Elasticsearch - Great tool for search applications
April 05, 2017
Elasticsearch - Great tool for search applications
Score 8 out of 10
Vetted Review
Verified User
Overall Satisfaction with Elasticsearch
We use ElasticSearch for the search functionality in our application. We have a lot of data to search from and ElasticSearch makes it ridiculously fast by tokenizing the content. It enables us to do free text search in a large blob of audio transcripts that we have.
- Easy to scale - It's designed to be used across distributed environments. Indexes can be divided into shards, with each shard able to have any number of replicas.
- Search queries can be structured as JSON objects (in addition to text strings) that enables complex and robust searches.
- If your application needs an effective solution for dynamic searching, I think ElasticSearch is the way to go.
- If you want to store or retrieve data outside of searching, you may want to try a different solution since ElasticSearch's capabilities are limited.
- If you want to do large or complex computations with the data, ElasticSearch isn't really good at that.
- ElasticSearch shouldn't be the primary source of data because data backups and durability are not high priority.
- Open source project that optimized search views in our application.
- Cost of managing the elasticsearch clusters was added (which is small compared to the gains).
- We are highly satisfied with what we got out of ElasticSearch.
- Apache Solr and Riak
For our application, ElasticSearch fulfilled all the criteria we were looking for. Something that's easy to scale and flexible. I think ElasticSearch works better that Solr with modern real-time search applications. Also, ElasticSearch is easy to integrate with. ElasticSearch is distributed with real-time replications.