Yesterday, Elasticsearch, the company that provides commercial support for the open source Elasticsearch search engine, announced the release of version 1.0. Coming almost exactly four years after the first release, version 1.0 offers major upgrades and represents the second major announcement by Elasticsearch, the company, in the first quarter of 2014. The first was Marvel, a new system for monitoring and analyzing the performance of Elasticsearch clusters.
Elasticsearch was originally spun off from the Compass project, an open source Java search engine framework, back in 2004, in an effort to create a highly scalable search solution. Built on top of the well-known and popular Lucene library from the Apache Software Foundation, Elasticsearch adds such features as multitenancy, sharding, faceted search, and a JSON-based REST API. This feature set puts it in competition with the Solr project as a complete search solution built on top of Lucene.
Since its first release in 2010, Elasticsearch has gained a substantial following with a number of high-profile organizations choosing to adopt it, including Netflix, Yelp, Verizon, Foursquare, Github, Associated Press, Soundcloud, Facebook, and McGraw-Hill. Elasticsearch is being applied to uses ranging from content search to social media analytics.
The 1.0 release of Elasticsearch adds a number of enhancements that make it a more robust enterprise search solution. Specific additions include federated analytics, new aggregation and analytics tools for performing sophisticated queries, a new "distributed percolation" feature to power an "alert-like" function, and new backup and restore capabilities, including incremental snapshots.
Shay Banon, founder of Elasticsearch, highlighted the importance of the new release: "Business leaders want actionable insights, but they also want a solution that will have the scale, stability, and robust features to grow as their business grows. That is what we are delivering with 1.0."
Given that Elasticsearch was already well regarded for its performance, distributed search facilities, automatic shard rebalancing ability, and support for complex nested types, this new release should go a long way toward establishing it as a premier enterprise search solutions. The new Marvel is an incredibly clean and sophisticated application for monitoring Elasticsearch deployments in real time, and it adds even more to the Elasticsearch value proposition with in-depth collection and monitoring of a wide array of internal metrics, along with a free-for-development license and affordable pricing.
In addition to the core search system, Elasticsearch combines with Logstash and Kibana to form the ELK stack, a complete solution for searching, analyzing, and visualizing data. Logstash moves logs and other time-series data from disparate sources into the central search repository, and it scrubs and parses data before canonicalizing it as JSON. Kibana provides visualization support by allowing users to interact with data stored in Elasticsearch, enabling customers to create custom dashboards that display changes to Elasticsearch data in real time.
With a reputation for impressive scalability, major new features in the 1.0 version, and the accompanying Marvel tool, Elasticsearch appears poised to continue its rapid growth and penetration of the enterprise market. Those looking to implement enterprise search or replace a legacy search system will almost certainly include Elasticsearch 1.0 on their list of candidates for evaluation.
This story, "Ultra-hip Elasticsearch hits commercial release" was originally published by InfoWorld.