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Allow Impala to start only with a running HMS (and no additional services like HDFS, HBase, Hive, YARN) and use the local file system. Skip all tests that need these services, use HDFS caching or assume that multiple impalads are running. To run Impala with the local filesystem, set TARGET_FILESYSTEM to 'local' and WAREHOUSE_LOCATION_PREFIX to a location on the local filesystem where the current user has permissions since this is the location where the test data will be extracted. Test coverage (with core strategy) in comparison with HDFS and S3: HDFS 1348 tests passed S3 1157 tests passed Local Filesystem 1161 tests passed Change-Id: Ic9718c7e0307273382b1cc6baf203ff2fb2acd03 Reviewed-on: http://gerrit.cloudera.org:8080/1352 Reviewed-by: Alex Behm <alex.behm@cloudera.com> Tested-by: Internal Jenkins Readability: Alex Behm <alex.behm@cloudera.com>
Welcome to Impala
Lightning-fast, distributed SQL queries for petabytes of data stored in Apache Hadoop clusters.
Impala is a modern, massively-distributed, massively-parallel, C++ query engine that lets you analyze, transform and combine data from a variety of data sources:
- Best of breed performance and scalability.
- Support for data stored in HDFS, Apache HBase and Amazon S3.
- Wide analytic SQL support, including window functions and subqueries.
- On-the-fly code generation using LLVM to generate CPU-efficient code tailored specifically to each individual query.
- Support for the most commonly-used Hadoop file formats, including the Apache Parquet (incubating) project.
- Apache-licensed, 100% open source.
More about Impala
To learn more about Impala as a business user, or to try Impala live or in a VM, please visit the Impala homepage.
If you are interested in contributing to Impala as a developer, or learning more about Impala's internals and architecture, visit the Impala wiki.