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--- Diff: python/pyspark/sql/functions.py ---
@@ -2142,18 +2159,26 @@ def udf(f=None, returnType=StringType()):
| 8| JOHN DOE|
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@@ -0,0 +1,127 @@
+/*
+ * Licensed to
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@@ -126,22 +126,20 @@ private[spark] class CompactBuffer[T: ClassTag]
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HashMapGrowthStrategy DOUBLING = new
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private final long length;
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[SPARK-22026][SQL][WIP] data source v2 write path
## What changes were proposed in this pull request?
A working prototype for data source v2 write path.
TODO: doc.
##
Github user gatorsmile commented on the issue:
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- You can get it from the table metadata `table: CatalogTable`
- `Insertintohadoopfsrelationcommand.scala ` is for data source tables. We
only have the issues for Hive table writing,
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Github user sethah commented on the issue:
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Sure, we all agree there is a mechanism for avoiding overhead. However,
performance tests are very tricky things, 5% is not a huge improvement, and
hard-coding the aggregation depth to `2` limits
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Can one of the admins verify this patch?
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No way to make the change without a PR, so no leave it.
http://spark.apache.org/contributing.html
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Will do. Delete this pull afterwards?
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Please make a JIRA @Taaffy
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Incorrect Metric reported in MetricsReporter.scala
Current implementation for processingRate-total uses wrong metric:
mistakenly uses inputRowsPerSecond instead of processedRowsPerSecond
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I think we should not do it, because no DB vendor does it.
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[WIP][SPARK-20628][CORE] Blacklist nodes when they transition to
DECOMMISSIONING state in YARN
## What changes were proposed in this pull request?
Dynamic cluster configurations where cluster
Github user rxin commented on the issue:
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What does this even mean?
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It looks good, but the actual code should be very simple if you are writing
using the Scala way
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I can see the value, but it does not perform well in most cases if we using
JDBC connection. Instead of adding the extra dialect to upstream, could you
please add Hive as a separate data source?
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Github user srowen commented on the issue:
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Yeah, agree, it could be some global constant. I don't think it should be
configurable. Ideally it's determined from the JVM, but don't know a way to do
that.
In many cases, assuming
Github user WeichenXu123 commented on the issue:
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@viirya I run the code, you're right, most of time cost on the executedPlan
generation (The old version code). thanks!
But can you append benchmark comparison with `RDD.aggregate` version?
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@@ -352,11 +374,16 @@ object TypeCoercion {
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@@ -925,6 +925,12 @@ object SQLConf {
.intConf
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@@ -925,6 +925,12 @@ object SQLConf {
.intConf
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@@ -925,6 +925,12 @@ object SQLConf {
.intConf
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Thanks! Merged to master.
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retest this please
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retest this please
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Ping @jkbradley .
Thank you @WeichenXu123 one again for the comment! Please, have a look.
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--- Diff:
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@@ -65,53 +60,76 @@ private[spark] class LiveListenerBus(conf: SparkConf)
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Github user maropu commented on the issue:
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I though, if this limit highly depends on JVM implementations, better to
put the limit as a global variable somewhere (e.g., `ARRAY_INT_MAX` in
`spark.util.Utils` or other places)? As another
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@@ -65,53 +60,76 @@ private[spark] class LiveListenerBus(conf: SparkConf)
Github user maver1ck commented on the issue:
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OK. It passed all tests, so let merge it
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Github user kevinyu98 commented on the issue:
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can we retest this ? The unknown return code is not related to the code.
Thanks.
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Looks not the reason. maybe issues somewhere else. Let me run test later.
Thanks!
But there is some small issues in test:
Don't include gen data time:
```
val start =
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--- Diff: python/pyspark/sql/dataframe.py ---
@@ -1810,17 +1810,20 @@ def _to_scala_map(sc, jm):
return sc._jvm.PythonUtils.toScalaMap(jm)
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CC @maropu
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[SPARK-22033][CORE] BufferHolder, other size checks should account for the
specific VM array size limitations
## What changes were proposed in this pull request?
Try to avoid allocating an
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