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.
Elasticsearch Interview Questions and Answers
A: Auditbeat is a lightweight shipper used to collect audit data from your systems. This Beat can also be used to detect crucial and unexpected changes to configuration files & binaries which can often be vital for pinpointing compliance issues and security violations occurring within your organisation.
A:Once you have Kibana loaded you will want to open the main menu and select Dashboard, from here you can select Create Dashboard. Once this step has been completed you will need to add panels of data to your dashboard for further visualisations and chart type selection to be applied.
A: The Logstash GeoIP filter is used to add supplementary information on the geographic location of IP addresses based on data available within the MaxMind GeoLite2 database.
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A: If you find that your Kibana instance is loading slowly it is often mentioned in the support forums that the reason this happens is due to the Apps bundle or apps themselves loading in the background.
Frequently asked powerapps interview questions for basic level:
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 analasys.
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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 Kibanas 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 : Kibana Docker s are of two different flavors called X-pack flavor and Oss Flavour. X-pack is the docker that is pre-installed one, hence called as default. Concerning the Oss flavor, it doesnt 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 agents 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.
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:
What is a Cluster?
A cluster is a collection of nodes which together holds data and provides joined indexing and search capabilities.
A node is an elastic search Instance. It is created when an elasticsearch instance begins.
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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.
Elasticsearch is used in many ways. It allows users to create indexes, highlight essential parts of results, classical full-text search, spell checker, general purpose document store, alerting engine, and fuzzy match.
There are many advantages of using the ELK stack
The 7.10 version of Elasticsearch and Kibana are classified as open-source versions. The latest version 7.11, is not Open Source, so now they are no longer Open Source softwares.
A bucket aggregation in elastic search creates sets of documents or buckets depending on the requirement. We can create filtering buckets based on the aggregation type, which represents multiple values, dates, IP ranges, etc. For example, a green bus, here an aggregation on the field will return a “bus” bucket and a “green” bucket. If a document that mentioned Red in the field will go to “green” bucked and likewise for “bus” bucket. Some documents will be seen in more than one bucket also.
Elasticsearch is ubiquitous for analytics and data research. In Elasticsearch, an adjustable RESTful API communicates with client applications. REST calls to load data and perform data analytics. To load data into Logstash to process within Elasticsearch, there includes some popular beats they are:
The data is stored in Elasticsearch in the default path. It depends on how you installed it. For RHEL/CentOS this will be located at /var/lib/elasticsearch & for Unbuntu/ Debian this will be located at /var/lib/elasticsearch/data,
An elasticsearch index is a set of documents that are related to each other. It contains multiple types of data and organisation.
Using Kibana has many advantages.
We can stop/ shutdown elasticsearch in three ways they are:
Elasticsearch is considered a modern search and analytical engine that is entirely open-source and built on java. Elasticsearch depends on the NoSQL database. It stores data in an unstructured way, and users cannot query using SQL.
Aws Elasticsearch service allows users to operate, deploy and scale Elasticsearch within the AWS cloud. Elasticsearch is an open-source search and analytics engine. AWS customised Kibana and added many additional features like an index, schedule reports, trace analytics, real-time monitoring, document and field level security, and clickstream analytics.
To check if Elasticsearch is running or not follow the below steps.
Advantages of using Elasticsearch are:
cURL means client URL, and it is a command line tool for developers to transfer server data. In Elasticsearch, cURL has many functions like list indexes, query using URL parameters, and list documents in the index. The Elasticsearch documentation uses cURL command line syntax to briefly and constantly define HTTP requests.
WinLogBeat is a blog reader that reads event logs with Windows APIs. It also filters the events depending on the configuration of the user and sends the data as configured outputs. When Windows log data is integrated with the ELK stack, errors and security-related issues can be monitored.
In logstash, there are four types of plugins: filter plugins, codec plugins, input plugins, and output plugins. To install the Logstash plugin, first we need to download it. They are available either as gem packages on https://github.com/logstash-plugins or https://rubygems.org/. Select the plugin you need to use and add it to the Logstash installation using the command: bin/logstash-plugin install logstash-input-GitHub. You can install plugins from GitHub, also.
There are many beneficial features of Logstash
A node is nothing but an instance of Elastic Elastic search. A collection of nodes is called a Cluster. Different nodes will work in collaboration and form an elastic search cluster. Each node has one or more than one role. Every node present in the cluster can handle transport traffic and HTTP accordingly.
Metricbeat is a lightweight metrics shipper built on the libbeat framework and can be installed on your server. Its main function is to collect metrics and statistics and send them for a specific output like Logstash or Elasticsearch. Some users use it as a service to buffer data and then automatically push their metrics data into Logstash.
Fuzzy search in Elasticsearch is an essential tool used to search usernames, a multitude of situations, and spellings, and sometimes we can solve aberrant problems with Fuzzy. Fuzzy search benefits the majority of eCommerce retailers if their visitors have spelling errors in locating a product when they need to buy it. In Elasticsearch, it provides positive matches even if there is a not-exact match of the items searched by the user.
FAQ
Why Elk stack is used?
Which three options are components of the Elk stack?
What is elk stack in Microservices?
