[SPARK-14628][WIP] Simplify task metrics by always tracking read/write metrics #12388
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rxin
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Test build #55806 has finished for PR 12388 at commit
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rxin
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rxin
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Test build #55862 has finished for PR 12388 at commit
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Test build #55865 has finished for PR 12388 at commit
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Test build #55880 has finished for PR 12388 at commit
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| (taskMetrics.shuffleReadMetrics.map(_.totalBytesRead).getOrElse(0L) | ||
| - oldMetrics.flatMap(_.shuffleReadMetrics).map(_.totalBytesRead).getOrElse(0L)) | ||
| taskMetrics.shuffleReadMetrics.totalBytesRead | ||
| - oldMetrics.map(_.shuffleReadMetrics.totalBytesRead).getOrElse(0L) |
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haha, finally understand why this is wrong, it's because
val i =
a
- b
equals
val i = a
-b
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and previously it's
val i =
(a
-b)
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oh come on... no one likes mandatory semi-colons right? 😜
Does this case not even generate a warning? I guess it doesn't know your .map is a pure expression.
asfgit
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Apr 15, 2016
…te metrics ## What changes were proposed in this pull request? Part of the reason why TaskMetrics and its callers are complicated are due to the optional metrics we collect, including input, output, shuffle read, and shuffle write. I think we can always track them and just assign 0 as the initial values. It is usually very obvious whether a task is supposed to read any data or not. By always tracking them, we can remove a lot of map, foreach, flatMap, getOrElse(0L) calls throughout Spark. This patch also changes a few behaviors. 1. Removed the distinction of data read/write methods (e.g. Hadoop, Memory, Network, etc). 2. Accumulate all data reads and writes, rather than only the first method. (Fixes SPARK-5225) ## How was this patch tested? existing tests. This is bases on #12388, with more test fixes. Author: Reynold Xin <rxin@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Closes #12417 from cloud-fan/metrics-refactor.
lw-lin
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Apr 20, 2016
…te metrics ## What changes were proposed in this pull request? Part of the reason why TaskMetrics and its callers are complicated are due to the optional metrics we collect, including input, output, shuffle read, and shuffle write. I think we can always track them and just assign 0 as the initial values. It is usually very obvious whether a task is supposed to read any data or not. By always tracking them, we can remove a lot of map, foreach, flatMap, getOrElse(0L) calls throughout Spark. This patch also changes a few behaviors. 1. Removed the distinction of data read/write methods (e.g. Hadoop, Memory, Network, etc). 2. Accumulate all data reads and writes, rather than only the first method. (Fixes SPARK-5225) ## How was this patch tested? existing tests. This is bases on apache#12388, with more test fixes. Author: Reynold Xin <rxin@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Closes apache#12417 from cloud-fan/metrics-refactor.
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What changes were proposed in this pull request?
Part of the reason why TaskMetrics and its callers are complicated are due to the optional metrics we collect, including input, output, shuffle read, and shuffle write. I think we can always track them and just assign 0 as the initial values. It is usually very obvious whether a task is supposed to read any data or not. By always tracking them, we can remove a lot of map, foreach, flatMap, getOrElse(0L) calls throughout Spark.
This patch also changes a few behaviors.
How was this patch tested?
Existing unit tests.