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c908ba1b7e
Hive allows creating Avro tables without an explicit Avro schema since 0.14.0. For such tables, the Avro schema is inferred from the column definitions, and not stored in the metadata at all (no Avro schema literal or Avro schema file). This patch adds support for loading the metadata of such tables, although Impala currently cannot create such tables (expect a follow-on patch). Change-Id: I9e66921ffbeff7ce6db9619bcfb30278b571cd95 Reviewed-on: http://gerrit.cloudera.org:8080/538 Reviewed-by: Alex Behm <alex.behm@cloudera.com> Tested-by: Internal Jenkins
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.