42e5ea7ea3
Imported query profiles are currently being stored in IndexedDB. Although IndexedDB does not have storage limitations like other browser storage APIs, there is a storage limit for a single attribute / field. For supporting larger query profiles, 'pako' compression library's v2.1.0 has been added along with its associated license. Before adding query profile JSON to indexedDB, it undergoes compression using this library. As compression and parsing profile is a long running process that can block the main thread, this has been delegated to a worker script running in the background. The worker script returns parsed query attributes and compressed profile text sent to it. The process of compression consumes time; hence, an alert message is displayed on the queries page warning user to refrain from closing or reloading the page. On completion, the raw total size, compressed total size, and total processing time are logged to the browser console. When multiple profiles are chosen, after each query profile insertion, the subsequent one is not triggered until compression and insertion are finished. The inserted query profile field is decompressed before parsing on the query plan, query profile, query statement, and query timeline page. Added tests for the compression library methods utilized by the worker script. Manual testing has been done on Firefox 126.0.1 and Chrome 126.0.6478. Change-Id: I8c4f31beb9cac89051460bf764b6d50c3933bd03 Reviewed-on: http://gerrit.cloudera.org:8080/21463 Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com> Tested-by: Impala Public Jenkins <impala-public-jenkins@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.