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> Visualizing a workflow is nice, but Spark's Pipelines are typically
pretty straightforward and linear. I could imagine producing a nicer
visualization than what you get from reading the Spark
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--- Diff:
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@@ -774,4 +774,23 @@ class InsertSuite extends QueryTest with
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Retest this please.
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Please update the title `[MINOR][DOC] Update the condition description of
serialized shuffle`
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Okey.@dongjoon-hyun
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Ok, got it. @vanzin or @squito or others would be better able to evaluate.
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@srowen
For now executor log url is **static** in Spark, which forces Node Manager
to be alive even after application is finished, in order to provide executor
log in SHS.
This
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ok to test
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Can one of the admins verify this patch?
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[SPARK-25401] [SQL] Reorder join predicates to match child outputOrdering
## What changes were proposed in this pull request?
In case of SortMergeJoin if tables are bucketed with keys
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--- Diff: sql/core/src/main/java/org/apache/spark/sql/sources/v2/Table.java
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@@ -18,9 +18,6 @@
package org.apache.spark.sql.sources.v2;
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--- Diff:
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@@ -20,6 +20,16 @@ package
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Thanks a lot @srowen .
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@dongjoon-hyun @felixcheung how about now?
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If you're on YARN, this feels like something you would manage via YARN and
its cluster management options. Is there a specific use case here, that this
has to happen in Spark?
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Github user srowen commented on the issue:
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My first impression is that it's a big change, which is reason for caution
here.
Visualizing a workflow is nice, but Spark's Pipelines are typically pretty
straightforward and linear. I
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Merged to master
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--- Diff:
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@@ -131,8 +131,20 @@ object ExtractPythonUDFs extends
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LGTM, cc @jiangxb1987
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--- Diff:
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@@ -33,10 +33,10 @@ import org.apache.spark.shuffle._
*
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LGTM except a code style comment
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Github user cloud-fan commented on the issue:
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Let's move the high level discussion to the doc:
https://docs.google.com/document/d/1vI26UEuDpVuOjWw4WPoH2T6y8WAekwtI7qoowhOFnI4/edit?usp=sharing
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--- Diff:
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@@ -229,6 +229,11 @@ private[json] trait
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--- Diff: docs/sql-migration-guide-upgrade.md ---
@@ -35,7 +35,9 @@ displayTitle: Spark SQL Upgrading Guide
- Since Spark 3.0, CSV datasource
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--- Diff:
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@@ -20,14 +20,27 @@
import
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--- Diff:
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@@ -17,52 +17,49 @@
package
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--- Diff:
sql/core/src/main/java/org/apache/spark/sql/sources/v2/TableProvider.java ---
@@ -25,7 +25,10 @@
* The base interface for v2 data sources
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[SPARK-26313][SQL] move read related methods from Table to read related
mix-in traits
## What changes were proposed in this pull request?
As discussed in
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cc @rdblue @HyukjinKwon @gatorsmile
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retest this please
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thanks @maropu for starting it!
> Which SQL standard does Spark SQL follow (e.g., 2011 or 2016)?
I think SQL 2011 is good, but if we can't find a public version, maybe it's
also OK to
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