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Thank you @gatorsmile and @viirya.
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Github user ssaavedra commented on the issue:
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I can probably take a look at testing this over 2.3.0-rc2 on Monday. I did
not test this on a clean 2.3.0-ish branch.
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retest this please.
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ping @aa8y
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@heary-cao have you benchmarked this? The reason I am asking is because
Spark SQL chains iterators, these are pipelined and only materialized when we
need to. Your PR effectively removes two
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@cloud-fan @gatorsmile
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Merged to master and branch-2.3.
Thank you @srowen, @viirya and @felixcheung.
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--- Diff: docs/sql-programming-guide.md ---
@@ -1640,6 +1640,133 @@ Configuration of Hive is done by placing your
`hive-site.xml`, `core-site.xml` a
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--- Diff: docs/sql-programming-guide.md ---
@@ -1640,6 +1640,133 @@ Configuration of Hive is done by placing your
`hive-site.xml`, `core-site.xml` a
GitHub user CCInCharge opened a pull request:
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[SPARK-23250][DOCS] Typo in JavaDoc/ScalaDoc for DataFrameWriter
## What changes were proposed in this pull request?
Fix typo in ScalaDoc for DataFrameWriter - originally stated "This
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Just for context, I'm seeing RDD.repartition being used *a lot*.
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retest this please.
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retest this please.
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@atallahhezbor Yeah! Please help us improve the test coverage. We do not
have a clear way to test the functionality in `SparkExecuteStatementOperation`
Adding unit test cases for
Github user gatorsmile commented on the issue:
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Also cc @wzhfy Do you have a bandwidth to review PRs?
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--- Diff: sql/core/src/test/resources/tpcds-v2.7.0/q11.sql ---
@@ -0,0 +1,78 @@
+with year_total as (
+ select c_customer_id customer_id
+
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ping @conorbmurphy
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LGTM pending Jenkins
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Also cc @dongjoon-hyun
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ok to test
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Thanks! Merged to master/2.3
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Github user mridulm commented on the issue:
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@sameeragarwal I am not sure if we can make shuffle fetch deterministic -
without quite a lot of perf overhead; do you have any thoughts on how to do
this in case I am missing something here ?
Github user mridulm commented on the issue:
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In addition, any use of random in spark code will get affected by this -
unless input is an idempotent source; even if random initialization is done
predictably with the partition index (which we
Github user felixcheung commented on the issue:
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So you have tested this on latest Spark 2.3.0 bit?
Test aside, do people think it is useful to include this fix in the 2.3.0
release?
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