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Why to use Elastic search monitoring

Page Background: In this document, we would discuss what is ElasticSearch and why are the benefits of using Elastic Search Monitoring and the different advantages for the same.

Elastic Search is a distributed, scalable, and available open-source search service based on Apache Lucene. It can be used to index and search large volumes of data quickly, analyze that data in near real-time, and return answers to queries in milliseconds. It can respond to search queries quickly because it doesn't search text directly; rather, it searches an index. Therefore it is a powerful document-oriented search engine software tool, which uses a structured data format for storing and searching data. It also comes with extensive APIs for storing and querying data.

Why Elasticsearch?

To provide top-of-the-class customer service, you need to be able to search quickly for your customers' preferred product from your large product base. To run an efficient and successful organisation, it is imperative in being able to access data and analytics from your database swiftly and rapidly. Data handling is more efficient when you can serve information faster.

Poor customer service can result from delayed retrieval of information. When designing a product, this is the most common issue. The relational database used for designing the product contains lots of tables, and getting consequential user data requires querying the data from them.

Elastic Search Features:

The features of Elastic are exposed as REST APIs and they are as follows:

  1. Index API – The Index API allows you to document the index.

  2. Get API – The Get API allows you to retrieve the document.

  3. Search API – The Search API offers the ability to submit your query and receive a result.

  4. Put Mapping API – The Mapping API allows for the custom definition of mapping choices.

Elastic search has developed its query domain language. Using the elastic search query language, you can build queries in JSON. Also, if you need to build queries for more complex queries then Elastic Search query language allows you to write nested queries as well. If you take into perspective more practical and real-world scenarios, then you might need to search with different conditions, weights, thresholds, predefined fields etc. Therefore this kind of real-world application of the search is easily catered to by the query language of Elastic Search.

How Elastic Search Query Works:

Please review the diagrams below, to understand briefly how Elastic Search Query Works:

What Is Elasticsearch Used For?

  • Text Search

  • Product Search

  • Data Aggregation

  • JSON Document Storage

  • Geo Search

  • Auto-Suggestion

  • Auto-Completion

  • Analytics

  • Security Analytics

The primary use cases for Elastic Search are described below:

Application search —- When using a search platform for data retrieval and reporting, applications need to be able to access, retrieve, and report data quickly.

Website search —- Elasticsearch’s popularity as a site search tool is steadily increasing. Websites find this open-source technology very useful for accurate searches and effective data management.

Enterprise search —- Elasticsearch is a search engine that can be used to index virtually any kind of data & achieve enterprise-wide search capabilities, including documents and E-commerce products. It has become increasingly popular over the past few years and has even replaced the search solutions of many popular websites.

Analytics —- Elasticsearch is commonly used for ingesting and analyzing log data in near-real-time, scaling to support high volumes of log data. Elasticsearch also provides important operational insights on log metrics, enabling actions to be taken.

Security analytics —- The Elastic Stack (ELK), which includes Logstash, Kibana and Beats, can be used to analyze access logs and similar information concerning system security. This allows you to gain a more complete picture of what is happening across your systems in real-time.

Benefits Of Using Elasticsearch

  • Faster Data retrieval: Data retrieval is quicker and more efficient when documents are stored near their corresponding metadata in the index. This reduces the number of data reads and as a result, increases search response time.

  • Quicker Response: Elasticsearch, can fetch requested search query data in a fraction of the time it would take for a traditional SQL database management system.

  • Scalability: Elasticsearch has a distributed architecture, which allows it to scale up to thousands of servers and accommodate petabytes of data. Scaling a workload across multiple nodes is easy. Start with fewer nodes and add more if needed, without incurring downtime. Customers then do not have to deal with the complexity of designing a distributed system, as that has already been done for them automatically.

  • Multilingual: Elastic Search supports multiple languages.

  • Document Oriented: The data is stored in JSON format, instead of the database tables that were used in the old implementations. This change allows for easier integration into other applications if you need to share your results with a team or client who uses a different platform.

  • Auto-completion: Elastic search allows for auto-completion of the search queries.

The Elastic Stack (ELK)

Elasticsearch is one of the core components of Elastic Stack, an open-source collection of tools for data ingestion, enrichment, storage, analysis and visualization. It was originally called the ELK stack after its component names Elasticsearch, Logstash and Kibana, but now also includes Beats. Although a search engine at its core, users started using Elasticsearch for log data and wanted a way to easily ingest and visualize that data.


In a nutshell, Elasticsearch is a search engine that can be used for many purposes, including search and analytics. It sits at the heart of an ecosystem of complementary tools that together can be used for many use cases including search, analytics, and data processing and storage.