Elasticsearch is a distributed search and analytics engine based on Lucene, which was the most popular solution in its field in the middle of the 2010’s. It was written in Java language, and distributed under Apache license. It is based on Lucene library (as well as Solr, which is the second most popular system). Official clients are available in Java, .NET (C#), Python, Groovy and several other programming languages.
The system was developed by Elastic in conjunction with other related projects: Logstash data collections and analyzing system, and visualization and analytics platform that works in Kibana environment. These three products are intended to be used as a unique solution called ELK Stack.
The following huge websites are based on Elasticsearch:
Amazon, IBM, Qbox and Elastic offer Elasticsearch as a controlled service for the subscribers. The majority of public cloud services also support Kibana.
Continue reading, and you will find the Elasticsearch tutorial that will give you the full understanding of the product.
Elasticsearch provides a horizontally scaled system that supports multithreading. Searching indexes can be divided into segments, while each segment can have several copies. Each node can host several segments, while the nod is used as the coordinating device that delegates operations to a specific segment, keeping the balance and creating proper routes in the automatic mode. Related information is usually stored within one index that contains one or several initial segments, and several copies. After creating an index, one can’t change the number of the initial segments.
Example of the analysis process using an html tag with embedded sentence:
All the features of Lucene are available through API interfaces on JSON and Java.
Another feature of the solution is the so-called “gate” that provides the long-term storage of the index. The index can be restored from the gate in case of server failure. The system supports real-time GET requests, which allows using it as NoSQL DBMS that doesn’t support distributed transactions.
On this page, you can also check different Elasticsearch tutorials: ELK stack tutorial, Elasticsearch Java tutorial, Elasticsearch Query tutorial, Elasticsearch Python tutorial, Elasticsearch Kibana tutorial, AWS Elasticsearch tutorial, Elasticsearch PHP tutorial.
Spend some time in order to watch the useful information offered in a form of interesting videos.
Getting started with ElasticSearch:
ELK stack tutorial (playlist):
Elasticsearch Java tutorial:
Elasticsearch Query tutorial (2 videos):
Elasticsearch Python tutorial (playlist):
Elasticsearch Kibana tutorial:
AWS Elasticsearch tutorial:
Elasticsearch PHP tutorial:
Elasticsearch tutorial in PDF file:
In case if you want to read we can offer also this Elasticsearch tutorial PDF (clickable).
The commercial version of the system supports several features, which are not offered in the freeware one, including the role model for the users, the notification system, the machine learning engines, and graph analytics. The commercial version is offered in the form of the annual subscription.
In 2004, Shay Banon created the predecessor of Elasticsearch called Compass. While developing the third version of Compass, Banon realized that he needed to create everything from scratch because he wanted to develop a scalable version of the system. As a result, the first version of Elasticsearch was released in February 2010.
In 2012, in order to monetize the project, Banon founded Elasticsearch BV Company in the Netherlands. In June 2014, the company announced that it managed to attract $70 million during the third cycle of investments. The entire process was carried out under the supervision of New Enterprise Associates (NEA). Benchmark Capital and Index Ventures managed to become secondary sponsors, thus bringing the total amount of $104 million to the project.
In March 2015, Elasticsearch changed its name to Elastic.
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