Elasticsearch: what do you need to know?

Elasticsearch can very well be an index, a search engine or a big data solution. Others even say that “it’s a bit like Google”. By the way, Elasticsearch greatly depends on the level of familiarity each person has with it. Its use with Elastic Stack, its ecosystem of components has truly grown over the years.

Elasticsearch: what is it?

Elasticsearch can be defined as a search and analysis engine distributed open source. It is based on Apache Lucene but is developed in Java. At the very beginning, this technology was an evolutionary version of the Lucene open source research framework. Then, over time, it grew and horizontally scaled the Lucene indices.

Elasticsearch roles are so appealing to today’s businesses. Not only does it allow you to store, search and analyze huge volumes of data quickly and in near real time. But, technology also gives answers in seconds. This is because the technology does not search the text directly. Rather, he is looking for a index.

How does Elasticsearch work?

Elasticsearch works using a few basic concepts. These govern how it organizes backend data and concepts. They consist of three fundamental elements, the logical concepts, the main components and the ELK elastic stack.

Logical concepts

When we talk about the logical concepts of Elasticsearch, it is actually the documents, indexes and the inverted index. The documents are the basic unit of information (expressed in JSON) that will later be indexed in Elasticsearch. A document can be more than just text. It can be any data, be it numbers, strings or dates. Each document has a unique identifier and a given data type, describing the entity type of the document.

As to clues, they include documents with similar characteristics. In general, all documents in an index are logically related. In an e-commerce site for example, there is an index for customers, one for products, one for orders.

Otherwise, an inverted index is an index in Elasticsearch. It is the basis of operation of any search engine. It is actually a data structure that keeps a mapping of content like words or numbers to its locations. Basically, it’s a data structure that helps direct the user from a word to a document.

Main components

The main components of Elasticsearch are the group, node, shards and replicas. First, a group can contain one or more node instances. It is an Elasticsearch cluster and its power lies in the distribution of tasks, search and indexing.

Next, a knot is a single server that is part of a cluster. Its role is to store data. However, it also contributes to the indexing and search capabilities of the cluster. Depending on its configuration, there are different types of nodes: the master node, the data node and the client node.

Then, the fragments are the subdivisions of the index into several parts. Thanks to them, Elasticsearch guarantees redundancy.

And finally, the replicas are copies of the fragments. They provide redundant copies of data. As a result, they easily respond to read requests such as searching for or retrieving a document.

The Elastic Stack (ELK)

Elasticsearch is the core component by Elastic Stack. It is a set of open source tools for data ingestion, enrichment, storage, analysis and visualization. Thanks to its components Elasticsearch, Logstash and Kibanait is known as the ELK stack.

What is Elasticsearch used for?

Elasticsearch is primarily used for application search, website search, enterprise search, log logging and analysis. Elasticsearch is much needed for applications that depend on a search platform for accessing, retrieving, and reporting data. Thanks to its efficiency and precision in terms of research, it is also very useful in the search for sites. Also, when it comes to enterprise search, Elasticsearch is a successful tool. It helps in finding documents, products, blogs, people and any other. And as explained earlier, Elasticsearch is commonly used to ingest and analyze log data. Moreover, it arises as a source of important operational information on log metrics to drive actions.

In addition, security analysis, business analysis, infrastructure metrics and container monitoring added to this list. The ELK stack can analyze access and similar logs regarding system security very well. Consequently, it provides a more complete picture of everything happening on systems in real time. Also, many built-in features available in the ELK stack make it a good business analysis tool. The ELK stack is also used by organizations to analyze various metrics.

Enterprise use cases

Many organizations use Elasticserarch in various cases, such as netflix for example. It leverages the ELK stack to monitor and analyze customer service operations and security logs. For the case ofeBay, it uses Elasticsearch as its backbone. It even created a custom “Elasticsearch-as-a-Service” platform.

walmart also uses Elastic Stack to reveal the hidden potential of its data. It also exploits ELK’s security features. And this, for security with SSO, alerting for anomaly detection and monitoring for DevOps.

And why use Elasticsearch?

The benefits of using Elasticsearch are numerous. First of all, it’s fast. Its design after Lucene makes it excellent in full-text search. Then Elasticsearch is naturally distributed. This is the advantage of its possession of the different containers called partitions. These are duplicated to incorporate doubled copies of the data during hardware failures.

Then, Elasticsearch also has a multitude of features that make storing and searching data more efficient. And finally, the Elastic Stack makes it easy to ingest, visualize, and report data.

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