Elasticsearch Interview Questions
Elasticsearch is a real-time and evenly distributed search engine that supports RESTful searching and analysis based upon the Apache Lucene full-text search engine. It has distributed and full field real-time analytics storage. Along with Logstash and Kibana, Elasticsearch is widely used. In the field of Elasticsearch during the last few years, ’ maximum competition is created. Hence, it becomes mandatory to know the most common Elasticsearch interview questions if you are willing to build a career in this segment. Elasticsearch is broadly used by major platforms. Most important among them are Wikipedia, Netflix, IFTTT, Accenture, Hip chat, Fujitsu, Stack Overflow, and Medium.
Elasticsearch is also document-oriented enables the storage of data and then their indexing so that the content becomes easily searchable. Elasticsearch entirely works over the HTTP interface and JSON documents, and altogether it is developed in Java programming language. It is known that the Elasticsearch server uses the port range around 9200 to 9300. In order to check if the server is running, you just need to type the URL of any corresponding homepage which is primarily followed in reference to the port number. Looking for a reliable piece of information, take a follow-up through these Elasticsearch interview questions.
|It is built upon||Apache Lucene a full-text search engine|
|Document orientation||stores the data which are structured JSON documents|
|Text support||it supports full-text search indexing to provide faster results|
|Supports||it supports auto-completion and instant results.|
|APIs||Elasticsearch supports restful APIs for the retrieval of data and records|
|Serves||Elasticsearch search the cross-platform|
|Developed and written in||it is developed in Java language|
|License||Apache license 2.0|
|Developed by||Shay Banon|
|Elasticsearch is also an open source software|
After these basic details on Elasticsearch, let’s move to the Elasticsearch interview questions and their answers.
Using the common interface with JSON and HTTP, Shay Banon authored Elasticsearch in Java programming language. In February 2010, Elastic NV developed a stable version of Elaticsearch. Currently, version 7.1.1 is in the market which was released on May 28, 2019.
- Elasticsearch is compatible with any platform
- It is a Near real-time (NRT) which makes it easily searchable
- The cluster of Elasticsearch is evenly distributed, which increase its scalability.
- It supports multiple documents
- Elasticsearch efficiently deals with the managed documents
Most Frequently Asked Elasticsearch Interview Questions And Answers With Examples:
- What is Elasticsearch? How does it work?
- How to install elasticsearch on windows?
- How to create a node in Elasticsearch?
- What is the ingest node in Elasticsearch?
- What is a split brain problem in Elasticsearch?
- What is a quorum in Elasticsearch?
- What is an Elasticsearch query? Explain the types of queries?
- What is a cluster in Elasticsearch?
- What is a Type in Elasticsearch?
- What are the advantages of Elasticsearch?
- How to create an Elasticsearch user?
- What is the difference between Elasticsearch and Logstash?
- What are mappings in Elasticsearch?
- What are shards? How many shards should Elasticsearch indexes have?
- What is a document in reference to the Elasticsearch? What can basic operations be performed on a document?
- What is dynamic mapping in Elasticsearch?
- What is Elasticsearch analyzer?
- How to enable authentication in Elasticsearch?
- What is REPLICAS in Elasticsearch?
- What is routing in Elasticsearch?
- What is filter Elasticsearch?
- How do I speed up Elasticsearch?
- What is the query DSL Elasticsearch?
- What is Elasticsearch inverted index?
- How does Elasticsearch work with Kibana?
- What is fuzzy search Elasticsearch?
- Name the software which is required to install Elasticsearch?
- How a user can create an index in the Elasticsearch cluster?
- What is the aggregation working in Elasticsearch?
Elasticsearch promotes the indexing of documents targeting the repository. Elasticsearch stores and executes the data as it converts the initial form of files into the internal documents and secures them with the basic data structure resembling with the JSON objects.
Below mentioned are a few steps to install Elasticsearch in windows -
- Download the zip file from relevant sources(websites)
- Install it and launch through GUI
- Carefully select the configuration and logs directories
- Now choose to install it as service or manually
- Finish the configuration steps carefully; it will be followed as cluster name, node, etc
- Select the plug-in
In Elasticsearch, the nodes are added to enhance the quality and reliability of the cluster. There can be of the master node to control the entire cluster and also a basic data node. In order to add a node follows the steps-
- You have to set up a new Elasticsearch instance
- Specify its name in the cluster.name attribute
- Begin the Elasticsearch and node will be created
Note: This is one of the basic Elasticsearch interview questions but an important one.
In Elasticsearch, an ingest node is a type of note that can be utilized during the documentation process before indexing. It is a part of the Elasticsearch cluster, and it intercepts the index request and bulk applying the transformation and later passes it back to the index.
Split brain is a consequence that generates when the master nodes in the cluster fail. In the bunch of clusters if any master node fails, then the slave node can choose a new master note for the effective functionality. In case the former one gets restore or comes into the functioning again, then it leads to the conflict. This problem also rises when communication is failed among the nodes.
The quorum by default, is set to action.write_onsistency. in case the quorum is not fulfilled then the index returns after the timeout with an error. Elasticsearch documentation follows the rule for write_consiatency level in quorum as quorum(>replicas/2+1).
Elasticsearch has its own query domain where the queries can be defined in the JSON format. Elasticsearch serves domain specific language DSL queries that make it easy to resolve real-world queries. Broadly Elasticsearch is divided into the following two different types of queries which ultimately solve multiple other queries associated with them. The list of them is-
- Full-text queries - this includes the multi-match query, match phrase prefix query, match query, common terms query and many more
- Term level queries - this includes the term query, term set query, existing query, ids query, wildcard query, and lot more.
Cluster is a collection of one or multiple servers which consists of the data and also serves the federated indexing across all the different nodes. By default, a cluster can be identified by a significant name, i.e., Elasticsearch.
Being the open source and highly distributive network Elasticsearch has many advantages.
- It is a document-oriented platform
- Elasticsearch supports multiple search options
- A large number of complicated queries can be executed speedily with Elasticsearch
- In order to avoid the chance of data loss, Elasticsearch manages the record.
- Elasticsearch also supports indexing and multi-tenancy
In order to create an Elasticsearch user, you have to follow the below mentioned steps
- With the help of Elasticsearch built-in user, log in to 'Kibana.'
- Follow the management/security and user page
- Select 'create a new user.'
- Click over the ‘create new user’ and select "logstash_internal user.
- Elasticsearch is the open source search engine platform whereas Logstash is a server-side data processing platform.
- Logstash can process the data from multiple sources all at once and transforms them accordingly. Elasticsearch, on the other hand, stores the complex entities as JSON documents.
Shards are the statements or smaller portion of the nodes which manage the data in an index. Actually, this indexing is done in order to overcome the limitations associated with the resources, especially like ram or CPU for the scalability. In order to resolve this, data is fragmented into different portions, which are administered by a different node or Elasticsearch. By default, the Elasticsearch index has 5 shards, which are primary and 1 replica. Thus, in total, each index has 10 shards.
In Elasticsearch a document refers to the row with the relatable database. More or less, they are similar. The major difference among them is that in each document an index can have a variant structure but comprising the same data type for similar fields is required. Whereas, in a document, every field or structure having different data can exist multiple times.
Fields can also contain different documents at the same time. Elasticsearch is a document-oriented search engine platform where the documents are stored in sequence.
- Documents can be indexed (document updating occurs here)
- Right after indexing documents can be searched, sort or filtered around
- Documents can also be retrieved
- Full-text search or query resolution can also be done with documents.
The constituent of analyzers includes a tokenizer which is preceded by the Char filters and 1 or 0 token filters. On the name of API or any other mapping definition, the analytical module also refers to the analyzers. In Elasticsearch, analyzers are already provided which are ready for the user to apply. Users are also allowed to create custom analyzers and built token filters or characters accordingly.
Elasticsearch interview question on your own to get a better understanding.
In order to enable the authentication in Elasticsearch, follow the steps-
- Stop Kibana
- Stop the Elasticsearch
- Follow settings and add xpack.security.enabled to the ES_PATH_CONF/ elasticsearch.yml file
As Elasticsearch is a full-text distributed search engine, each index in it splits to multiple forms. It comprises five shards and one replica for each specific index. Replica serves the queries as requests, and each of them corresponds to the primary shared in the cluster. When the index is created the number of replicas per index can be defined. They exist in order to provide availability and fault tolerance.
In the Elasticsearch analyzer, the text which is passed through a character is obtained by the character filter. This can be edited as by deleting, adding, or shifting the characters in a number of ways. In the Elasticsearch analyzer, the tokens which are forwarded are obtained by the token filters. It can be edited by deleting or altering them.
Query DSL is actually a flexible and expressive search language that the Elasticsearch utilizes in order to expose the sparkling side of Lucene with a JSON interface. It simplifies the queries and transforms them to be more accurate and flexible and easy to debug.
The open source data visualization plug-in is known as the commander in Elasticsearch. It enhances the visual capacities on the top of the index over the Elasticsearch cluster. It also allows the user to create the lines, bar, and scatter plots for any of the charts or maps over the large volume of the data.
Fuzzy search is actually a process in which web page location document location is identified resembling with the search argument. It also serves even if the argument is not relevant with the search correspondent for the particular information.
- The advanced and latest version of Java its series
- Another version of Java 1.8.0_131
And index can be easily created in the Elasticsearch cluster all you have to do is use the command PUT prior to the index name. You will be permitted to create the index, and you can also add multiple other indexes if you need. Once it is done, you have to apply the command POST before the index name.
Note: The above question is a very important question when it comes to Elasticsearch interview questions.
In Elasticsearch, the aggregated data structures based on the search queries are provided by the aggregation hierarchy or framework. There are multiple aggregations available which have different outputs and functioning. Aggregation in Elasticsearch is a functional unit that binds the analytical details for the particular set of data information.