Github user a10y commented on the issue:
https://github.com/apache/spark/pull/20580
If you did add a test it should probably generate the Parquet file
programmatically rather than checking it in. Some examples in
https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org
Github user a10y commented on the issue:
https://github.com/apache/spark/pull/20580
As far as we can tell this is an accidental breaking change, as dropping
support for this in vectorized Parquet reader was never called out. We have
Parquet datasets with binary columns with logical
Github user a10y commented on the issue:
https://github.com/apache/spark/pull/18424
@ptkool are you still tracking this at all?
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Github user a10y commented on a diff in the pull request:
https://github.com/apache/spark/pull/18931#discussion_r144025706
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala
---
@@ -175,6 +175,25 @@ trait CodegenSupport extends SparkPlan
Github user a10y commented on a diff in the pull request:
https://github.com/apache/spark/pull/19082#discussion_r143836700
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala
---
@@ -244,6 +246,89 @@ case class HashAggregateExec
Github user a10y commented on a diff in the pull request:
https://github.com/apache/spark/pull/19082#discussion_r143836110
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala
---
@@ -244,6 +246,89 @@ case class HashAggregateExec
Github user a10y commented on a diff in the pull request:
https://github.com/apache/spark/pull/18945#discussion_r140142458
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -1761,12 +1761,37 @@ def toPandas(self):
raise ImportError("%s\n%s" % (e.me
Github user a10y commented on a diff in the pull request:
https://github.com/apache/spark/pull/18945#discussion_r140141889
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -1761,12 +1761,37 @@ def toPandas(self):
raise ImportError("%s\n%s" % (e.me
Github user a10y commented on a diff in the pull request:
https://github.com/apache/spark/pull/18945#discussion_r139450187
--- Diff: python/pyspark/sql/dataframe.py ---
@@ -1810,17 +1810,20 @@ def _to_scala_map(sc, jm):
return sc._jvm.PythonUtils.toScalaMap(jm
Github user a10y commented on a diff in the pull request:
https://github.com/apache/spark/pull/18424#discussion_r127611976
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
---
@@ -238,6 +238,14 @@ private[parquet] object
Github user a10y commented on the issue:
https://github.com/apache/spark/pull/18424
Indeed it appears to be. The resolution from my previous PR was that per
@HyukjinKwon's benchmarks, performing the disjunction in Spark was slightly
more performant than pushing it down to Parquet
Github user a10y closed the pull request at:
https://github.com/apache/spark/pull/14671
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