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[SPARK-20781] the location of Dockerfile in docker.properties.templat is
wrong
[https://issues.apache.org/jira/browse/SPARK-20781](https://issues.apache.org/jira/browse/SPARK-20781)
the
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@ueshin Yes, I will do it ,thanks
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I guess this would also affect date-related functions such as `dayofyear`,
`year`, `quater` and `weekofyear`, etc.
Could you add tests for them?
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}
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thanks, merging to master/2.2!
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I've been running some local tests of scheduler throughput to make sure
that this doesn't adversely affect performance in the processing of task
completion events (I discovered the perf. hotspot
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Thanks @cloud-fan @hvanhovell @gatorsmile @wzhfy
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@cloud-fan Yeah, I change the aliased name in one test. I tested it locally.
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oops, you pushed a new commit... But it should be fine as the change is
very small.
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thanks, merging to master!
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Sure.
On May 17, 2017 12:10 PM, "Wenchen Fan" wrote:
> *@cloud-fan* commented on this pull request.
> --
>
> In
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--- Diff: docs/configuration.md ---
@@ -954,16 +970,16 @@ Apart from these, the following properties are also
available, and may be useful
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Github user viirya commented on the issue:
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ping @gatorsmile Can you help review this? Thanks.
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Thanks, I add a row to the table `t` and now we can do nonEmpty check on
table `tabNullType`.
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Can one of the admins verify this patch?
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[SPARK-20779][Examples]The ASF header placed in an incorrect location in
some files.
## What changes were proposed in this pull request?
The license is not at the top in some files.
Github user wzhfy commented on a diff in the pull request:
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Github user auskalia commented on the issue:
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Hi @mpjlu , your are right. But I consider that sometimes we have to use
several spark mission to finish our work, especially the resource is
insufficient in hadoop cluster. Due to save and reload
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Hi @auskalia , you are right. repartition can improve the performance of
recommendForAll.
In my experiment for PR 17742, I have 120 cores, I use 20 partition for
userFeatures, and itemFeatures.
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@sethah Sorry for the late response. Setting as WIP. We have performance
data for dense features, data for the sparse feature will be ready soon. thanks.
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18008
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Github user auskalia commented on the issue:
https://github.com/apache/spark/pull/17919
Hi, @MLnick, We find that just do repartition for userFeatures and
productFeatures can improve the efficiency significantly on the ALS
recommendForAll().
Here is our procedure:
1.
Github user AmplabJenkins commented on the issue:
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