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The key features of Elasticsearch for Apache Hadoop include:
- Scalable Map/Reduce model
- elasticsearch-hadoop is built around Map/Reduce: every operation done in elasticsearch-hadoop results in multiple Hadoop tasks (based on the number of target shards) that interact, in parallel with Elasticsearch.
- REST based
- elasticsearch-hadoop uses Elasticsearch REST interface for communication, allowing for flexible deployments by minimizing the number of ports needed to be open within a network.
- Self contained
- the library has been designed to be small and efficient. At around 300KB and no extra dependencies outside Hadoop itself, distributing elasticsearch-hadoop within your cluster is simple and fast.
- Universal jar
- whether you are using Hadoop 1.x or Hadoop 2.x, vanilla Apache Hadoop or a certain distro, the same elasticsearch-hadoop jar works transparently across all of them.
- Memory and I/O efficient
- elasticsearch-hadoop is focused on performance. From pull-based parsing, to bulk updates and direct conversion to/of native types, elasticsearch-hadoop keeps its memory and network I/O usage finely-tuned.
- Adaptive I/O
- elasticsearch-hadoop detects transport errors and retries automatically. If the Elasticsearch node died, re-routes the request to the available nodes (which are discovered automatically). Additionally, if Elasticsearch is overloaded, elasticsearch-hadoop detects the data rejected and resents it, until it is either processed or the user-defined policy applies.
- Facilitates data co-location
- elasticsearch-hadoop fully integrates with Hadoop exposing its network access information, allowing co-located Elasticsearch and Hadoop clusters to be aware of each other and reduce network IO.
- Map/Reduce API support
- At its core, elasticsearch-hadoop uses the low-level Map/Reduce API to read and write data to Elasticsearch allowing for maximum integration flexibility and performance.
mapred) & new(
mapreduce) Map/Reduce APIs supported
elasticsearch-hadoop automatically adjusts to your environment; one does not have to change between using the
mapreduceAPIs - both are supported, by the same classes, at the same time.
- Apache Hive support
- Run Hive queries against Elasticsearch for advanced analystics and real_time responses. elasticsearch-hadoop exposes Elasticsearch as a Hive table so your scripts can crunch through data faster then ever.
- Apache Pig support
elasticsearch-hadoop supports Apache Pig exposing Elasticsearch as a native Pig
Storage. Run your Pig scripts against Elasticsearch without any modifications to your configuration or the Pig client.
- Cascading support
- Cascading is an application framework for Java developers to simply develop robust applications on Apache Hadoop. And with elasticsearch-hadoop, Cascading can run its flows directly onto Elasticsearch.
- Apache Spark
Run fast transformations directly against Elasticsearch, either by streaming data or indexing arbitrary
RDDs. Available in both Java and Scala flavors.
- Apache Storm
elasticsearch-hadoop supports Apache Storm exposing Elasticsearch as both a
Spout(source) or a