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@som-snytt initialize it in `printWelcome` will not work since in order
version of Scala, `printWelcome` is the last one to be executed.
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> StreamRequest will not block the server netty handler thread.
Hmm, I'm not so sure that's accurate. I think the main difference is that I
don't think there is currently any code path that
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[SPARK-24491][Kubernetes] Configuration support for requesting GPUs on k8s
## What changes were proposed in this pull request?
Configuration support for generating the GPU requests in
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Retest this please.
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I think consistency in Spark's naming convention (and therefore increased
discoverability by users) outweighs the advantage of naming it exactly for
the Impala equivalent. I do agree that
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KubernetesSuite:
- Run SparkPi with no resources
- Run SparkPi with a very long application name.
- Run SparkPi with a master URL without a scheme.
- Run SparkPi with an argument.
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Github user rxin commented on the issue:
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Thanks, Henry. In general I'm not a huge fan of adding something because
hypothetically somebody might want it. Also if you want this to be compatible
with Impala, wouldn't you want to name this the
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--- Diff: R/pkg/NAMESPACE ---
@@ -281,6 +281,8 @@ exportMethods("%<=>%",
"initcap",
"input_file_name",
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[SPARK-24490][WebUI] Use WebUI.addStaticHandler in web UIs
`WebUI` defines `addStaticHandler` that web UIs don't use (and simply
introduce duplication). Let's clean them up and remove
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"input_file_name",
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Github user BryanCutler commented on the issue:
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Looks good to me, pending the above comments
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@rxin, that in itself is a bit weird, but there are ways to express inf
values in Scala and thus inf values can show up flowing through Spark plans.
I'm not sure MySQL has any such facility.
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@deepaksonu Would it be possible to close this PR?
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@jmwdpk can you update this pr, since there is conflict. I have update this
pr. https://github.com/merlintang/spark/commits/SPARK-23674
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@@ -1468,3 +1472,388 @@ case class
Github user HeartSaVioR commented on the issue:
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@aalobaidi
One thing you may want to be aware is that in point of executor's view,
executor must load at least 1 version of state in memory regardless of caching
versions. I guess you may
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--- Diff: python/pyspark/sql/tests.py ---
@@ -1884,7 +1885,164 @@ def test_query_manager_await_termination(self):
finally:
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--- Diff: python/pyspark/sql/streaming.py ---
@@ -843,6 +844,169 @@ def trigger(self, processingTime=None, once=None,
continuous=None):
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--- Diff: python/pyspark/sql/streaming.py ---
@@ -843,6 +844,169 @@ def trigger(self, processingTime=None, once=None,
continuous=None):
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--- Diff:
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@@ -0,0 +1,161 @@
+/*
+ * Licensed to the
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/ForeachWriter.scala
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@@ -71,23 +110,17 @@ abstract class ForeachWriter[T] extends Serializable {
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Check for invalid input type of weight data in ml.PowerIterationClustering
## What changes were proposed in this pull request?
The test case will result the following failure. currently in
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--- Diff: python/pyspark/sql/streaming.py ---
@@ -843,6 +844,169 @@ def trigger(self, processingTime=None, once=None,
continuous=None):
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--- Diff: python/pyspark/sql/streaming.py ---
@@ -843,6 +844,169 @@ def trigger(self, processingTime=None, once=None,
continuous=None):
Github user HeartSaVioR commented on the issue:
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@aalobaidi
You can also merge #21506 (maybe with changing log level or modify the
patch to set message to INFO level) and see latencies on loading state,
snapshotting, cleaning up.
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retest this please
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--- Diff: python/pyspark/sql/streaming.py ---
@@ -843,6 +844,169 @@ def trigger(self, processingTime=None, once=None,
continuous=None):
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[SPARK-24488] [SQL] Fix issue when generator is aliased multiple times
## What changes were proposed in this pull request?
Currently, the Analyzer throws an exception if your try to nest
Github user shahidki31 commented on the issue:
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Hi @hhbyyh @srowen ,
PowerIterationClustering API has some modifications based on the discussion
in the JIRA, https://issues.apache.org/jira/browse/SPARK-15784.
The examples also have
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Github user srowen commented on the issue:
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OK, I think this could have been combined with
https://github.com/apache/spark/pull/21283, but whatever.
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Hi @srowen , Thanks for the comment. I have modified the code.
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Sorry for the late reply. The option is useful for specific use case which
is micro-batches with relatively large number partitions with each of the
partitions is very big in size. When this
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Github user zsxwing commented on a diff in the pull request:
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/ForeachWriter.scala
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@@ -71,23 +110,17 @@ abstract class ForeachWriter[T] extends Serializable {
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