ec31324eb5
Tables that are in unloaded state are represented as IncompleteTable. Table level metrics of them won't be used at all but occupy around 7KB of memory for each table. This is a significant amount comparing to the table name strings. This patch skips initializing these metrics for IncompleteTable to save memory usage. This reduces the initial memory requirement to launch catalogd. To avoid other codes unintentionally add new metrics to IncompleteTable, overrides all Table methods that use metrics_ to return simple results, e.g. IncompleteTable.getMedianTableLoadingTime() always returns 0. IncompleteTable.getMetrics() shouldn't be used. Added a Precondition check for this. Tests: - Verified in a heap dump file after loading 1.3M IncompleteTables that the heap usage reduces to 2GB and only few instances of com.codahale.metrics.Timer are created. Previously catalogd OOM in a heap size of 18GB when running global IM, and the number of com.codahale.metrics.Timer instances is similar to the number of IncompleteTables. - Passed CORE tests. Change-Id: If0fcfeab99bbfbefe618d0abf7f2482a0cc5ef9f Reviewed-on: http://gerrit.cloudera.org:8080/23547 Reviewed-by: Riza Suminto <riza.suminto@cloudera.com> Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Reviewed-by: Michael Smith <michael.smith@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.