Elastic Search Vs Solr search

Solr is search server for creating standard search applications, no massive indexing and no real time updates are required, but on the other hand Elasticsearch takes it to the next level with an architecture aimed at building modern real-time search applications. Percolation is an exciting and innovative feature. Elasticsearch is scalable and speedy, and if distributed indexing is needed then Elasticsearch would be the right choice.

If you’ve already invested a lot of time in Solr, stick with it, unless there are specific use cases that it just doesn’t handle well.

If you need a data store that can handle analytical queries in addition to text searching, Elasticsearch is a better choice.

At the end, Solr and ElasticSearch are very close to each other in feature sets, and it would be really difficult to make a decision on one or the other, without really knowing the exact requirements.

What is Elasticsearch?

“Elasticsearch is a highly scalable open-source full-text search and analytics engine. It allows you to store, search, and analyze big volumes of data quickly and in near real time.”

It is a document-oriented search engine, that is, it stores and queries data in JSON format. It also provides a RESTful interface to interact with the Lucene engine.

Many popular communities including Github, StackOverflow, and Wikipedia benefit from Elasticsearch due to its speed, distributed architecture, and scalability.

Difference Between Elastic Vs Solr :

Optimized Query Execution:
Solr: Currently have none
Elastic : Faster range queries depending on the context

Search Speed:

solr: Best for static data, because of caches and uninverted reader

elastic: Very good for rapidly changing data, because of per-segment caches

Analysis Engine Performance:

Solr:

Great for static data with exact calculations
elastic:

Exactness of the results depends on data placement

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