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[SPARK-25028][SQL] Avoid NPE when analyzing partition with NULL values #22036

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[SPARK-25028][SQL] Avoid NPE when analyzing partition with NULL values #22036

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[SPARK-25028][SQL] Avoid NPE when analyzing partition with NULL values
mgaido91 Aug 8, 2018
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mgaido91 Aug 13, 2018
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[SPARK-25028][SQL] Avoid NPE when analyzing partition with NULL values
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@mgaido91
mgaido91 committed Aug 8, 2018
commit ee64a6ba3d41c0f3d6776d7ccbd9af7185d8a3ad
10 changes: 8 additions & 2 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/command/AnalyzePartitionCommand.scala
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Expand Up @@ -20,7 +20,7 @@ package org.apache.spark.sql.execution.command
import org.apache.spark.sql.{AnalysisException, Column, Row, SparkSession}
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.analysis.{NoSuchPartitionException, UnresolvedAttribute}
import org.apache.spark.sql.catalyst.catalog.{CatalogTable, CatalogTableType}
import org.apache.spark.sql.catalyst.catalog.{CatalogTable, CatalogTableType, ExternalCatalogUtils}
import org.apache.spark.sql.catalyst.catalog.CatalogTypes.TablePartitionSpec
import org.apache.spark.sql.catalyst.expressions.{And, EqualTo, Literal}
import org.apache.spark.sql.execution.datasources.PartitioningUtils
Expand Down Expand Up @@ -140,7 +140,13 @@ case class AnalyzePartitionCommand(
val df = tableDf.filter(Column(filter)).groupBy(partitionColumns: _*).count()

df.collect().map { r =>
val partitionColumnValues = partitionColumns.indices.map(r.get(_).toString)
val partitionColumnValues = partitionColumns.indices.map { i =>
if (r.isNullAt(i)) {
ExternalCatalogUtils.DEFAULT_PARTITION_NAME
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@cloud-fan cloud-fan Aug 13, 2018 •
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do we need to change the read path? i.e. where we use these statistics.

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@mgaido91 mgaido91 Aug 13, 2018

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I don't think so, as the same situation would happen if Hive's statistics are used instead of the ones computed by Spark

} else {
r.get(i).toString
}
}
val spec = tableMeta.partitionColumnNames.zip(partitionColumnValues).toMap
val count = BigInt(r.getLong(partitionColumns.size))
(spec, count)
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18 changes: 18 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/StatisticsCollectionSuite.scala
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Expand Up @@ -204,6 +204,24 @@ class StatisticsCollectionSuite extends StatisticsCollectionTestBase with Shared
}
}

test("SPARK-25028: column stats collection for null partitioning columns") {
val table = "analyze_partition_with_null"
withTempDir { dir =>
withTable(table) {
sql(s"""
|CREATE TABLE $table (name string, value string)
|USING PARQUET
|PARTITIONED BY (name)
|LOCATION '${dir.toURI}'""".stripMargin)
val df = Seq(("a", null), ("b", null)).toDF("value", "name")
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@maropu maropu Aug 9, 2018

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super nit: better to add a non-null partition value, e.g., val df = Seq(("a", null), ("b", null), ("c", "1")).toDF("value", "name")? btw, why is this a reverse column order (not "name", "value", but "value", "name")?

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@mgaido91 mgaido91 Aug 9, 2018

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I don't think it is needed to add another partition value, as the problem here is with null throwing an NPE and the test shows that no NPE is thrown. But if you think it is necessary I can add it.

The reverse column order is the way spark works when inserting data into a partitioned table. The partitioning columns are specified at the end, after the non-partitioning ones.

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@cloud-fan cloud-fan Aug 13, 2018

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when creating the table, we can put partition column at the end, to avoid this confusion.

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@mgaido91 mgaido91 Aug 13, 2018

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ok, will do, thanks.

df.write.mode("overwrite").insertInto(table)
sql(s"ANALYZE TABLE $table PARTITION (name) COMPUTE STATISTICS")
val partitions = spark.sessionState.catalog.listPartitions(TableIdentifier(table))
assert(partitions.head.stats.get.rowCount.get == 2)
}
}
}

test("number format in statistics") {
val numbers = Seq(
BigInt(0) -> (("0.0 B", "0")),
Expand Down