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  Anurag Srivastava

  Dec 3, 2018, 6:40:39 PM | Share

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  Scalability comparison:
  - Elasticsearch can move the shards automatically on different nodes when a new node joins or removed from a cluster. Solr is static here as it will not do anything and we need to rebalance the data by manually moving the shards but it takes several steps.
  
  Performance comparison:
  - Elasticsearch is able to choose whether to iterate over all the documents or only match a particular set of documents. With the logic inside the search engine, Elasticsearch can provide a very efficient range query without any data modifications.
  - I you have non-flat data, with lots of nested objects inside a nested object and inside another nested object and you don’t want to flatten down the data, but just index your JSON objects and have it ready for full-text searching? Elasticsearch will be a perfect tool for that with its support for objects, nested documents, and the parent-child relationship. Solr may not be the best fit here.
  
  Complexity of search
  - In Elasticsearch everything that you need from your data can be expressed in the query language. Up to Solr 7 Solr was still using the URI search, at least in its most widely used API. All the parameters went into the URI, which could lead to long and complicated queries.
  
  Ecosystem
  - The tools that come with Solr are nice, but they feel modest. If you look at the ecosystem around Elasticsearch it is very modern and sorted. You have a new version of Kibana with new features popping up every month. If you don’t like Kibana, you have Grafana which is now a product on its own providing a wide variety of features, you have a long list of data shippers and tools that can use Elasticsearch as a data source.
  

  
  

  I hope this has answered your question.
  

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