Updated March 22, 2023
Elasticsearch Interview Questions and Answers
Elasticsearch is a search and analytics engine that enables data storage, searching, and analysis. It is a highly sought-after skill in IT professionals and comes with a lucrative salary. On average, Elasticsearch software engineers earn $106,208 annually, while senior engineers can make anywhere from $100,000 to $171,000, and top Elasticsearch data engineers can earn up to $207,000 per year.
Elasticsearch interview questions are designed to test a candidate’s understanding of Elasticsearch and ability to use it effectively to meet business requirements. Interview questions may cover topics such as data indexing, querying, scalability, high availability, and performance optimization. Preparing for these interviews with a good set of Elasticsearch interview questions can help candidates improve their knowledge and answer confidently.
Table of Contents
- Introduction
- Elasticsearch Interview Questions (Basic)
- Elasticsearch Interview Questions (Advanced)
- Final Thoughts
- Frequently Asked Questions (FAQs)
- Recommended Articles
Key Highlights
- Elasticsearch interview questions prepare candidates to face technical questions about Elasticsearch.
- Being a highly sought-after skill for IT professionals, the pay scale for Elasticsearch developers is excellent.
- It is an open-source search engine based on the Apache Lucene library used in various types of analysis.
- Elasticsearch interview questions include nodes, clusters, documents, and indexes.
Elasticsearch Interview Questions (Basic)
1. What is Elasticsearch? What are its key features?
Answer: Elasticsearch is a distributed, open-source search engine based on the Lucene library. Its key features include:
- Full-text search capabilities
- Real-time data processing and analytics
- Horizontal scalability
- Near real-time indexing and search results
2. What is a cluster in Elasticsearch, and how does it work?
Answer: A cluster in Elasticsearch is a collection of one or more nodes (servers) that work together to store and process data. Each node in the cluster has a unique name and is responsible for a subset of the data. The nodes communicate with each other to synchronize data and ensure high availability and scalability.
3. Does Elasticsearch store data in memory or disk?
Answer: Elasticsearch stores data both in memory and on disk. When data indexes, it is initially stored in memory for fast access. Once the memory limit reaches, data flushes to the disk. Elasticsearch combines in-memory caching and disk-based storage to provide quick and efficient search and analytics capabilities while providing durability and fault tolerance.
4. What kind of data does Elasticsearch store?
Answer:
Elasticsearch is a versatile search and analytics engine that can store a wide range of data types-
- Text data: full-text search strings, document content, and log entries.
- Numeric data: integers, floats, and geospatial coordinates.
- Date and time data: timestamps and durations.
- Structured data: JSON documents, XML files, and CSV files.
- Binary data: images, audio, and video files.
- Statistical data: machine learning models for performing advanced analytics.
- Geospatial data: points, lines, and polygons for performing location-based searches and analysis.
- Graph data: social network graphs for performing graph-based searches and analysis.
- Metrics and performance data: server performance metrics and application performance metrics.
- Business data: customer data, product information, and financial data.
5. What are an index and an inverted index in Elasticsearch?
Answer:
Index
- In Elasticsearch, an index is similar to a database in the traditional sense.
- An index is a collection of documents that allow efficient searching and retrieval.
- Each document in an index contains one or more fields that can store data.
- The fields can be of different data types, such as text, numbers, dates, etc.
- An index can contain multiple documents, but using a single type per index is recommended for better organization.
Inverted Index
- An inverted index is a data structure used by Elasticsearch to look up documents based on the terms within them quickly.
- It’s called an inverted index because it’s essentially a “flipped” version of the original data, where the terms become the keys and the document IDs become the values.
- On document indexing, Elasticsearch tokenizes the text into terms and creates an inverted index for each term, which points to the documents that contain that term.
- The inverted index is highly optimized for searching and provides high-speed access to documents that match a particular query.
6. What is a document in Elasticsearch?
Answer:
The primary data type that Elasticsearch may store and index is a document.
- A document in Elasticsearch is similar to a row in a table in a traditional database.
- A document can be any piece of data represented in JSON formats, such as customer data, product information, or log entries.
- Each document has a unique ID within an index.
- A document consists of one or more fields, which can be of different data types, such as text, numbers, dates, and more.
- Elasticsearch can store and manage large numbers of documents, with each index potentially containing millions of documents.
- Addition, updation, and deletion of documents occur in real time without affecting other documents in the index.
- Elasticsearch provides robust APIs for working with documents, including indexing, searching, filtering, and aggregating.
7. How does Elasticsearch handle data replication and sharding?
Answer: Elasticsearch handles data replication and sharding by distributing the data across multiple nodes in the cluster. Each index splits into multiple shards, and each shard replicates to a configurable number of nodes to ensure high availability and fault tolerance.
8. Explain the difference between a document and a shard in Elasticsearch?
Answer: In Elasticsearch, a document is the basic unit of data that is indexable and searchable. It consists of a set of fields (key-value pairs) that contain the actual data. A shard is a single unit of an index containing a subset of the data and metadata for the index. Multiple shards can distribute the load across the nodes in the cluster.
Elasticsearch Interview Questions (Advanced)
9. How does Elasticsearch handle query operations?
Answer: Elasticsearch uses a query language called the Query DSL (Domain Specific Language) to search and retrieve data from its indices. The Query DSL provides a range of powerful search and filtering capabilities, including full-text search, geospatial search, and more.
10. What is the process of creating an index in Elasticsearch?
Answer: To create an index in Elasticsearch, you typically use the Create Index API. This involves specifying the name of the index, along with any relevant settings and mappings that define the structure and behavior of the index.
11. What is the primary use of Elasticsearch?
Answer: Elasticsearch is a powerful search and analytics engine with many uses-
- Full-text search: It can search large volumes of text-based data quickly and efficiently.
- Log analytics: It can store and analyze logs and machine-generated data, allowing users to gain insights into system performance, security, and other metrics.
- Business analytics: It allows users to gain insights into business trends and performance.
- Website search: It can power search functionality on websites and e-commerce platforms, providing users with fast and accurate search results.
- Data exploration: It can explore and analyze large volumes of data, allowing users to visualize and understand patterns and trends in their data.
- Geospatial search: It allows users to perform location-based searches and analyses.
- Machine learning: It uses machine learning frameworks such as Apache Spark and TensorFlow to perform advanced analytics and predictive modeling on large datasets.
- Application performance monitoring: It allows users to troubleshoot issues and optimize system performance by analyzing performing metrics from applications.
12. How can one tune Elasticsearch for better performance?
Answer: There are several strategies you can use to improve Elasticsearch performance, such as-
- Scaling the cluster horizontally by adding more nodes
- Optimizing data storage and indexing settings
- Using appropriate hardware and infrastructure
- Reducing the number of shards per node
- Tuning garbage collection settings
- Using caching mechanisms like Doc Values and Field Data
- Using search profiling and query optimization techniques
13. What is the difference between Elasticsearch and Apache Solr?
Answer: Elasticsearch and Apache Solr are open-source search engines built on top of Apache Lucene but have some key differences.
- Elasticsearch is more scalable and flexible, with a more straightforward and intuitive API.
- On the other hand, Solr provides more advanced features and customization options but can be more complex to set up and maintain.
14. How does Elasticsearch handle security and access control?
Answer: Elasticsearch provides a range of security features to help secure data, including:
- Transport-level encryption to secure communication between nodes
- Access control mechanisms to restrict access to indices and actions
- Role-based access control to define permissions for specific users and groups
- Audit logging to track and monitor user activity
- Integration with external authentication and authorization providers
15. What is a Node in Elasticsearch?
Answer:
In Elasticsearch, a node is a single instance of Elasticsearch running on a server.
- A node is the smallest unit of an Elasticsearch cluster.
- Each node contains a complete copy of the index or indices it is responsible for.
- A node can perform different roles, such as master-eligible, data, ingest, or coordinating node.
- A master-eligible node coordinates the cluster and maintains cluster-level metadata.
- A data node stores the index data and performs indexing and searching operations.
- An ingest node performs pre-processing on documents before indexing.
- A coordinating node acts as a traffic cop, routing requests to the appropriate nodes and coordinating responses.
- Elasticsearch provides several APIs for managing nodes, such as adding or removing nodes, monitoring node health, and performing rolling upgrades.
16. What is Schema in Elasticsearch?
Answer:
In Elasticsearch, a schema defines the fields and data types that index and search documents within an index.
- A schema is also known as a mapping in Elasticsearch.
- A schema defines the fields in a document and the data types and settings for those fields.
- Elasticsearch uses a dynamic schema by default, meaning that it automatically detects the fields and data types in incoming documents and creates mappings on the fly.
- However, a dynamic schema can lead to mapping conflicts and unexpected behaviors, so explicitly defining a schema is often recommended.
- JSON or YAML can define a schema.
- Elasticsearch’s REST API or the Elasticsearch Python client can create the schema.
Final Thoughts
Elasticsearch is now a crucial tool for searching and analyzing vast amounts of data. Businesses seek competent Elasticsearch developers who can provide effective and dependable solutions. One can improve your chances of getting hired by practicing using these Elasticsearch interview questions and showcasing one’s proficiency in Elasticsearch.
Frequently Asked Questions (FAQs)
1. What is Elasticsearch used for?
Answer: Elasticsearch has many uses-
- Full-text search
- Log Analytics
- Business Analytics
- Website search
- Data exploration
- Geospatial search
- Machine learning
- Application performance monitoring
2. Is Elasticsearch SQL or NoSQL?
Answer: Elasticsearch is a NoSQL database, as it does not use a traditional relational database schema or SQL language for querying data.
3. Why use Elasticsearch instead of SQL?
Answer: Elasticsearch provides fast and efficient search and analytics capabilities for unstructured data, while SQL databases work for structured data. Elasticsearch also offers scalability, fault tolerance, and real-time analytics capabilities that may be difficult to achieve with a traditional SQL database.
4. Why is Elasticsearch faster than SQL?
Answer: Elasticsearch is faster than SQL databases for search and analytics of unstructured data because it uses inverted indexes and a distributed search architecture to quickly search and retrieve data from large volumes of data across multiple nodes.
5. Which ways are used for searching in Elasticsearch?
Answer: Elasticsearch provides a variety of ways to search data, including:
- Full-text search: searches for exact or partial matches of text-based data.
- Term search: searches for exact matches of a term or terms in the data.
- Range search: searches for data within a specified numeric or date range.
- Geospatial search: searches for data based on geographic location.
- Fuzzy search: searches for data similar or related to a specified term or phrase.
- Boolean search: combines multiple search criteria using Boolean logic to retrieve data that meets specific criteria.
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