Github user asfgit closed the pull request at:
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57683434
I committed this. I missed there wasn't a jira here so filed
https://issues.apache.org/jira/browse/SPARK-3768.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57705659
Thanks @tgravescs
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57518030
retest this please
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57326448
@andrewor14 did you have any further comments on this?
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Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57341483
I think this is fine. I spotted one semicolon but I'll let that go. LGTM.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57360651
Semicolon removed (nice catch)
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56979002
It seems a bit much to have 2 configs to do essentially same thing. I see
it leading to confusion and just extra overhead.
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Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56987453
Yes I'm inclined towards having only one config, and beefing up the
documentation and comments on the existing one.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57001251
@andrewor14 I had added a few comments in code/docs yday. Not sure if you
got a chance to take a look. If there is anything specific (in terms of
comments) you'd
Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57001360
Hey @nishkamravi2 yes I just looked at the latest changes and the existing
comments are good. Any other comments by others?
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Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57001461
retest this please
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57001671
Can you refactor this to be non-YARN specific? It would be good to share
code between this and #2401.
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57001899
In particular, look at how I put the logic into a common function,
`calculateTotalMemory`.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57002615
Not sure what you mean by non-yarn specific. The two code bases are quite
different and memoryOverhead is too small of an intersection to try and unify
them. I
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57002734
Calculate totalMemory can be differently defined for the two code paths.
The overhead percentage will have to be different too. As long as they follow
the same
Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57004118
Why can't they both share the same config parameters, for example? I
understand the implementation differences, but we shouldn't need to have
distinct config
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57004722
For one, it would mean a change in the UI, which breaks existing
deployments and there should be a compelling reason to do so.
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Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57004940
So I guess there's nothing to do.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57005579
I think PR #2401 can be modeled after this one. Instead of defining
overhead as a percentage, it could (and probably should) be defined as an
absolute value. Also,
Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57006489
Naturally you wouldn't want to have to change yours.
I'll drop the `.minimum` thing, and prefix the config params with `.mesos`,
like you've done for yarn.
Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57007876
Hey I just talked to @pwendell about this. I think it's better for us to
have a yarn config and a mesos config, but not generalize this to use a common
Github user brndnmtthws commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57008082
That's fair. I'm updating the PR to make that Mesos specific now.
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Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57009912
retest this please
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57018879
Need some help interpreting the test results. Not clear which one is
failing.
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Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-57029756
It's the python ones. This is unlikely to be related to your patch. Let's
retest this please.
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56816028
Jenkins, retest this please
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56818674
@JoshRosen would you mind kicking jenkins again now that its upmerged?
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Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/2485#discussion_r18049289
--- Diff:
yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala ---
@@ -64,14 +64,18 @@ private[spark] trait ClientBase extends Logging
Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/2485#discussion_r18049314
--- Diff:
yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala ---
@@ -64,14 +64,18 @@ private[spark] trait ClientBase extends Logging
Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/2485#discussion_r18049333
--- Diff:
yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala ---
@@ -64,14 +64,18 @@ private[spark] trait ClientBase extends Logging
Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/2485#discussion_r18049458
--- Diff:
yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
---
@@ -39,15 +39,19 @@ private[spark] class
Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/2485#discussion_r18049502
--- Diff:
yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala ---
@@ -117,9 +118,10 @@ private[yarn] abstract class YarnAllocator(
Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56863597
Have we ever come to a consensus on whether 0.07 is an appropriate default?
Under the current settings this means anything above ~5.5G of executor / driver
memory
Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56865310
@nishkamravi2 arrived at this through experimentation. He had a few
details on his experiments on the previous incarnation of this PR #1391 . If
anything, I think 0.07 is
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56875798
Updated as per @andrewor14 's comments.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56876371
As Sandy points out, 7% is on the conservative side. In the interest of
minimizing memory waste while covering the common cases (as per our
experiments). Anything
Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/2485#discussion_r18057379
--- Diff: out ---
@@ -0,0 +1 @@
+Already up-to-date.
--- End diff --
Wait, can you delete this file?
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Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56878538
I see. Maybe it makes sense to at least add a comment (in the documentation
and in the code) to explain how we arrived at these numbers. This config can't
be
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56887022
@andrewor14 Which config are we referring to? spark.yarn.*.memoryOverhead
is configurable.
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Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56889393
The static value (MB) is configurable, but the user can't specify 15% or
20% but is stuck with 7%. This is probably fine.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56891420
I see. Yeah, we have the choice to expose the config parameter relative to
the container size or as an absolute value. Since memoryOverhead as a function
of the
Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56892181
We could also expose both and make memoryOverhead override the other. I
think this could be reasonable because the scale is most likely to be set in
spark-defaults.conf
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56893439
Not a bad suggestion Sandy, but I would be wary of the potential confusion
it may create. Ideally this parameter should not be exposed as a config
parameter at all
Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56706929
@pwendell @mateiz @andrewor14 can any of you kick jenkins?
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Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56707385
I just kicked it from the `spark-prs` parameterized build trigger; let's
wait and see if it starts...
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Github user SparkQA commented on the pull request:
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56707989
ah sorry, looks like something conflicts now and it needs upmerged.
@nishkamravi2 can you please upmerge
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56762805
Updated and merged.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56763002
ClientBase changes are now distributed over ClientBase and ClientArguments.
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Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56480298
This looks good to me.
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56526845
Jenkins, retest this please.
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56522491
Jenkins, test this please
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56532051
@JoshRosen Any idea why Jenkins isn't running on this? Could you kick it
manually?
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GitHub user nishkamravi2 opened a pull request:
https://github.com/apache/spark/pull/2485
Modify default YARN memory_overhead-- from an additive constant to a
multiplier
Redone against the recent master branch
(https://github.com/apache/spark/pull/1391)
You can merge this pull
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56334768
Can one of the admins verify this patch?
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56334843
Have redone the PR against the recent master branch, which has undergone
significant structural changes for Yarn. Addressed review comments and changed
the
Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56342496
If #2485 is the replacement, can we close this one out?
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Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/2485#discussion_r17837060
--- Diff:
yarn/common/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala ---
@@ -117,9 +118,10 @@ private[yarn] abstract class YarnAllocator(
Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56343225
It would also be nice to log what it is when we fail to get a container
large enough or it fails due to the cluster max allocation limit was hit.
@tgravescs I
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56345871
Updated as per @sryza 's comments
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56346539
Shall we let this linger on for just a bit until the other one gets merged?
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Github user nishkamravi2 closed the pull request at:
https://github.com/apache/spark/pull/1391
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56347057
Noticed that we have a reference to this one in 2485, closing it out.
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56371027
yes it would be nice to tell the user what the overhead limit is calculated
to be as I might not realize there is overhead and that its dependent upon the
multiplier.
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/2485#issuecomment-56457534
Updated as per @tgravescs 's comments
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56142506
@sryza Thanks Sandy. Will do.
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56174217
@mridulm any comments?
I'm ok with it if its a consistent problem for users. One thing we
definitely need to do is document it and possibly look at including
Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/1391#discussion_r17762675
--- Diff:
yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
---
@@ -92,7 +92,7 @@ private[yarn] class
Github user andrewor14 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56119506
What is the current state of this PR? @tgravescs @mridulm any more thoughts
about the current approach? This is a related PR for mesos and I'm wondering if
we can use
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56120931
Updated as per @andrewor14 's comments
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Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56132524
@nishkamravi2 mind resolving the merge conflicts?
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Github user sryza commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-56132497
These changes look good to me. This addresses what continues to be the #1
issue that we see in Cloudera customer YARN deployments. It's worth
considering boosting this
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-54694595
Can one of the admins verify this patch?
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Github user tgravescs commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-49480064
I'll let mridul comment on this but I think adding a comment where 0.06
came from would be useful.
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Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-49483642
6% was experimentally obtained (with the goal of keeping the bound as tight
as possible without the containers crashing). Three workloads were experimented
with:
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-49348179
Bringing the discussion back online. Thanks for all the input so far.
Ran a few experiments yday and today. Number of executors (which was the
other main
Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835312
We have gone over this in the past .. it is suboptimal to make it a linear
function of executor/driver memory.
Overhead is a function of number of executors,
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835447
That makes sense, but then it doesn't explain why a constant amount works
for a given job when executor memory is low, and then doesn't work when it is
high. This has
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835560
Sean, the memory_overhead is fairly substantial. More than 2GB for a 30GB
executor. Less than 400MB for a 2GB executor.
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Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835566
The default constant is actually a lowerbound to account for other
overheads (since yarn will aggressively kill tasks)... Unfortunately we
have not sized this
Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835618
That would be a function of your jobs.
Other apps would have a drastically different characteristics ... Which is
why we can't generalize to a simple fraction of
Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835656
The basic issue is you are trying to model overhead using the wrong
variable... It has no correlation on executor memory actually (other than
vm overheads as heap
You are lucky :-) for some of our jobs, in a 8gb container, overhead is
1.8gb !
On 13-Jul-2014 2:40 pm, nishkamravi2 g...@git.apache.org wrote:
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835560
Sean, the
Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835727
Yes of course, lots of settings' best or even usable values are ultimately
app-specific. Ideally, defaults work for lots of cases. A flat value is the
simplest of models,
Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835769
You are lucky :-) for some of our jobs, in a 8gb container, overhead is
1.8gb !
On 13-Jul-2014 2:41 pm, nishkamravi2 notificati...@github.com wrote:
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835852
Experimented with three different workloads and noticed common patterns of
proportionality.
Other parameters were left unchanged and only executor size was
Github user nishkamravi2 commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835881
That's why the parameter is configurable. If you have jobs that cause
20-25% memory_overhead, default values will not help.
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