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Github user holdenk commented on the pull request:
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@viirya - I think having consistent accumulators only be consistent inside
some of the Spark operations might be a little confusing, although that's also
where I
Github user viirya commented on the pull request:
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I am thinking that if we can support the semantics that the accumulator
updates in anyone RDD will not be recounted. Is it already enough? So we don't
need to
Github user squito commented on the pull request:
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+1 on "data property" instead of consistent
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--- Diff:
core/src/test/scala/org/apache/spark/DataPropertyAccumulatorSuite.scala ---
@@ -0,0 +1,361 @@
+/*
+ * Licensed to the Apache Software
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Github user holdenk commented on the pull request:
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Sounds reasonable, I'll rename things to "data property" from consistent :)
Let me know if you want to chat about the API together at some point.
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Github user rxin commented on the pull request:
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I"m going to think about the user facing API in the next few days. For this
one, can you do one change first? Have only a single flag that controls whether
this is a data
Github user holdenk commented on the pull request:
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I think with the changes so far, it might be good to change to a regular PR
from WIP/RFC - any other things people think I should address before that?
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Github user holdenk commented on the pull request:
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I think the change with only wrapping the iterators with consistent
accumulators present in the task should address the performance concerns @rxin
had (or we can
Github user squito commented on a diff in the pull request:
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--- Diff: core/src/main/scala/org/apache/spark/Accumulable.scala ---
@@ -114,23 +163,40 @@ class Accumulable[R, T] private (
* same mutable instance
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--- Diff: core/src/main/scala/org/apache/spark/Accumulable.scala ---
@@ -53,42 +54,83 @@ import org.apache.spark.util.Utils
*
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So I did a bit of a change so we only wrap the iterators if the task
involved has a consistent accumulable.
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BTW the other thing I really don't like is the current accumulator user
facing API. It is way too complicated with implicit parameters etc, making it
unusable in Java and
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--- Diff: core/src/main/scala/org/apache/spark/Accumulable.scala ---
@@ -146,6 +212,32 @@ class Accumulable[R, T] private (
def merge(term: R) {
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Sorry for the confusiong. When I said "current accumulator user facing
API", I literally meant the current one in Spark master branch right now.
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--- Diff: core/src/main/scala/org/apache/spark/Accumulable.scala ---
@@ -146,6 +212,32 @@ class Accumulable[R, T] private (
def merge(term: R) { value_
Github user holdenk commented on the pull request:
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@squito so I don't really care about a partial partition process - but a
take(1) on a cached RDD for example will fully evaluate one full partition that
won't be
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}
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--- Diff:
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@@ -0,0 +1,284 @@
+/*
+ * Licensed to the Apache Software
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--- Diff: core/src/main/scala/org/apache/spark/util/collection/Utils.scala
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@@ -36,4 +36,21 @@ private[spark] object Utils {
}
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--- Diff: core/src/main/scala/org/apache/spark/Accumulable.scala ---
@@ -114,23 +163,40 @@ class Accumulable[R, T] private (
* same mutable instance
Github user rxin commented on the pull request:
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We have recently changed accumulators so they can satisfy the use case of
metrics (e.g. counting the size of bytes read) in
Github user squito commented on a diff in the pull request:
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@@ -0,0 +1,284 @@
+/*
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I'm not sure why MiMa is failing on private methods but I'll add the
exclusions.
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@rxin So the implicit params are the same ones used by the current regular
accumulator API. So regardless of the name "consistent" - do you think having
something for
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Oh ok yes, I agree I think the current one in master could certainly be
improved - I didn't want to try and touch that at the same time as this though
because its
Github user squito commented on the pull request:
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@rxin totally understand that this might have an unacceptable impact on
performance. That part remains to be explored. For now the focus has mostly
been on trying to
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--- Diff: core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala
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@@ -329,6 +334,20 @@ class TaskMetrics private[spark] (initialAccums:
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just thinking aloud here -- it seems like the implementation is complicated
significantly by trying to support counters when you only partially read
partitions, eg.
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Github user holdenk commented on the pull request:
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@squito some progress on a few of the suggestions (not all for sure).
Wrapping the generic compute function sounds like a good idea I'll do that next
(will still need
Github user holdenk commented on a diff in the pull request:
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--- Diff: core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
---
@@ -66,7 +67,10 @@ private[spark] class ResultTask[T, U](
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/11105#issuecomment-195913335
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