What is Elasticsearch? Terms such as index, search engine, analytical database, big data solution, etc. are a set of terms that you will encounter when you get to know the elastic database. Depending on your level of familiarity with this technology, these answers may help you. All of the above statements about what elasticsearch is can be true, and this is an important part of Elasticsearch’s appeal.

Over the years, Elasticsearch and its other components, which have grown into the Elastic Stack, have been used for many things. Things like searching a website, collecting and analyzing data, or even business intelligence. In this post, by understanding what Elasticsearch is, we answer the question of what Elasticsearch is used for and how it can be used; Stay with us.

What is Elasticsearch?

elastic search is one of the basic tools of elk. In general, ELK Stack is a collection of three open source products Elasticsearch, Logstash and Kibana, which are all developed, managed and maintained by Elastic.

In other words, Elasticsearch is an opensource search and analysis engine built on Apache Loosen and developed in Java. Elastic Search Database allows you to quickly store, search, and analyze large amounts of data and respond in milliseconds.

In addition, this search engine uses a document-based structure instead of tables and uses a REST API to store and search data.

How does Elasticsearch work?

Using API and data collection engine or log interpretation tools such as logstash and Amazon kinesis Firehose, you can send data to ElasticSearch in the form of JSON files.

Elasticsearch immediately caches the original file and adds a searchable key to the file’s cluster index. Now you can search and retrieve the file using the API designed for Elasticsearch. In addition, you can use Kibana.

Kibana is an open source visualization tool that, together with Elasticsearch, visualizes your data and creates interactive dashboards.

What is the meaning of indices in elastic search?

An index is a collection of documents that have similar characteristics. The index is the highest level you can search in Elasticsearch. We suggest that you think of an index as similar to a database, where each document is related to an index.

For example, in the context of an e-commerce website, you can have one directory for customers, one for products, one for orders, and so on. A profile is identified by a name that is used to refer to the profile when indexing, searching, updating, and deleting operations are performed against the documents in it.

What is inverted index?

We hope that by now you know the answer to your question about what is elasticsearch. Now we want to talk about the concept of Inverted Index.

An index in Elasticsearch is actually what is called an inverse index. A mechanism that all search engines work with. It is a data structure that stores a mapping of content, such as words or numbers, to its locations in a document or set of documents.

An inverted index is a map-like data structure that guides you from a word to a document. A reverse index does not store strings directly, but instead breaks each document into individual search terms (ie, each word). It then maps each search term to documents that contain those search terms. Using the Inverted Index, Elastic Search Database quickly finds the best matches even from very large data sets.

Is Elasticsearch free?

Yes, Elasticsearch is free and open source software. You can run Elastic search directly on site or on Amazon EC2 or Amazon Elasticsearch service.

If you run it on-premises or on Amazon EC2, you will be responsible for installing Elasticsearch and other required peripherals, preparing the infrastructure, and managing the cluster.

But on the other hand, the Amazon Elasticsearch service is a fully managed service that if you use it, you don’t need to worry about the time consumption of cluster management tasks such as providing hardware, software packaging, system recovery after each failure, backup Get and monitor.

What are the benefits of ElasticSearch?

Spend less time from start to finish

Interesting to know, ElasticSearch provides simple REST-based APIs and an easy-to-use HTTP user interface, and uses JSON files that are completely free. These features make it very quick and easy to start working with it and create applications for different purposes.

high efficiency

The fact that Elasticsearch is distributed allows it to process a large amount of data in parallel and quickly find the best answers for your searches.

Kibana is a popular visualization and reporting tool that is integrated with Elasticsearch.

Elasticsearch is also integrated with Beats and Logstash, allowing you to easily convert your source code and upload it to your Elasticsearch cluster.

In addition to these three items, you can use Elasticsearch open source plugins such as language interpreters and recommenders to increase the efficiency of your programs.

Almost Real Time operation

Elasticsearch operations such as reading or writing data usually take less than a second. Such a high speed allows you to use Elasticsearch in almost real-time tasks such as monitoring and error detection software.

Easy application development

Elasticsearch supports a variety of languages, including Java, Python, PHP, JavaScript, Node.js, Ruby, and more.

The most common uses of Elasticsearch

In addition to search, the use cases of Elasticsearch are always growing and changing over time. We mention 5 uses of Elasticsearch:

Logging and log analysis

For people who have worked with Elasticsearch, this usage is very familiar. The tools that come with Elasticsearch and are integrated with it make it one of the easiest logging methods at scale.

Many people use this possibility to activate their project logs. Elasticsearch provides you with a variety of features from Beats to Logstash and Ingest Node to collect data wherever they are and index them. Tools like Kibana give you the ability to create powerful dashboards and analytics, while Curator lets you automate maintenance operations.

Collection and synthesis of public data

Elastic Stack has several tools for easy collection and indexing of remote data. Also, like many non-relational storage methods that are based on document storage, not having a strict theme has made Elasticsearch flexible enough to load different data sources, maintain them, and make them searchable.

Fulltext search

This application was not too unexpected and far from the mind, because Full Text search is one of the main features of Elasticsearch, which we also mention in the list of the most used ones. Interestingly, customers who use this feature get much better results than traditional search methods and e-commerce.

From fraud detection, security to collaboration and beyond, our customers have proven that search capabilities with Elasticsearch are powerful and flexible, and include many tools that make searching easier.

Elasticsearch has its own query DSL. It also has the ability to automatically correct the text; For example, if you have a typo in the text, it will say: Do you mean “his own correct guess”?

Program criteria and data

Elasticsearch also works great on time series data such as metrics and application events. This is another area where the large Beats ecosystem allows you to easily collect data for collaborative searches. Whatever technology you use, Elasticsearch most likely has a component to search for its metrics and events. Even if it does not have such a component, there is no need to add it.

Imaging data

With hundreds of charting options, tile service for geographic information, TimeLion for time series data, Kibana is actually an incredibly powerful and easy-to-use visualization tool. For each of the things I said, there are several visual components in Kibana. If you’ve worked with various data access tools, you’ll find that Elasticsearch + Kibana will become your favorite tool for data visualization.

Conclusion

Although the things we reviewed together are not all the uses of ElasticSearch, they are the most used ones.

Elasticsearch and the rest of the Elastic Stack tools prove to be very versatile and as you saw above, there are many ways to integrate Elasticsearch with what you’re already doing, so it gives you more results.

This is the most interesting part of Elasticsearch for me, because instead of adding another database to store data, it gives you the ability to upgrade the technologies you are already using.