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ElasticSearch handles a large number of requests quickly and easily
February 27, 2019

ElasticSearch handles a large number of requests quickly and easily

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with Elasticsearch

ElasticSearch is used to store all searchable data indices from our product. We use ElasticSearch because it is extremely fast, highly available, and able to meet the demand of our product. We were using a different index-based search technology before, and it failed terribly. We migrated to ElasticSearch and have been very happy with the results.
  • Easy to install
  • Easy to use/lots of documentation
  • Easy to scale up as demand increases
  • The price point for the X-Pack plugins (ie. Security, Alerting, etc.) is a bit high, especially if you only want to do something small and simple and you don't need to leverage the full power of the plugin
  • Configuring the right hardware and capacity planning (when at scale) can get really tricky. In order to get the best performance, a lot of tweaking is needed, and not all of the secret tricks are documented
  • Getting used to ElasticSearch's query language was a bit of an adjustment. You really have to delve into defining analyzers and tokenizers in order to get application-specific results
  • ElasticSearch was able to meet the high demands of our product when it mattered most.
  • Implementation of ElasticSearch was easy and quick, saving on the cost of implementation.
  • Managing ElasticSearch is very easy. With the right monitoring tools in place, it really is "set it and forget it".
All database systems have things they are good at, and things they aren't as good at. Riak/SOLR is great as a K/V store, but SOLR cannot handle requests as fast as ElasticSearch. In fact, SOLR is the reason we had to migrate to ElasticSearch.
Redis is great at SET operations on large sets of data and quick in-memory operations. We actually use Redis for a small subset of tasks in our product that wasn't appropriate to perform on ElasticSearch. In this case, it was much faster and cheaper to use Redis.
Riak, Redis, MySQL, Amazon Relational Database Service, Amazon Elastic Compute Cloud (EC2), AWS Elastic Beanstalk, AWS Lambda, Docker, Bitbucket, GitHub, Gitlab, Amazon Aurora
ElasticSearch is great when you need a lot of data indexed really fast, as well as when you need to retrieve a large number of documents based on a complex query. Searching is super-fast.

If you need a large data store for documents where not everything needs to be indexed, don't use JUST ElasticSearch. We use one KV database system to store all of our data and use ElasticSearch as our Index. All searches are run off of ElasticSearch, and the main data store that it pulls from is the other database.