A free, distributed, and open search and analytics engine known as Elasticsearch is used for all types of data like textual, numerical, structured, unstructured, and geospatial. Released in 2010 by Elasticsearch N.V. and built on Apache Lucene. It has various features like scalability, speed, and distributed nature. A set of free and open tools for data enrichment, ingestion, storage, visualization, and analysis is known as Elastic Stack, whose center component is Elasticsearch. It has many lightweight shipping agents for sending data known as beats. We will present Elasticsearch Interview Questions in this article here.
Elasticsearch Interview Questions
- Give some uses of Elasticsearch.
A.1 Elasticsearch is quite a versatile search engine and can be used at many different places. Some of them are mentioned below:
- Logs: Scalable and fast logging that won’t quit.
- APM: Application performance insights can be obtained.
- Site search: Creation of an easy search for the website
- Workplace search: Corporate data silos can be centralized searched.
- SIEM: Automated threat detection and interactive investigation
- Endpoint Security: Detects, prevents, hunts, and responds to potential threats.
- Maps: Real-time exploration of location.
- App search: Search across documents, geodata, and more
- Uptime: Availability issues can be monitored and reacted to.
- Metrics: Metrics of your system can be monitored and visualized.
- Explain the working of Elasticsearch.
Elasticsearch collects raw data from a variety of sources like logs, web applications, and system metrics. Data ingestion is taking in raw data to normalize and enrich that before it is indexed. The data can be run on complex problems by users or use aggregations to receive complex data summaries after the data is indexed in the Elasticsearch. You can visualize the data, share dashboards and manage Elasticsearch by the use of Kibana.
- Can you please give more information about the Elasticsearch index?
A related collection of data is known as the Elasticsearch index. The documents are in the form of JSON documents. Corresponding values (strings, geolocations, booleans, array of values, dates, numbers, or any other data type) are correlated to a set of keys (names of fields or properties) in each document. Elasticsearch uses the inverted index as a data structure that is designed in a way to enable fast full-text searches. It searches for unique words in documents and identifies all documents with that word. Elasticsearch provides the data with an inverted index in the indexing process, which makes it easier to search in the near real-time run. An index is done by index API, which helps add or update a JSON document in a needed index.
- Tell us something about Logstash.
For the collection and transformation of data on the fly, Logstash is a server-side data processing, open-source software. HTTP to s3 bucket list, Logstash supports a large variety of input sources. It is commonly used for Elasticsearch as a pipeline due to its fly transformation. Filter plugins are used for the application of these transformations. Logstash provides many different versions for use.
- Logstash OSS: Under the Apache 2.0 Licence, it is open software.
- Others have Elasticsearch extensions that are proprietary under a license. But it is recommended to download Logstash OSS and used it with Managed Elasticsearch offerings.
- What is Kibana and tell some of its features that are acquired in Elasticsearch?
Kibana is an open software that helps visualize the data and search in Elasticsearch and sits on the top of the Elastic stack. For monitoring, managing, and securing an Elastic stack cluster and acting as a centralized hub for solutions developed in Elastic stack, which are in-built, Kibana is used. It also makes understanding data easy and modifiable. Kibana is used mainly due to its innumerable features:
- Discover: The Discover page in conjugation with time enables access to each field, making data exploration interactive and easy. Filtering search data, viewing documents, setting time filters, submitting search queries can be done.
- Visualize: In the Elasticsearch indices, Kibana allows us to visualize the data with the help of a wide variety of visualization. Some of the elements are as follows:
- Pie chart
- Data tables
- Line charts
- Geo maps
- Markdown visualization in the dashboard
- Single metric visualization.
- Time series
- Dashboards: It is like the menu for the elements that help create the visualization of the data. The main feature of the dashboards is their adaptability and dynamic nature. The filtration of data on the fly and the dashboard on the whole page can be done simultaneously. The features like modification, resizing, and arranging make it unique and easy to use because it can be saved and shared.
- Timelion: Using simple expression languages, it searches and designs the time series data, and it also has the feature to bring different sources in a single interface altogether. Queries, transformation, and visualization can be done by using this since it provides ways to get this done.
- APM: The application, along with services, can be monitored, and the Application Performance Monitoring system can collect deep performance metrics and errors. The apps can be checked regularly for performance data and bottlenecks.
- Dev Tools: A console supports the developers in writing the commands in one tab and viewing them in another one. Due to the presence of a console, a debugger and a search profiler can work together to configure the app and provide you with the best services.
- Monitoring: The three effective ways of monitoring are as follows:-
- Visualization of the data across is possible on Elasticsearch due to the presence of monitoring services by Kibana, which includes analyzing the performance data of apps like Kibana, Elasticsearch, and Logstash along with real-time analysis of beats.
- Analysation of the past performance of the apps can be done easily.
- Deep monitoring of data and directing it towards monitoring clusters.
- Management: Kibana’s runtime configuration can be performed by the use of a management page. There are three vital actions:-
- Index Pattern: Index names are configured by their support for initial and ongoing indexes.
- Saved Objects: Dashboards, visualization, and other elements are saved and hosted.
- Advance: It helps in improving Kibana’s behavior.
- Can you tell me some important uses of Kibana?
Kibana can be used in many different ways varying from analyzing to managing. Some of the uses are as follows:
- Interactive Charts: You can zoom in and out on some data, drag time, drill-down reports, and log in to some more interactive elements like seeing some insights.
- Filters: With a few clicks, you can run various analysis elements like histograms, trends, etc.
- Anomaly Detection: Detection of anomaly is easier because of machine learning. In simpler words, issues can be sorted quickly due to finding the root causes easily.
- Security: The options in the Kibana will stop the data leakage from protecting privacy. The sharing of data is possible with everyone in the team, like mates, boss, and the customers. The restriction option can create limited visibility.
- Graph: Along with creating graphs, you can also define a relation between them with the help of powerful graphics, which help summarize the data.
- Reporting: Reports can be generated easily by analyzing and visualizing. The reports can be scheduled, which helps get them at the perfect conditions. HTTP POST can also be used, which is an easier method.
- Canvas: It is a plain sheet that can be used for different visualization, which is a great method to create logos, graphics, drawings, etc.
- What is Term Shard?
For the splitting and distribution of data, the index is broken down into smaller atomic elements known as shards. The addition of more nodes is possible due to the distribution of shards over clusters.
- Give some advantages of Elasticsearch software.
Due to the flexible and versatile nature of Elasticsearch, it is preferred by a large number of people. Some of the benefits can be as follows:
- Creation of schema and also storing schema-less data.
- Record by record data can be modified and analyzed by APIs.
- Insights are easy to see and analyze.
- Scalability, capability, reliability is provided in real-time use of indexes.
- Scaling is available in both directions.
- What is the role of Tokenizer in ElasticSearch?
Stream is a division of values of documents of fields. A tokenizer does the breakdown/ division. Update and creation of inverted values are done with this data. The document contains the streams of data.
- Name some important functions/ operations that can be done on a document.
There are mainly four operations that can be performed to make the data simple and sorted. The operation is as follows:
- Indexing: The data is indexed to make it easier to access along with giving it proper documentation.
- Fetching: The data is fetched from all the available sources and compiled based on unique words appearing in them.
- Updating: The data can be updated via the use of Kibana and some in-built features. The data is modified, analyzed, and visualized.
- Deleting: The documents can be deleted after their work is done or when the user or the developer does not need it anymore.
- Explain and give the full form of NRT in Elasticsearch.
NRT is to get a real-time search or with a latency of seconds between indexing of documents and when it becomes searchable. NRT stands for Near real-time search.
Elasticsearch is a powerful tool used for frontend web development and can be a great asset if used with knowledge and accuracy. It is also a very common software used by big companies like Netflix, Amazon prime for the search results and what you want to watch. This can be seen when you enter the site and your recommendations are shown. This also provides some elements which make it more accessible along with its other features.
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