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This patch fixes two issues around handling of constant expr args. The patches are combined because they touch some of the same code and depend on some of the same memory management cleanup. First, it fixes IMPALA-2379, where constant expr args were not visible to UDAFs. The issue is that the input exprs need to be opened before calling the UDAF Init() function. Second, it avoids overhead from repeated evaluation of constant arguments for ScalarFnCall expressions on both the codegen'd and interpreted paths. A common example is an IN predicate with a long list of constant values. The interpreted path was inefficient because it always evaluated all children expressions. Instead in this patch constant args are evaluated once and cached. The memory management of the AnyVal* objects was somewhat nebulous - adjusted it so that they're allocated from ExprContext::mem_pool_, which has the correct lifetime. The codegen'd path was inefficient only with varargs - with fixed arguments the LLVM optimiser is able to infer after inlining that the expressions are constant and remove all evaluation. However, for varargs it stores the vararg values into a heap-allocated buffer. The LLVM optimiser is unable to remove these stores because they have a side-effect that is visible to code outside the function. The codegen'd path is improved by evaluating varargs into an automatic buffer that can be optimised out. We also make a small related change to bake the string constants into the codegen'd code. Testing: Ran exhaustive build. Added regression test for IMPALA-2379 and MemPool test for aligned allocation. Added a test for in predicates with constant strings. Perf: Added a targeted query that demonstrates the improvement. Also manually validated the non-codegend perf. Also ran TPC-H and targeted perf queries locally - didn't see any significant changes. +--------------------+-------------------------------+-----------------------+--------+-------------+------------+-----------+----------------+-------------+-------+ | Workload | Query | File Format | Avg(s) | Base Avg(s) | Delta(Avg) | StdDev(%) | Base StdDev(%) | Num Clients | Iters | +--------------------+-------------------------------+-----------------------+--------+-------------+------------+-----------+----------------+-------------+-------+ | TARGETED-PERF(_20) | primitive_filter_in_predicate | parquet / none / none | 1.19 | 9.82 | I -87.85% | 3.82% | 0.71% | 1 | 10 | +--------------------+-------------------------------+-----------------------+--------+-------------+------------+-----------+----------------+-------------+-------+ (I) Improvement: TARGETED-PERF(_20) primitive_filter_in_predicate [parquet / none / none] (9.82s -> 1.19s [-87.85%]) +--------------+------------+----------+----------+------------+-----------+----------+----------+------------+--------+--------+-----------+ | Operator | % of Query | Avg | Base Avg | Delta(Avg) | StdDev(%) | Max | Base Max | Delta(Max) | #Hosts | #Rows | Est #Rows | +--------------+------------+----------+----------+------------+-----------+----------+----------+------------+--------+--------+-----------+ | 01:AGGREGATE | 14.39% | 155.88ms | 214.61ms | -27.37% | 2.68% | 163.38ms | 227.53ms | -28.19% | 1 | 1 | 1 | | 00:SCAN HDFS | 85.60% | 927.46ms | 9.43s | -90.16% | 4.49% | 1.01s | 9.50s | -89.42% | 1 | 13.77K | 14.05K | +--------------+------------+----------+----------+------------+-----------+----------+----------+------------+--------+--------+-----------+ Change-Id: I45c3ed8c9d7a61e94a9b9d6c316e8a53d9ff6c24 Reviewed-on: http://gerrit.cloudera.org:8080/4838 Reviewed-by: Tim Armstrong <tarmstrong@cloudera.com> Tested-by: Internal 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
./buildall.sh -notests
Export Control Notice
This distribution uses cryptographic software and may be subject to export controls. Please refer to EXPORT_CONTROL.md for more information.