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Traditionally table metadata is loaded by the catalog and sent as thrift
to the Impala daemons. With Iceberg tables, some metadata, for example
the org.apache.iceberg.Table, is loaded in the Coordinator at the same
time as the thrift description is being deserialized. If the loading of
the org.apache.iceberg.Table fails, perhaps because of missing Iceberg
metadata, then the loading of the table fails. This can cause an
infinite loop as StmtMetadataLoader.loadTables() waits hopefully for
the catalog to send a new version of the table.

Change some Iceberg table loading methods to throw
IcebergTableLoadingException when a failure occurs. Prevent the hang by
substituting in an IncompleteTable if an IcebergTableLoadingException
occurs.

The test test_drop_incomplete_table had previously been disabled because
of IMPALA-11509. To re-enable this required a second change. The way
that DROP TABLE is executed on an iceberg table depends on which
Iceberg catalog is being used. If this Iceberg catalog is not a Hive
catalog then the execution happens in two parts, first the Iceberg
table is dropped, then the table is dropped in HMS. If this case, if
the drop fails in Iceberg, we should still continue on to perform the
drop in HMS.

TESTING

- Add a new test, originally developed for IMPALA-11330, which tests
  failures after deleting Iceberg metadata.
- Re-enable test_drop_incomplete_table().

Change-Id: I695559e21c510615918a51a4b5057bc616ee5421
Reviewed-on: http://gerrit.cloudera.org:8080/19509
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
2c77993

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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, Apache Kudu, Amazon S3, Azure Data Lake Storage, Apache Hadoop Ozone and more!
  • Wide analytic SQL support, including window functions and subqueries.
  • On-the-fly code generation using LLVM to generate lightning-fast code tailored specifically to each individual query.
  • Support for the most commonly-used Hadoop file formats, including Apache Parquet and Apache ORC.
  • Support for industry-standard security protocols, including Kerberos, LDAP and TLS.
  • Apache-licensed, 100% open source.

More about Impala

The fastest way to try out Impala is a quickstart Docker container. You can try out running queries and processing data sets in Impala on a single machine without installing dependencies. It can automatically load test data sets into Apache Kudu and Apache Parquet formats and you can start playing around with Apache Impala SQL within minutes.

To learn more about Impala as a user or administrator, or to try Impala, please visit the Impala homepage. Detailed documentation for administrators and users is available at Apache Impala documentation.

If you are interested in contributing to Impala as a developer, or learning more about Impala's internals and architecture, visit the Impala wiki.

Supported Platforms

Impala only supports Linux at the moment. Impala supports x86_64 and has experimental support for arm64 (as of Impala 4.0). Impala Requirements contains more detailed information on the minimum CPU requirements.

Supported OS Distributions

Impala runs on Linux systems only. The supported distros are

  • Ubuntu 16.04/18.04
  • CentOS/RHEL 7/8

Other systems, e.g. SLES12, may also be supported but are not tested by the community.

Export Control Notice

This distribution uses cryptographic software and may be subject to export controls. Please refer to EXPORT_CONTROL.md for more information.

Build Instructions

See Impala's developer documentation to get started.

Detailed build notes has some detailed information on the project layout and build.