The River
Blog post from Elastic
Elasticsearch addresses the "river" problem by enabling the integration and processing of continuous data streams from various sources. This can involve direct data entry by users or the automatic pushing of data from tools like Cloudera's log aggregator. The focus of the discussion is on pulling data from external sources, exemplified by a Twitter component that listens to and indexes Twitter stream updates into Elasticsearch. These components, known as rivers, require additional features like failover support and state storage, which Elasticsearch provides by allocating rivers to nodes within its cluster. Rivers are represented as types within a special index called _river, allowing them to be easily created or deleted, with state stored as additional documents. The implementation of rivers facilitates the indexing of global data streams, enhancing the power and capabilities of data processing within Elasticsearch. The upcoming version 0.11 will feature several river implementations, including a Twitter river available as a plugin for easy installation.
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