ElasticSearch is an advanced open-source and cross-platform analytic and search engine built on Apache. Lucene and it’s written in Java supporting clients in many different languages such as PHP, Python, C++, and Ruby.

ElasticSearch’s first public release version was 0.4 in February 2010, and it’s developed by Shay Banon.

ElasticSearch takes in unstructured data from different locations, stores and indexes it according to user-specified mapping, and makes it searchable.

ElasticSearch analysis all types of data including text, numerical, geospatial, structured, and unstructured.

ElasticSearch search engine technology help in extracting meaning from data at scale.

For some problems, ElasticSeach can give you an answer back in milliseconds while other systems like Hadoop or Apache Spark might take hours.

Let’s see what are the pros and cons of Elasticsearch that the experts have experienced.

Pros and Cons of Elasticsearch

Pros

• Since it has been developed in Java, Elasticsearch is compatible to run on every platform.
• Compared to Apache Solr, multi-tenancy can be handled by Elasticsearch in an ease.
• There is no need to pay lisence cost for downloading Elasticsearch, because it is an open source.
• Elasticsearch supports all types of document, except for those who do not support text rendering.
• Elasticsearch is documented in several languages. Hence, people from different countries or regions can use it in their languages.
• There is a gateway concept for Elasticsearch. With the help of the gateway, we can create full backups easily.
• Since it is a real time search engine, it takes only a second before the added document is searchable in the particular engine.
• Since this search engine is document oriented, it makes easy to scale up in bigger organization.

Cons

• Once in while, a Split-brain problem occurs in Elasticsearch.
• Elasticsearch doesn’t have multilanguage support. It makes handling request and response data harder.
• Elasticsearch is indeed a flexible and powerful data storage search engine, but it is harder to learn.
• Data store of Elasticsearch is not that impressive, as there are other efficient options such as MongoDB, Hadoop etc. It has good performance in terms of small use cases. But if you are having the use of steaming TB’s data per day, then it is not a great option. It may chokes or loses your valuable data.

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