20 ELK Stack Interview Questions and Answers

The ELK Stack is a popular open source solution for managing log data. It is comprised of three main components: Elasticsearch, Logstash, and Kibana. When applying for a position that involves the ELK Stack, it is important to be prepared to answer questions about all three components. In this article, we will review some of the most commonly asked questions about the ELK Stack and how you should answer them.

ELK Stack Interview Questions and Answers

Here are 20 commonly asked ELK Stack interview questions and answers to prepare you for your interview:

1. What is ELK Stack?

ELK Stack is a combination of three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine, Logstash is a data ingestion and transformation tool, and Kibana is a data visualization tool. Together, these three tools provide a powerful platform for data analysis and exploration.

2. Can you explain what Elasticsearch is in the context of ELK Stack?

Elasticsearch is a search and analytics engine that forms the heart of the ELK Stack. It can be used to search through all of the data that is stored in the other components of the stack, such as Logstash and Kibana. Elasticsearch is also used to provide analytics on this data, such as identifying trends and patterns.

3. How does Logstash work with other components of the ELK stack?

Logstash is the component of the ELK stack that is responsible for collecting and parsing data from a variety of sources, and then sending it to Elasticsearch for indexing. Logstash can be configured to work with a wide variety of data sources, and it has a large number of plugins that allow it to parse and transform data into the desired format.

4. What are some typical use cases for Kibana?

Kibana is a great tool for visualizing data and creating dashboards. Some typical use cases include monitoring server logs, analyzing application performance, and tracking customer behavior.

5. Can you give me a few examples where ELK stack can be used?

ELK stack can be used for a variety of tasks, including log management, data analysis, and monitoring.

6. What types of visualizations can be created using Kibana?

Kibana is a visualization tool that can be used to create a variety of different types of visualizations, including line graphs, bar charts, pie charts, heat maps, and more.

7. Does Kibana support any other features apart from data visualization? If yes, then which ones?

Kibana does support other features apart from data visualization. It also supports features such as data analysis, machine learning, and monitoring.

8. What’s the main difference between Graylog and ELK Stack?

The main difference between Graylog and ELK Stack is that Graylog is designed to be a centralized logging solution while ELK Stack is designed to be a more general data analysis platform. Graylog is easier to set up and use for centralized logging, but ELK Stack provides more features and flexibility for data analysis.

9. Is it possible to use multiple instances of Elasticsearch or Kibana on a single machine? If yes, how?

Yes, it is possible to use multiple instances of Elasticsearch or Kibana on a single machine. You can do this by running each instance on a different port.

10. Which programming language should one know before taking a job as an ELK developer?

One should be proficient in Java before taking a job as an ELK developer.

11. What do you understand about sharding in the context of Elasticsearch?

Sharding is a process of dividing data into smaller pieces so that it can be more easily managed and processed. In the context of Elasticsearch, sharding is used to improve performance by distributing the data across multiple nodes. This allows for faster search and retrieval times, as well as improved scalability.

12. Can you explain what an index is in the context of Elasticsearch?

An index is a collection of documents in Elasticsearch that have similar characteristics. An index can be compared to a database in a traditional relational database system. By creating an index, you can specify which fields will be searchable and how those fields should be analyzed.

13. What’s the structure of an index in Elasticsearch?

An index in Elasticsearch is a collection of documents that have similar characteristics. This similarity is determined by the mapping that you create for the index. A mapping is a blueprint for how the documents in an index should be structured. It includes the fields that will be present in each document, as well as the data types for those fields.

14. What is the role of Mapping in Elasticsearch?

Mapping is the process of defining how a document should be indexed and stored. This includes defining the data types, properties, and fields that should be included in the index. It is important to create a mapping before indexing documents, as it can help to ensure that the documents are properly indexed and searchable.

15. What are the different ways in which an index can be created in Elasticsearch?

There are three different ways in which an index can be created in Elasticsearch:

1. By using the create index API
2. By using the index template
3. By using the bulk API

16. How many shards does Elasticsearch create by default when creating an index?

Elasticsearch creates 5 shards by default when creating an index.

17. Can you explain what inverted indices are in the context of Elasticsearch?

Inverted indices are a type of index that is used in Elasticsearch in order to improve search performance. An inverted index contains a list of all of the unique terms that appear in a document, as well as a list of which documents each term appears in. This allows Elasticsearch to quickly identify which documents match a given search query.

18. What is the query DSL in the context of Elasticsearch?

The query DSL is a set of tools that allows you to specify the criteria for a search query, as well as the fields that you want to be returned in the results. It is written in JSON and is very flexible, allowing you to create complex queries.

19. What is the function that Elasticsearch uses to divide indexes into smaller pieces?

The function that Elasticsearch uses to divide indexes into smaller pieces is called shards. A shard is a single instance of Elasticsearch that stores a subset of the data in an index. By default, an index is divided into five shards.

20. Can you explain what CRUD operations are in the context of Elasticsearch?

CRUD stands for Create, Read, Update, and Delete. In Elasticsearch, these operations are used to manage the documents that are stored in the index. Creating a document simply means adding it to the index, while reading a document means retrieving it from the index. Updating a document means modifying it, and deleting a document means removing it from the index.