ELK Stack is a platform, a combination of different tools used for log management. ELK Stack is designed to allow business organizations to perform several functionalities like searching, analyzing, and visualizing the data on a real-time basis. ELK stack also provides centralized logging, which helps users identify the issues or problems in the applications or servers related to the business. In this article, you can go through the set of ELK Stack interview questions most frequently asked in the interview panel. And these lists will help you to crack the interview as the topmost industry experts curate these at HKR training. Let us have a quick review of the ELK stack interview questions.
Ans: ELK Stack refers to Elasticsearch, Logstash, and Kibana, a combination of three different tools that work together for data analysis and log management. All these three form a single system for performing data related functionalities like storage, retrieval of data, sorting, and data analysis.
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Components of ELK stack:
1.Elastic Search: Elastic Search in ELK is the product that stores the data of the applications and log management.
2.Logstash: Logstash in ELK is the server component designed to process the incoming logs and feeds to ElasticSearch.
3.Kibana: Kibana in ELK is the web interface designed to search and visualize the logs based on the business needs.
Ans: Kibana is the platform designed for the visualization and exploration of the data from ElasticSearch. It is an open-source product that helps perform the data analysis in an advanced manner along with data visualization in the form of tables, charts, maps, etc. Kibana also helps in creating dynamic dashboards and share them as well. In simpler times, Kibana is the data visualization tool, a simple web-based interface used to perform the operations on the data using RESTful APIs.
Ans: Kibana holds a list of features and the most critical features of Kibana are listed below:
1.Allows user management
2.Provides Cognitive insights related to the data and the organization
3.Sends the automatic email notifications on the Elasticsearch monitoring alerts
4.Allows exporting of the data into CSV format
5.Also includes the dashboard-only mode
6.Provides the flexibility to view the surrounding documents
7.Makes use a new language called kuery for help in enhancing the Kibana's performance
8.Maintaining and running of proximity events
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Ans: Kibana dashboard is the page in the Kibana used to create, view, and modify the custom dashboards. Using a dashboard, multiple visualizations are allowed to combine on a single page. It also allows filtering of the visualizations using the elements in the filter option. The Kibana dashboard gives an overall view of the different logs and the relationships between the various logs and visualizations.
The following are the steps to create a dashboard in Kibana:
1.Go to the dashboard menu item and click on it.
2.Navigate to the option called Add visualization and click on the same.
3.Followed by Added Log counts pie chart
4.Click on the collapsed Add visualization menu
5.Resize and rearrange the visualizations as needed
6.Save the dashboard by clicking on save
7.Add a name to the dashboard before saving it.
Ans: As discussed earlier, Elasticsearch is a database that allows the management of the data, either document-oriented or semi-structured. It helps in performing operations like storing, retrieving and managing the data as needed. Elasticsearch is designed in such a way to provide all the relevant analytics data or real-time search data.
Elastic search cluster is the combination or a group of one node or more nodes of instances that are interconnected or interlinked. The Elasticsearch cluster is responsible for searching, distributing the tasks, and indexing across the nodes.
Ans: An instance of Elasticsearch is called a node. There are different types of nodes that are listed below:
1.Data nodes: Data nodes are the nodes that hold the data that helps in performing operations like create, read, update and delete, search, and aggregations on the data.
2.Client nodes: Client nodes are the nodes that are authorized to send the cluster requests to the master node and the data requests to the data nodes.
3.Master nodes: Master nodes are the nodes that help manage and configure the data to add and remove the nodes to the cluster as required.
4.Ingest nodes: Ingest nodes are the nodes that help in pre-processing the documents before performing the indexing.
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Ans: Kibana Docker image: Kibana Docker images are of two different flavors called X-pack flavor and Oss Flavour. X-pack is the docker image that is pre-installed one, hence called as default. Concerning the Oss flavor, it doesn't have any link with the X-pack, but it is only an open-source one.
Kibana Port and Kibana.yml file: The configuration of the default setting is done on the localhost 5601 to run Kibana. The port number can be changed, or the connection can be established in an Elasticsearch that is installed on another machine, the kibana.yml file has to be updated. The Kibana server will then read the properties of the kibana.yml file.
Ans: Different operations can be performed on the document using Elasticsearch. They are:
Ans: Kibana provides the flexibility to host the Elasticsearch and navigate to perform the searching and modifications efficiently. The major components of the Kibana are listed below:
Kibana Visual interface: Kibana visual interface is a platform designed to make the changes or modifications to the customs based on the requirements. This includes bars, pie charts, and tables related to data.
Ans: A document in Elasticsearch refers to the structured data represented in the form of segments. It is correlated accordingly, and every field can be represented multiple times in the document.
There are two types of queries supported by Elastic search:
Ans: Analyzers in Elasticsearch are specifically designed for text analysis. Analyzers include at least one tokenizer, zero or more character filters, and zero or more filters. Analyzers could be either a custom analyzer or a built-in analyzer, and There are different types of analyzers available in Elasticsearch. They are listed below:
Ans: Index: A cluster in Elasticsearch consists of multiple indices or indexes. In relational databases, the indices consist of multiple types called tables. As a table consists of the rows and columns in a relational database, the documents are represented as rows, and the properties in the documents represent the columns.
Shards: Shards are used in the scenario when there is an increase in the number of documents. The indexed data is divided into different small chunks represented as Shards. When the number of documents increases, the processing power also should be compatible to respond to the client requests, which might take more than the expected. In such cases, the shards will help fetch the results quickly and faster during the data search.
Replica: Replica refers to the copy of the shard, which efficiently manages requests. Replicas are primarily used to enhance the query throughput or gain high availability during extreme load conditions.
Ans: Filebeat is used to represent the shipping of the log files or log data. Filebeat is designed to play the logging agent's role, which is installed on the machine generating the log files, tailing them, and forwarding the data to either Logstash for advanced processing or directly into Elasticsearch for indexing.
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Ans: Logstash is the product in ELK that is called as the data-pipeline tool. It is specifically designed to perform operations like collecting, storing, and parsing the logs for future use. It is an open-source data collection engine capable of unifying the data from multiple resources and normalizing it. Logstash also helps in diverse downstream analytics for business enhancements.
[Related article:Elastic search Tutorial]
Ans: The following are the X-pack commands:
Ans: The following are the configuration management tools that are supported by Elasticsearch.
Ans: Elasticsearch is called NRT (Near Real-Time), as Elasticsearch is the fastest possible search platform. The latency is less than one second from the time of indexing of the document. It immediately becomes searchable within no span of time.
Ans: Tokenizer is a term in Elasticsearch defined to break or divide the stream of strings into a set of characters. In simple terms, tokenizers help break into tokens, and the output will be represented in the form of an array or collection of the tokens. Tokenizers can be represented in the form of structured text, word-oriented, or partial-word tokenizers.
Ans. Filters are correctly used to compare the tokens with the searched stream. The output would be either true or false, a boolean value. Tokenizers send the text tokens to the Token filters and can be modified to compare them with the search conditions.
Ans: There are three possible ways in which we can perform a search in Elasticsearch. They are represented below:
Ans: The syntax that is used to retrieve the document by ID in Elasticsearch is:
The GET API is used to retrieve the specified JSON document from an index.
Ans: Mapping refers to the outline of the documents that are stored in the index. Mapping helps in understanding the indexing of the document and how the fields are indexed and stored accordingly.
Ans: The following are the steps to be followed to start an Elasticsearch server from the command line:
Ans: The index can be deleted in the Elasticsearch by entering the following syntax:
_all or * can also be used to delete or remove all the indices.
Ans: Elasticsearch uses Query DSL (Domain Specific Language), an Apache Lucene query language.
Ans: The filebeat is dependent on the Logstash forwarder source code and replaces the Logstash forwarder. This method is specifically used to make use of the tailing log file and forward the same accordingly to the Logstash.
Ans: The Java version required to install the Logstash is Java8, while it does not support Java9. Some of the inputs used in Logstash are:
Ans: GeoIP plugin: GeoIP plugin helps in deriving the geographic location information by looking up the IP addresses and also adds the information to the logs and log files.
Grok Filter plugin: The Grok filter plugin is used to pars the unstructured log data into structured and queryable data. The grok filter is primarily responsible for looking into the patterns of the incoming log data, the configuration of the plugin. It helps you to make the right decision on how to identify the patterns as well.
Ans: The logs and metrics are responsible for handling all the different types of logging data. It also helps in capturing different log formats like networking and firewall logs, syslog, etc. It helps in collecting the metrics from NetFlow, JMX, and many other platforms and infrastructures. It also includes compatibility with Filebeat.
Ans: Filters in Logstash refer to the processing devices, which will help you combine with the conditionals. It is done to perform an action or an event based on the criteria set. Some of the filters include: Clone, Drop, Mutate. Grok, GeoIP.
Codecs: Codecs refer to the streamlined filter that helps separate the transport of the messages from the serial process. It can be used on both input and output basis. Some of the codecs are msgpack, plain(text), json.