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[SPARK-18258] Sink need access to offset representation
## What changes were proposed in this pull request?
Currently, sinks only have access to the batchId and the data,
Github user jerryshao commented on the issue:
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> The only change with this PR is that the welcome message will be printed
first, and then the Spark URL will be shown latter. It's a minor difference.
I think we should create a JIRA to
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--- Diff: python/pyspark/ml/clustering.py ---
@@ -622,10 +621,10 @@ def __init__(self, featuresCol="features",
predictionCol="prediction", maxIter=2
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+/*
+ * Licensed to the Apache
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@@ -53,6 +53,19 @@ private[spark] class WholeTextFileInputFormat
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nit: Also, can you put `[SQL][PYTHON]` in the title?
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@icexelloss Can you also show the query plan of the examples in the PR
description? Thanks.
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@rezazadeh is there is any plan to incorporate GLRM into core Spark?
It seems
https://github.com/rezazadeh/spark/tree/glrm/examples/src/main/scala/org/apache/spark/examples/glrm
hasn't had
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This PR takes me a while to get to because I am not very familiar with
Catalyst rules. I think in the end the change is relative simple but I would
appreciate some more careful review from
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[SPARK-24624] Support mixture of Python UDF and Scalar Pandas UDF
## What changes were proposed in this pull request?
This PR add supports for using mixed Python UDF and Scalar Pandas
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cc @liufengdb
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level:
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--- Diff: core/src/main/scala/org/apache/spark/storage/BlockManager.scala
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@@ -1341,12 +1390,16 @@ private[spark] class BlockManager(
try {
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+/*
+ *
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@@ -659,6 +701,11 @@ private[spark] class BlockManager(
* Get block
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### Memory Improvements
**toPandas()**
The most significant improvement is reduction of the upper bound space
complexity in the JVM driver. Before, the entire dataset was
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Github user holdenk commented on the issue:
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So I had this come up while I was testing Spark 2.1.3 RC2 on a machine with
an existing YARN cluster with `spark-testing-base`. Haven't had the chance to
dig into it fully.
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### Performance Tests - createDataFrame
Tests run on a 4 node standalone cluster with 32 cores total,
14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute
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### Performance Tests - toPandas
Tests run on a 4 node standalone cluster with 32 cores total,
14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute `toPandas()` and took
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_UPDATE_
I changed `toPandas` to write out of order partitions to python as they
come in, followed by a list of indices to represent the correct batch order.
In python, the batches are
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Github user attilapiros commented on a diff in the pull request:
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--- Diff:
resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
---
@@ -309,6 +312,9 @@ private[spark] class
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