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Thanks @HyukjinKwon for your help.
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I think I made a duplicated effort .. thanks for taking this in.
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I get the problem that org.apache.spark.SparkException: Exception thrown in
awaitResult .how can I handle the problem
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@cloud-fan Yea you provide a more clear statement here, and I totally agree!
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> Not quite - coalesce will not combine partitions across executors (aka
shuffle) so you could still end up having many many files.
I'm not sure if I follow here. For `coalesce(1)` Spark
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@felixcheung You are right that I didn't make it clear there should be
still many shuffle blocks, and if you have the read task retried it should be
slower than using `repartition(1)` directly.
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--- Diff: python/pyspark/sql/session.py ---
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LGTM except a few comments. also cc @rdblue
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/**
* An interface to
Github user felixcheung commented on the issue:
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> Actually for the first case, you shall use coalesce() instead of
repartition() to get a similar effect, without need of another shuffle!
Not quite - coalesce will not combine partitions
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/**
* An interface to
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@@ -63,7 +63,7 @@
StructType readSchema();
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StructType readSchema();
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@@ -50,7 +50,7 @@
}
/**
- *
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@@ -22,19 +22,19 @@
import
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/**
* A concrete
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--- Diff: docs/sql-programming-guide.md ---
@@ -1640,6 +1640,133 @@ Configuration of Hive is done by placing your
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LGTM, and +1 on @viirya 's idea. I like it better for the comment to be on
top of the class declaration instead of inside it; but I'm okay either way if
others have strong opinion otherwise. As
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Talked to @yanboliang offline, he claimed that the major use cases of
RDD/DataFrame.repartition() in ml workloads he has observed are:
1. During save models, you may need `repartition()` to
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@@ -1640,6 +1640,129 @@ Configuration of Hive is done by placing your
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--- Diff: python/pyspark/cloudpickle.py ---
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Cool, I can take a gander at this tomorrow (beyond my limited skim just
now).
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CC @ajbozarth .\
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retest this please
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`org.apache.spark.sql.execution.datasources.orc.OrcQuerySuite.(It is not a
test it is a sbt.testing.SuiteSelector)` is another flaky test
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*/
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LGTM too.
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LGTM
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private[sql]
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*/
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please update your title to `[SPARK-23249][SQL] ...`
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LGTM, also cc @hvanhovell @marmbrus
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thanks, merging to master/2.3!
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import
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LGTM otherwise. @ueshin, I assume you were following this too. Did you had
a change to take a look?
Also, @rgbkrk, I think it would be great to have your look too .. It's
basically a
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Github user HyukjinKwon commented on the issue:
https://github.com/apache/spark/pull/20373
I took a quick look for the commits and seems we should backport
https://github.com/cloudpipe/cloudpickle/pull/145 too as looks introduced from
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