c5072807df
This patch implements delete orphan files query for Iceberg table. The following statement becomes available for Iceberg tables: - ALTER TABLE <tbl> EXECUTE remove_orphan_files(<timestamp>) The bulk of implementation copies Hive's implementation of org.apache.iceberg.actions.DeleteOrphanFiles interface (HIVE-27906, 6b2e21a93ef3c1776b689a7953fc59dbf52e4be4), which this patch rename to ImpalaIcebergDeleteOrphanFiles.java. Upon execute(), ImpalaIcebergDeleteOrphanFiles class instance will gather all URI of valid data files and Iceberg metadata files using Iceberg API. These valid URIs then will be compared to recursive file listing obtained through Hadoop FileSystem API under table's 'data' and 'metadata' directory accordingly. Any unmatched URI from FileSystem API listing that has modification time less than 'olderThanTimestamp' parameter will then be removed via Iceberg FileIO API of given Iceberg table. Note that this is a destructive query that will wipe out any files within Iceberg table's 'data' and 'metadata' directory that is not addressable by any valid snapshots. The execution happens in CatalogD via IcebergCatalogOpExecutor.alterTableExecuteRemoveOrphanFiles(). CatalogD supplied CatalogOpExecutor.icebergExecutorService_ as executor service to execute the Iceberg API planFiles and FileIO API for deletion. Also fixed toSql() implementation for all ALTER TABLE EXECUTE queries. Testing: - Add FE and EE tests. Change-Id: I5979cdf15048d5a2c4784918533f65f32e888de0 Reviewed-on: http://gerrit.cloudera.org:8080/23042 Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Reviewed-by: Zoltan Borok-Nagy <boroknagyz@cloudera.com>
Welcome to Impala
Lightning-fast, distributed SQL queries for petabytes of data stored in open data and table formats.
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 Apache Iceberg, 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.