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This moves away from the PipelinedPlanNodeSet approach of enumerating sets of concurrently-executing nodes because unions would force creating many overlapping sets of nodes. The new approach computes the peak resources during Open() and the peak resources between Open() and Close() (i.e. while calling GetNext()) bottom-up for each plan node in a fragment. The fragment resources are then combined to produce the query resources. The basic assumptions for the new resource estimates are: * resources are acquired during or after the first call to Open() and released in Close(). * Blocking nodes call Open() on their child before acquiring their own resources (this required some backend changes). * Blocking nodes call Close() on their children before returning from Open(). * The peak resource consumption of the query is the sum of the independent fragments (except for the parallel join build plans where we can assume there will be synchronisation). This is conservative but we don't synchronise fragment Open() and Close() across exchanges so can't make stronger assumptions in general. Also compute the sum of minimum reservations. This will be useful in the backend to determine exactly when all of the initial reservations have been claimed from a shared pool of initial reservations. Testing: * Updated planner tests to reflect behavioural changes. * Added extra resource requirement planner tests for unions, subplans, pipelines of blocking operators, and bushy join plans. * Added single-node plans to resource-requirements tests. These have more complex plan trees inside a single fragment, which is useful for testing the peak resource requirement logic. Change-Id: I492cf5052bb27e4e335395e2a8f8a3b07248ec9d Reviewed-on: http://gerrit.cloudera.org:8080/7223 Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com> Tested-by: Impala Public Jenkins
Welcome to Impala
Lightning-fast, distributed SQL queries for petabytes of data stored in Apache Hadoop clusters.
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 HDFS, Apache HBase and Amazon S3.
- Wide analytic SQL support, including window functions and subqueries.
- On-the-fly code generation using LLVM to generate CPU-efficient code tailored specifically to each individual query.
- Support for the most commonly-used Hadoop file formats, including the Apache Parquet (incubating) project.
- Apache-licensed, 100% open source.
More about Impala
To learn more about Impala as a business user, or to try Impala live or in a VM, please visit the Impala homepage.
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.
Build Instructions
See bin/bootstrap_build.sh.
Export Control Notice
This distribution uses cryptographic software and may be subject to export controls. Please refer to EXPORT_CONTROL.md for more information.