Preface
One of the main requirements of today's applications is search capability. In the market, we can find a lot of solutions that answer this need, both in commercial as well as the open source world. One of the most used libraries for searching is Apache Lucene. This library is the base of a large number of search solutions such as Apache Solr, Indextank, and ElasticSearch.
ElasticSearch is written with both cloud and distributed computing in mind. Its main author, Shay Banon, who is famous for having developed Compass (http://www.compass-project.org), released the first version of ElasticSearch in March 2010.
Thus, the main scope of ElasticSearch is to be a search engine; it also provides a lot of features that allow you to use it as a data store and an analytic engine using aggregations.
ElasticSearch contains a lot of innovative features: it is JSON/REST-based, natively distributed in a Map/Reduce approach, easy to set up, and extensible with plugins. In this book, we will go into the details of these features and many others available in ElasticSearch.
Before ElasticSearch, only Apache Solr was able to provide some of these functionalities, but it was not designed for the cloud and does not use the JSON/REST API. In the last few years, this situation has changed a bit with the release of the SolrCloud in 2012. For users who want to more thoroughly compare these two products, I suggest you read posts by Rafał Kuć, available at http://blog.sematext.com/2012/08/23/solr-vs-elasticsearch-part-1-overview/.
ElasticSearch is a product that is in a state of continuous evolution, and new functionalities are released by both the ElasticSearch company (the company founded by Shay Banon to provide commercial support for ElasticSearch) and ElasticSearch users as plugins (mainly available on GitHub).
Founded in 2012, the ElasticSearch company has raised a total of USD 104 million in funding. ElasticSearch's success can best be described by the words of Steven Schuurman, the company's cofounder and CEO:
It's incredible to receive this kind of support from our investors over such a short period of time. This speaks to the importance of what we're doing: businesses are generating more and more data—both user- and machine-generated—and it has become a strategic imperative for them to get value out of these assets, whether they are starting a new data-focused project or trying to leverage their current Hadoop or other Big data investments.
ElasticSearch has an impressive track record for its search product, powering customers such as Fourquare (which indexes over 50 million venues), the online music distribution platform SoundCloud, StumbleUpon, and the enterprise social network Xing, which has 14 million members. It also powers GitHub, which searches 20 terabytes of data and 1.3 billion files, and Loggly, which uses ElasticSearch as a key value store to index clusters of data for rapid analytics of logfiles.
In my opinion, ElasticSearch is probably one of the most powerful and easy-to-use search solutions on the market. Throughout this book and these recipes, the book's reviewers and I have sought to transmit our knowledge, passion, and best practices to help readers better manage ElasticSearch.
