a8618c6a65
The admission request may contain the lineage graphs and other stuff that the admission control service doesn't need. For example, currently the admission controller service would hold onto the full TQueryExecRequest object for the entire lifetime of a query, even after the admission decision was complete. This led to unnecessary memory consumption. This commit introduces two optimizations for reducing the memory footprint: 1. A lightweight copy of TQueryExecRequest is now created on the client side before sending to the admission control service. Fields that are not required for admission decisions (e.g., query_plan, lineage_graph) are cleared from this copy. 2. The AdmissionState now uses a unique_ptr to manage the TQueryExecRequest. This allows the object's memory to be explicitly released as soon as the query schedule is generated and the request object is no longer needed. During a customized high concurrent TPCDS run, without the change, the peak memory usage in admissiond was around 2GB. With this change, it required less than half that memory. Tests: Passed exhaustive tests. Change-Id: I1ba5e8818336bd1fc3ad604a0acee5eb7a1116c4 Reviewed-on: http://gerrit.cloudera.org:8080/23546 Reviewed-by: Michael Smith <michael.smith@cloudera.com> Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Reviewed-by: Abhishek Rawat <arawat@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.