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--- Diff:
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@@ -266,8 +268,9 @@ object OrcReadBenchmark extends
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[SPARK-25964][SQL][Minor] Revise OrcReadBenchmark/DataSourceReadBenchmark
case names and execution instructions
## What changes were proposed in this pull request?
1.
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At least it doesn't fail on the cases
https://github.com/apache/spark/pull/22938/files#diff-6626026091295ad8c0dfb66ecbcd04b1R568
and
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I guess the problem belongs to `FailureSafeParser`, in particular
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kryo
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@SehanRathnayake Kafka is designed for at most one consumer per partition
per consumer group at any given point in time,
https://kafka.apache.org/documentation/#design_consumerposition
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@HyukjinKwon Sure, the test would be for invalid JSON array:
```Scala
test("from_json - input=invalid JSON array, schema=array, output=array") {
val input = """[{"a": 1},
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@attilapiros, mind showing rough small test codes for it please? just want
to see if this is something we should fix or not.
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Yea, looks fine in general. Will take a look within this week or weekends.
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@MaxGekk I have checked out your PR and played a little bit with it:
created a new unit test as a copy of "from_json - input=array, schema=array,
output=array" with an invalid JSON.
I
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I think "This is fundamentally not correct, different apps should use
different consumer group" statement is wrong. .According to Kafka Having
consumers as part of the same consumer group
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[SPARK-25963] Optimize generate followed by window
## What changes were proposed in this pull request?
I've added an optimizer rule to add Repartition operator when we have a
Generate operator
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--- Diff: R/pkg/R/functions.R ---
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#' also supported for the schema.
#' \item \code{from_csv}:
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[SPARK-25962][BUILD][PYTHON]Specify minimum versions for both pydocstyle
and flake8 in 'lint-python' script
## What changes were proposed in this pull request?
This PR explicitly
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--- Diff: python/pyspark/sql/tests.py ---
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def unpartitioned_window(self):
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[SPARK-25921][PySpark] Fix BarrierTaskContext while python worker reuse
## What changes were proposed in this pull request?
While python worker reuse,
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--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -530,6 +530,8 @@ Here are the details of all the sources in Spark.
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For encryption stuff, I will try to handle that as well (maybe as a
followup(?)) so that we support it even when that's enabled.
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Thanks, @felixcheung. I will address those comments during cleaning up.
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--- Diff: R/pkg/R/SQLContext.R ---
@@ -147,6 +147,30 @@ getDefaultSqlSource <- function() {
l[["spark.sql.sources.default"]]
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retest this please
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