Github user henryr commented on a diff in the pull request:
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--- Diff:
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---
@@ -63,115 +58,139 @@ public final
Github user megaserg commented on the issue:
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Thank you @dongjoon-hyun! This was also affecting our Spark job performance!
We're using `mapreduce.fileoutputcommitter.algorithm.version=2` in our
Spark job config, as recommended e.g.
Github user wzhfy commented on a diff in the pull request:
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--- Diff:
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@@ -304,45 +304,14 @@ case class LoadDataCommand(
}
Github user kiszk commented on the issue:
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Good point. I would add benchmark results. Let me leave ToDo in the
description.
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@@ -176,12 +176,13 @@ class HadoopTableReader(
val
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Github user rxin commented on the issue:
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What are the performance improvements? Without additional data this seems
like just an invasive change without any real benefits ...
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LGTM, merging to master!
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--- Diff:
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---
@@ -129,35 +151,41 @@ case class
Github user kiszk commented on the issue:
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According to my understanding, these activities are to improve
compatibility with other DBs (like Presto) in
https://issues.apache.org/jira/browse/SPARK-23899 and
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Github user cloud-fan commented on the issue:
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can we add a test? We can use
`HiveCatalogMetrics.METRIC_PARTITIONS_FETCHED.getCount()` to check if this
patch can really reduce the number of partitions being fetched.
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Can one of the admins verify this patch?
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GitHub user devaraj-kavali opened a pull request:
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[SPARK-21962][CORE] Distributed Tracing in Spark
## What changes were proposed in this pull request?
This PR integrates with HTrace, it sends traces for the application and
tasks
Github user gengliangwang commented on a diff in the pull request:
https://github.com/apache/spark/pull/20933#discussion_r181531212
--- Diff:
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---
@@ -0,0 +1,194 @@
+/*
+ *
Github user rxin commented on the issue:
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If there is already size, why do we need to create a new implementation?
Why can't we just rewrite cardinality to size?
Also I wouldn't add any programming API for this, since there is
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--- Diff:
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---
@@ -2128,38 +2128,60 @@ class JsonSuite extends
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--- Diff:
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@@ -413,6 +413,78 @@ class DataFrameFunctionsSuite extends QueryTest
Github user tdas commented on the issue:
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I am merging this to master. Once again, thank you for your reviews.
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Github user sijunhe commented on the issue:
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Would love to see this feature in Spark SQL.
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Github user mridulm commented on the issue:
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@squito I completely agree that the comment is inaccurate.
Note that this is for a specific taskset, so impact is limited to that
taskset (w.r.t using executors for spec exec)
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--- Diff:
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Github user tdas commented on the issue:
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@steveloughran Thanks for your comments :)
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Github user vanzin commented on a diff in the pull request:
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--- Diff: hadoop-cloud/pom.xml ---
@@ -38,7 +38,32 @@
hadoop-cloud
+
+
target/scala-${scala.binary.version}/classes
Github user vanzin commented on a diff in the pull request:
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--- Diff: assembly/pom.xml ---
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spark-hadoop-cloud_${scala.binary.version}
${project.version}
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--- Diff: hadoop-cloud/pom.xml ---
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hadoop-cloud
+
--- End diff --
Is this still needed after you
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cc @cloud-fan @hvanhovell
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Github user yanji84 commented on the issue:
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Anything else do we need to do to merge in this change?
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Github user rdblue commented on the issue:
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Upstream benchmarks for buffer management changes are here:
https://github.com/apache/parquet-mr/pull/390#issuecomment-338505426
That doesn't show the GC benefit for smaller buffer
Github user BryanCutler commented on the issue:
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retest this please
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Could you share the performance number?
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SPARK-23972: Update Parquet to 1.10.0.
## What changes were proposed in this pull request?
This updates Parquet to 1.10.0 and updates the vectorized path for buffer
management changes.
Github user gatorsmile commented on the issue:
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Thanks! Merged to master.
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--- Diff:
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@@ -1185,6 +1185,13 @@ object SQLConf {
.stringConf
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Github user jose-torres commented on the issue:
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I guess we might not even need to make an API change, just document that
these flags only mean anything for microbatch execution. In any case that's a
separate discussion.
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Github user jose-torres commented on the issue:
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I'm not sure isDataAvailable makes sense in the context of continuous
processing; it seems fundamentally tied to the microbatch execution model. I
think the best option is to just leave it and
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Github user mgaido91 commented on the issue:
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@DylanGuedes the first suggestion I can give you is: do not use spark-shell
for testing, but write UT and run them with a debugger. Then, you can
breakpoint to check the generated code (or you can
Github user DylanGuedes commented on the issue:
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Ok so It works fine in spark-shell but in pyspark I got this error:
```shell
File "/home/dguedes/Workspace/spark/python/pyspark/sql/functions.py", line
2155, in pyspark.sql.functions.zip
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Github user kiszk commented on a diff in the pull request:
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
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@@ -87,6 +87,62 @@ case class MapKeys(child:
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