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In this tutorial article, we step through an introduction on a very popular Elasticsearch feature, the Percolator.

When most ES developers think conventionally, they design documents according the structure of data and store them in an index. When they subsequently want to retrieve these documents, they define queries through the search API. The percolator works in the opposite direction. First you store queries into an index and then—through the Percolate API—you define documents in order to retrieve these queries. Continue reading to see how you can use Percolate to perform these reverse searches.

Bitemyapp, an avid Haskell user and contributor to several github projects, is building a new client library and DSL for Elasticsearch in Haskell. The project — aptly named “Bloodhound” — is licensed under the Apache 2.0 license and is publicly available on github for contributions.

Bloodhound is currently the primary Haskell client and DSL for Elasticsearch, with Bitemyapp currently the only contributor to the project. In alpha, Bloodhound is not to be considered final or stable at this time, but as bitemyapp explains:

“Bloodhound is alpha at the moment. The library works fine, but I don’t want to mislead anyone into thinking the API is final or stable. I wouldn’t call the library “complete” or representative of everything you can do in Elasticsearch but compared to clients in other languages, the usability is good.”

Clinton Gormley, co-author of “Elasticsearch – The Definitive Guide” and developer for Elasticsearch, recently gave a presentation at ScaleConf about scaling real-time search and analytics with Elasticsearch.

In his presentation Gormley talks about an inverted index, lucene segmenting, shards, and nodes in a cluster. His presentation offers a descriptive overview of how Elasticsearch creates its real-time, powerful, scalable, search and analytics. Even those without a technical background will find this presentation is excellent in conveying the power and utility of Elasticsearch.

Welcome to Episode #3 of our Elasticsearch tutorial. In our last episode we searched with and learned about some of the Query DSL of Elasticsearch. Today we’ll create unstructured search in Elasticsearch using analyzers. After you have an Elasticsearch cluster started per instructions of Episode #1, we’ll get started.

Three new aggregation types were released in Elasticsearch v1.1.0:

• Significant Terms
• Cardinality
• Percentile

As we did in our earlier aggregations post, we will explain these new aggregations through examples. If you’ve never used aggregations before, please visit our introduction before you begin this tutorial. To kick things off we’ll start a local Elasticsearch cluster and import our data.