Native integration library for using Elasticsearch with Hadoop, Spark, and Hive for real-time search and analytics on big data.
Elasticsearch Hadoop is an open-source connector library that integrates Elasticsearch with Hadoop, Spark, and Hive. It enables big data processing frameworks to read data from Elasticsearch for analysis and write processed results back, bridging real-time search with batch processing. The library provides native APIs for MapReduce, Spark RDDs/DataFrames, and Hive external tables.
Data engineers and developers working with Hadoop ecosystems who need to combine Elasticsearch's real-time search capabilities with big data processing frameworks for analytics pipelines.
It offers a lightweight, dependency-free solution with native support for major Hadoop tools, eliminating custom integration code and enabling efficient bidirectional data flow between Elasticsearch and big data platforms.
:elephant: Elasticsearch real-time search and analytics natively integrated with Hadoop
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Provides dedicated APIs like EsInputFormat for Hadoop MapReduce, RDDs for Spark, and storage handlers for Hive, enabling seamless data flow without custom code, as shown in the usage examples.
Designed as a small, dependency-free jar (~300kB) that can be added directly to job classpaths, simplifying deployment in distributed environments, per the Requirements section.
Translates HiveQL and Spark SQL operations into Elasticsearch Query DSL at runtime, pushing down computations to Elasticsearch for optimized performance, as mentioned in Key Features.
Supports multiple big data frameworks including Hadoop MapReduce, Apache Spark (with Scala and Java APIs), and Apache Hive, offering flexibility for diverse data pipelines.
Requires careful alignment of ES-Hadoop versions with Elasticsearch clusters, as backward compatibility is limited and mismatches can cause issues, noted in the compatibility matrix.
Primarily optimized for batch processing with Hadoop and Spark, lacking native support for real-time streaming frameworks like Apache Flink or Kafka, which may limit use cases.
Demands manual management of properties like es.resource and es.query, plus jar dependencies in Hadoop environments, increasing setup complexity and potential errors.
Reading and writing in Hive are handled separately, and automatic query translation is incomplete, as admitted in the Hive section with 'we're working on unifying the two'.