[GitHub] [hudi] alexeykudinkin commented on a change in pull request #5077: [HUDI-3664] Handle type conversion for comparison of column range metadata

[GitBox <gi...@apache.org>]

alexeykudinkin commented on a change in pull request #5077:
URL: https://github.com/apache/hudi/pull/5077#discussion_r831514560



##########
File path: hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestColumnStatsIndex.scala
##########
@@ -63,6 +69,91 @@ class TestColumnStatsIndex extends HoodieClientTestBase {
     cleanupSparkContexts()
   }
 
+  @Test
+  def testMetadataColumnStatsIndex(): Unit = {

Review comment:
       In general, i think we should test against all operations that can modify Col Stats index (commit, delta_commit, clean, compact, cluster) to make sure they leave it in consistent state.

##########
File path: hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestColumnStatsIndex.scala
##########
@@ -63,6 +69,91 @@ class TestColumnStatsIndex extends HoodieClientTestBase {
     cleanupSparkContexts()
   }
 
+  @Test
+  def testMetadataColumnStatsIndex(): Unit = {
+    setTableName("hoodie_test")
+    initMetaClient()
+    val sourceJSONTablePath = getClass.getClassLoader.getResource("index/zorder/input-table-json").toString
+    val inputDF =
+    // NOTE: Schema here is provided for validation that the input date is in the appropriate format
+      spark.read
+        .schema(sourceTableSchema)
+        .json(sourceJSONTablePath)
+
+    val opts = Map(
+      "hoodie.insert.shuffle.parallelism" -> "4",
+      "hoodie.upsert.shuffle.parallelism" -> "4",
+      HoodieWriteConfig.TBL_NAME.key -> "hoodie_test",
+      RECORDKEY_FIELD.key -> "c1",
+      PRECOMBINE_FIELD.key -> "c1",
+      HoodieMetadataConfig.ENABLE.key -> "true",
+      HoodieMetadataConfig.ENABLE_METADATA_INDEX_COLUMN_STATS.key -> "true",
+      HoodieMetadataConfig.ENABLE_METADATA_INDEX_COLUMN_STATS_FOR_ALL_COLUMNS.key -> "true",
+      HoodieTableConfig.POPULATE_META_FIELDS.key -> "true"
+    )
+
+    inputDF.repartition(4)
+      .write
+      .format("hudi")
+      .options(opts)
+      .option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL)
+      .option(HoodieStorageConfig.PARQUET_MAX_FILE_SIZE.key, 100 * 1024)
+      .mode(SaveMode.Overwrite)
+      .save(basePath)
+
+    metaClient = HoodieTableMetaClient.reload(metaClient)
+
+    val metadataTablePath = HoodieTableMetadata.getMetadataTableBasePath(basePath)
+
+    val targetColStatsIndexColumns = Seq(
+      HoodieMetadataPayload.COLUMN_STATS_FIELD_FILE_NAME,
+      HoodieMetadataPayload.COLUMN_STATS_FIELD_MIN_VALUE,
+      HoodieMetadataPayload.COLUMN_STATS_FIELD_MAX_VALUE,
+      HoodieMetadataPayload.COLUMN_STATS_FIELD_NULL_COUNT)
+
+    val requiredMetadataIndexColumns =
+      (targetColStatsIndexColumns :+ HoodieMetadataPayload.COLUMN_STATS_FIELD_COLUMN_NAME).map(colName =>
+        s"${HoodieMetadataPayload.SCHEMA_FIELD_ID_COLUMN_STATS}.${colName}")
+
+    // Read Metadata Table's Column Stats Index into Spark's [[DataFrame]]
+    val metadataTableDF = spark.read.format("org.apache.hudi")
+      .load(s"$metadataTablePath/${MetadataPartitionType.COLUMN_STATS.getPartitionPath}")
+
+    val colStatsDF = metadataTableDF.where(col(HoodieMetadataPayload.SCHEMA_FIELD_ID_COLUMN_STATS).isNotNull)
+      .select(requiredMetadataIndexColumns.map(col): _*)
+
+    // assert min/max for some columns
+    val minC1 = colStatsDF.select(HoodieMetadataPayload.COLUMN_STATS_FIELD_MIN_VALUE)

Review comment:
       Instead of selectively asserting min/max for columns, let's use the final table fixture and match it as a whole (like is being done in `testZIndexTableComposition`)




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