| commit | a6ee96f95a9608046b773b2ed8493bdcb4282463 | [log] [tgz] |
|---|---|---|
| author | Nathan Harmata <nharmata@google.com> | Tue Mar 14 17:22:12 2017 +0000 |
| committer | Yun Peng <pcloudy@google.com> | Tue Mar 14 19:52:02 2017 +0000 |
| tree | 50bdabf7602f2da7f6d5a3f321ace389557a67c4 | |
| parent | 4ccabd395a591e85abf108b757f994c184b87d61 [diff] |
Fix inadvertent performance regression introduced by the recent rewrite of 'blaze query'. The "streaming" callbacks used by some query functions, e.g. 'deps', make calls to QueryEnvironment#buildTransitiveClosure. For a cold blaze server, these calls do package loading via LabelVisitor (which calls into Skyframe via a top-level #evaluate call). So we'd prefer a single massive call which can make full use of blaze's loading-phase parallelism via Skyframe over a bunch of sequential small calls. For a hot blaze server, there are two problems: (1) LabelVisitor's meager up-to-date check isn't useful (as in we cannot reuse old visitations) when we do a whole bunch of small visitations instead of one massive one. (2) The actual work of the LabelVisitor (building up a portion of a temporary graph) isn't being effectively parallelized when we do it sequentially in small chunks. This issue is yet another subtle reason why the old BlazeQueryEnvironment#eval made sense (and why it was unfortunately not compatible with the streaming query evaluation model from the beginning). -- PiperOrigin-RevId: 150081619 MOS_MIGRATED_REVID=150081619
{Fast, Correct} - Choose two
Bazel is a build tool that builds code quickly and reliably. It is used to build the majority of Google‘s software, and thus it has been designed to handle build problems present in Google’s development environment, including:
A massive, shared code repository, in which all software is built from source. Bazel has been built for speed, using both caching and parallelism to achieve this. Bazel is critical to Google's ability to continue to scale its software development practices as the company grows.
An emphasis on automated testing and releases. Bazel has been built for correctness and reproducibility, meaning that a build performed on a continuous build machine or in a release pipeline will generate bitwise-identical outputs to those generated on a developer's machine.
Language and platform diversity. Bazel's architecture is general enough to support many different programming languages within Google, and can be used to build both client and server software targeting multiple architectures from the same underlying codebase.
Find more background about Bazel in our FAQ.