Use stream instead of parallelStream #437
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Ran it on our codebase Before: After Looks good |
kageiit
suggested changes
May 4, 2017
buildSrc/build.gradle
Outdated
| @@ -46,7 +46,7 @@ task sourcesJar(type: Jar) { | |||
| classifier = 'sources' | |||
| } | |||
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| def publishVersion = '0.20.7' | |||
| def publishVersion = '0.20.8-SNAPSHOT' | |||
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Isn't it typical to bump the version to the next release snapshot? This makes it easier to test changes locally.
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you can do so if you want to locally. Even if we wanted to start doing that, it should be a separate change
The overhead of creating threads for very small tasks might not be faster than performing the same tasks serially. I ran a benchmark using this commit against our repo. I killed the gradle daemon and then ran the okbuck task 3 times. Before: using parallel() 20.353 secs 6.644 secs 5.89 secs After: using stream() 18.539 secs 6.316 secs 5.613 secs The results were quite similar with and without parallel stream. I'm wondering if there is any noticeable difference with even larger projects.
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Removed snapshot version! |
cwoodwar6
pushed a commit
to cwoodwar6/okbuck
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this pull request
May 10, 2017
* uber/master: Use stream instead of parallelStream (uber#437) Add empty resource rule if using resource union. (uber#438) Publich v0.20.7 Move okbuck task to its own gradle task class. (uber#436) Use try-with-resources (uber#432) Update README.md Do not allow okbuck to be run without okbuck.wrapper set to true. (uber#433) Set descriptions in tasks class instead of when creating them. (uber#431) Move cleaning the .okbuck/gen folder to the first step of setupOkbuck. (uber#430) Publish v0.20.6 Add support for downloading 22-25 API robolectric runtime dependencies. (uber#427) Skip okbuck if we just want to kill the buck daemon. (uber#425)
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The overhead of creating threads for very small tasks might not be
faster than performing the same tasks serially.
I ran a benchmark using this commit against our repo. I killed the
gradle daemon and then ran the okbuck task 3 times.
Before: using parallel()
20.353 secs
6.644 secs
5.89 secs
After: using stream()
18.539 secs
6.316 secs
5.613 secs
The results were quite similar with and without parallel stream. I'm
wondering if there is any noticeable difference with even larger projects.