implementation that handles structured, unstructured, and time series data.

• horizontally scalable to hundreds of nodes.

• High availability and fault tolerance through features like shard replication.

• Flexible deployment using containers like Docker and Kubernetes.

• Rich ecosystem and support for integrations.

What are typical use cases for Elasticsearch?

Some typical use cases for Elasticsearch include:

• Search – Providing full-text search and autocomplete/suggestions for applications like ecommerce, blogs, wikis etc.

• Logging and log analytics – Collecting, transforming, analyzing, visualizing application and server logs at scale.

• Application monitoring – Tracking key metrics about applications for performance monitoring and troubleshooting.

• Security analytics – Analyzing security logs and events to detect threats and anomalies.

• Business analytics – Powering real-time reports, dashboards, and insights for business intelligence use cases.

• Geospatial data analysis – Enabling location-based search, analytics, and visualizations leveraging geospatial capabilities.

Advanced Elasticsearch Interview Questions

Here are some more advanced Elasticsearch interview questions that you may come across:

How does Elasticsearch handle relevancy and ranking search results?

Elasticsearch uses algorithms like BM25 and TF/IDF to score documents based on the search query and fields being matched. Beyond that, it allows influencing relevance through features like boosting, functions scoring, and controlling precision/recall tradeoff.

Promoting exact matches over partial matches, freshness, user preferences etc. can improve relevance. Ranking also considers distributed scoring and shard-level scores.

How can you tune the performance of an Elasticsearch cluster?

Some ways to tune Elasticsearch performance include:

• Right sizing nodes for CPU, memory and storage needs.

• Using faster storage options like SSDs.

• Optimizing heap sizes and JVM settings.

• Enabling index compression to optimize storage.

• Tuning refresh intervals, replicas, number of shards etc.

• Using faster analyzers like keyword or whitelist based.

• Improving indexing performance with bulk APIs and pipelines.

• Caching hot documents and using doc values.

• Scaling horizontally by adding more data nodes.

What are the different ways to index data in Elasticsearch?

Some ways to index data in Elasticsearch include:

• Using the index APIs to index individual documents directly.

• Batch indexing a group of documents together using the bulk API.

• Building a Logstash pipeline to ingest data from other sources like databases into Elasticsearch.

• Using a data stream to store append-only time series data.

• Building a custom application to extract, transform and index data.

• Using Kibana to manually build and upload a JSON file containing documents.

How can you monitor performance in an Elasticsearch cluster?

Elasticsearch provides many APIs and tools to monitor performance like:

• _cat APIs can provide quick insights into nodes, tasks, indices, shards etc.

• _cluster/stats and _nodes/stats APIs gives detailed metrics.

• Monitoring tools like Kibana, Grafana, Prometheus etc. can visualize metrics.

• Enabling slow log capture for slow queries.

• Tracing requests helps understand latency issues.

• Tracking JVM stats around heap usage, garbage collection etc.

• Monitoring index stats like segments counts, refresh times etc.

• Keeping an eye on pipeline processor stats for ingest nodes.