1008decc07
StmtMetadataLoader.getMissingTables() load missing tables in serial manner. In local catalog mode, large number of serial table loading can incur significant round trip latency to CatalogD. This patch parallelize the table loading by using executor service to lookup and gather all non-null FeTables from given TableName set. Modify LocalCatalog.loadDbs() and LocalDb.loadTableNames() slightly to make it thread-safe. Change FrontendProfile.Scope to support nested scope referencing the same FrontendProfile instance. Added new flag max_stmt_metadata_loader_threads to control the maximum number of threads to use for loading table metadata during query compilation. It is deafult to 8 threads per query compilation. If there is only one table to load, max_stmt_metadata_loader_threads set to 1, or RejectedExecutionException raised, fallback to load table serially. Testing: Run and pass few tests such as test_catalogd_ha.py, test_concurrent_ddls.py, and test_observability.py. Add FE tests CatalogdMetaProviderTest.testProfileParallelLoad. Manually run following query and observe parallel loading by setting TRACE level log in CatalogdMetaProvider.java. use functional; select count(*) from alltypesnopart union select count(*) from alltypessmall union select count(*) from alltypestiny union select count(*) from alltypesagg; Change-Id: I97a5165844ae846b28338d62e93a20121488d79f Reviewed-on: http://gerrit.cloudera.org:8080/23436 Reviewed-by: Quanlong Huang <huangquanlong@gmail.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.