wangyum commented on a change in pull request #24715: [SPARK-25474][SQL] Data
source tables support fallback to HDFS for size estimation
URL: https://github.com/apache/spark/pull/24715#discussion_r316534207
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/StatisticsCollectionSuite.scala
##########
@@ -650,4 +652,129 @@ class StatisticsCollectionSuite extends
StatisticsCollectionTestBase with Shared
}
}
}
+
+ test("Non-partitioned data source table support fallback to HDFS for size
estimation") {
+ withTempDir { dir =>
+ Seq(false, true).foreach { fallBackToHDFS =>
+ withSQLConf(SQLConf.ENABLE_FALL_BACK_TO_HDFS_FOR_STATS.key ->
s"$fallBackToHDFS") {
+ withTable("spark_25474") {
+ sql(s"CREATE TABLE spark_25474 (c1 BIGINT) USING PARQUET LOCATION
'${dir.toURI}'")
+
spark.range(5).write.mode(SaveMode.Overwrite).parquet(dir.getCanonicalPath)
+
+ assert(getCatalogTable("spark_25474").stats.isEmpty)
+ val relation =
spark.table("spark_25474").queryExecution.analyzed.children.head
+ // Table statistics are always recalculated by FileIndex
+ assert(relation.stats.sizeInBytes === getDataSize(dir))
+ }
+ }
+ }
+ }
+ }
+
+ test("Partitioned data source table support fallback to HDFS for size
estimation") {
+ Seq(false, true).foreach { fallBackToHDFS =>
+ withSQLConf(SQLConf.ENABLE_FALL_BACK_TO_HDFS_FOR_STATS.key ->
s"$fallBackToHDFS") {
+ withTempDir { dir =>
+ withTable("spark_25474") {
+ sql("CREATE TABLE spark_25474(a int, b int) USING parquet " +
+ s"PARTITIONED BY(a) LOCATION '${dir.toURI}'")
+ sql("INSERT INTO TABLE spark_25474 PARTITION(a=1) SELECT 2")
+
+ assert(getCatalogTable("spark_25474").stats.isEmpty)
+ val relation =
spark.table("spark_25474").queryExecution.analyzed.children.head
+ if (fallBackToHDFS) {
+ assert(relation.stats.sizeInBytes ===
+ CommandUtils.getSizeInBytesFallBackToHdfs(spark,
getCatalogTable("spark_25474")))
+ } else {
+ assert(relation.stats.sizeInBytes === conf.defaultSizeInBytes)
+ }
+ }
+ }
+ }
+ }
+ }
+
+ test("Partitioned data source table support fallback to HDFS for size
estimation" +
+ "with defaultSizeInBytes") {
+ val defaultSizeInBytes = 10 * 1024 * 1024
+ Seq(false, true).foreach { fallBackToHDFS =>
+ withSQLConf(
+ SQLConf.ENABLE_FALL_BACK_TO_HDFS_FOR_STATS.key -> s"$fallBackToHDFS",
+ SQLConf.DEFAULT_SIZE_IN_BYTES.key -> s"$defaultSizeInBytes") {
+ withTempDir { dir =>
+ withTable("spark_25474") {
+ sql("CREATE TABLE spark_25474(a int, b int) USING parquet " +
+ s"PARTITIONED BY(a) LOCATION '${dir.toURI}'")
+ sql("INSERT INTO TABLE spark_25474 PARTITION(a=1) SELECT 2")
+
+ assert(getCatalogTable("spark_25474").stats.isEmpty)
+ val relation =
spark.table("spark_25474").queryExecution.analyzed.children.head
+ if (fallBackToHDFS) {
+ assert(relation.stats.sizeInBytes ===
+ CommandUtils.getSizeInBytesFallBackToHdfs(spark,
getCatalogTable("spark_25474")))
+ } else {
+ assert(relation.stats.sizeInBytes === defaultSizeInBytes)
+ }
+ }
+ }
+ }
+ }
+ }
+
+ test("Partitioned data source table stats should be cached") {
+ Seq(false, true).foreach { fallBackToHDFS =>
+ withSQLConf(SQLConf.ENABLE_FALL_BACK_TO_HDFS_FOR_STATS.key ->
s"$fallBackToHDFS") {
+ withTempDir { dir =>
+ withTable("spark_25474") {
+ sql("CREATE TABLE spark_25474(a int, b int) USING parquet " +
+ s"PARTITIONED BY(a) LOCATION '${dir.toURI}'")
+ sql("INSERT INTO TABLE spark_25474 PARTITION(a=1) SELECT 2")
+
+ assert(getCatalogTable("spark_25474").stats.isEmpty)
+ val relation =
spark.table("spark_25474").queryExecution.analyzed.children.head
+ if (fallBackToHDFS) {
+ val dataSize =
+ CommandUtils.getSizeInBytesFallBackToHdfs(spark,
getCatalogTable("spark_25474"))
+ assert(relation.stats.sizeInBytes === dataSize)
+
+ val qualifiedTableName =
+
QualifiedTableName(spark.sessionState.catalog.getCurrentDatabase, "spark_25474")
+ val logicalRelation =
spark.sessionState.catalog.getCachedTable(qualifiedTableName)
+ .asInstanceOf[LogicalRelation]
+ assert(logicalRelation.catalogTable.get.stats.get.sizeInBytes
=== dataSize)
+ } else {
+ assert(relation.stats.sizeInBytes === conf.defaultSizeInBytes)
+ }
+ }
+ }
+ }
+ }
+ }
+
+ test("External partitioned data source table does not support fallback to
HDFS " +
+ "for size estimation") {
+ Seq(true).foreach { fallBackToHDFS =>
+ withSQLConf(SQLConf.ENABLE_FALL_BACK_TO_HDFS_FOR_STATS.key ->
s"$fallBackToHDFS") {
+ withTempDir { dir =>
+ withTable("spark_25474") {
+ sql("CREATE TABLE spark_25474(a bigint, b bigint) USING parquet " +
+ s"PARTITIONED BY(a) LOCATION '${dir.toURI}'")
+
+ withTempDir { partitionDir =>
+
spark.range(5).write.mode(SaveMode.Overwrite).parquet(partitionDir.getCanonicalPath)
+ sql(s"ALTER TABLE spark_25474 ADD PARTITION (a=1) LOCATION
'$partitionDir'")
+ assert(getCatalogTable("spark_25474").stats.isEmpty)
+ val relation =
spark.table("spark_25474").queryExecution.analyzed.children.head
+ assert(spark.table("spark_25474").count() === 5)
+ if (fallBackToHDFS) {
+ assert(relation.stats.sizeInBytes === 0)
Review comment:
Yes. Non-partitioned data source table will get the correct statistics if
table statistics are not available.
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