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fix: Support data pruning using nested partition columns #18126

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fix: Support data pruning using nested partition columns #18126

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ff71010
Handle nested map and array columns in MDT
vinishjail97 Dec 31, 2025
eaf9bd8
Fix the issue and add tests
linliu-code Feb 8, 2026
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5 changes: 4 additions & 1 deletion ...tasource/hudi-spark-common/src/main/scala/org/apache/hudi/SparkHoodieTableFileIndex.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -134,8 +134,11 @@ class SparkHoodieTableFileIndex(spark: SparkSession,
val partitionFields: Array[StructField] = partitionColumns.get().map(column => StructField(column, StringType))
StructType(partitionFields)
} else {
// Use full partition path (e.g. "nested_record.level") as the partition column name so that
// data schema does not exclude a same-named top-level column (e.g. "level") when partition
// path is a nested field. Otherwise partition value would overwrite the data column on read.
val partitionFields: Array[StructField] = partitionColumns.get().filter(column => nameFieldMap.contains(column))
.map(column => nameFieldMap.apply(column))
.map(column => StructField(column, nameFieldMap.apply(column).dataType))

if (partitionFields.length != partitionColumns.get().length) {
val isBootstrapTable = tableConfig.getBootstrapBasePath.isPresent
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198 changes: 198 additions & 0 deletions ...k-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestCOWDataSource.scala
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Expand Up @@ -2453,6 +2453,204 @@ class TestCOWDataSource extends HoodieSparkClientTestBase with ScalaAssertionSup
writeToHudi(opt, firstUpdateDF, DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL)
})
}

@ParameterizedTest
@CsvSource(Array("COW", "MOR"))
def testNestedFieldPartition(tableType: String): Unit = {
// Define schema with nested_record containing level field
val nestedSchema = StructType(Seq(
StructField("nested_int", IntegerType, nullable = false),
StructField("level", StringType, nullable = false)
))

val schema = StructType(Seq(
StructField("key", StringType, nullable = false),
StructField("ts", LongType, nullable = false),
StructField("level", StringType, nullable = false),
StructField("int_field", IntegerType, nullable = false),
StructField("string_field", StringType, nullable = true),
StructField("nested_record", nestedSchema, nullable = true)
))

// Create test data where top-level 'level' and 'nested_record.level' have DIFFERENT values
// This helps verify we're correctly partitioning/filtering on the nested field
val recordsCommit1 = Seq(
Row("key1", 1L, "L1", 1, "str1", Row(10, "INFO")),
Row("key2", 2L, "L2", 2, "str2", Row(20, "ERROR")),
Row("key3", 3L, "L3", 3, "str3", Row(30, "INFO")),
Row("key4", 4L, "L4", 4, "str4", Row(40, "DEBUG")),
Row("key5", 5L, "L5", 5, "str5", Row(50, "INFO"))
)

val tableTypeOptVal = if (tableType == "MOR") {
DataSourceWriteOptions.MOR_TABLE_TYPE_OPT_VAL
} else {
DataSourceWriteOptions.COW_TABLE_TYPE_OPT_VAL
}

val baseWriteOpts = Map(
"hoodie.insert.shuffle.parallelism" -> "4",
"hoodie.upsert.shuffle.parallelism" -> "4",
DataSourceWriteOptions.RECORDKEY_FIELD.key -> "key",
DataSourceWriteOptions.PARTITIONPATH_FIELD.key -> "nested_record.level",
HoodieTableConfig.ORDERING_FIELDS.key -> "ts",
HoodieWriteConfig.TBL_NAME.key -> "test_nested_partition",
DataSourceWriteOptions.TABLE_TYPE.key -> tableTypeOptVal
)
val writeOpts = if (tableType == "MOR") {
baseWriteOpts + ("hoodie.compact.inline" -> "false")
} else {
baseWriteOpts
}

// Commit 1 - Initial insert
val inputDF1 = spark.createDataFrame(
spark.sparkContext.parallelize(recordsCommit1),
schema
)
inputDF1.write.format("hudi")
.options(writeOpts)
.option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL)
.mode(SaveMode.Overwrite)
.save(basePath)
val commit1 = DataSourceTestUtils.latestCommitCompletionTime(storage, basePath)

// Commit 2 - Upsert: update key1 (int_field 1->100), insert key6 (INFO)
val recordsCommit2 = Seq(
Row("key1", 10L, "L1", 100, "str1", Row(10, "INFO")),
Row("key6", 6L, "L6", 6, "str6", Row(60, "INFO"))
)
val inputDF2 = spark.createDataFrame(
spark.sparkContext.parallelize(recordsCommit2),
schema
)
inputDF2.write.format("hudi")
.options(writeOpts)
.option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL)
.mode(SaveMode.Append)
.save(basePath)
val commit2 = DataSourceTestUtils.latestCommitCompletionTime(storage, basePath)

// Commit 3 - Upsert: update key3 (int_field 3->300), insert key7 (INFO)
val recordsCommit3 = Seq(
Row("key3", 30L, "L3", 300, "str3", Row(30, "INFO")),
Row("key7", 7L, "L7", 7, "str7", Row(70, "INFO"))
)
val inputDF3 = spark.createDataFrame(
spark.sparkContext.parallelize(recordsCommit3),
schema
)
inputDF3.write.format("hudi")
.options(writeOpts)
.option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL)
.mode(SaveMode.Append)
.save(basePath)
val commit3 = DataSourceTestUtils.latestCommitCompletionTime(storage, basePath)

// Snapshot read - filter on nested_record.level = 'INFO' (latest state: 5 records)
val snapshotResults = spark.read.format("hudi")
.load(basePath)
.filter("nested_record.level = 'INFO'")
.select("key", "ts", "level", "int_field", "string_field", "nested_record")
.orderBy("key")
.collect()

val expectedSnapshot = Array(
Row("key1", 10L, "L1", 100, "str1", Row(10, "INFO")),
Row("key3", 30L, "L3", 300, "str3", Row(30, "INFO")),
Row("key5", 5L, "L5", 5, "str5", Row(50, "INFO")),
Row("key6", 6L, "L6", 6, "str6", Row(60, "INFO")),
Row("key7", 7L, "L7", 7, "str7", Row(70, "INFO"))
)
assertEquals(expectedSnapshot.length, snapshotResults.length,
s"Snapshot (INFO) count mismatch for $tableType")
expectedSnapshot.zip(snapshotResults).foreach { case (expected, actual) =>
assertEquals(expected, actual)
}

// Time travel - as of commit1 (only initial 5 records; INFO = key1, key3, key5)
val timeTravelCommit1 = spark.read.format("hudi")
.option(DataSourceReadOptions.TIME_TRAVEL_AS_OF_INSTANT.key, commit1)
.load(basePath)
.filter("nested_record.level = 'INFO'")
.select("key", "ts", "level", "int_field", "string_field", "nested_record")
.orderBy("key")
.collect()

val expectedAfterCommit1 = Array(
Row("key1", 1L, "L1", 1, "str1", Row(10, "INFO")),
Row("key3", 3L, "L3", 3, "str3", Row(30, "INFO")),
Row("key5", 5L, "L5", 5, "str5", Row(50, "INFO"))
)
assertEquals(expectedAfterCommit1.length, timeTravelCommit1.length,
s"Time travel to commit1 (INFO) count mismatch for $tableType")
expectedAfterCommit1.zip(timeTravelCommit1).foreach { case (expected, actual) =>
assertEquals(expected, actual)
}

// Time travel - as of commit2 (after 2nd commit; INFO = key1 updated, key3, key5, key6)
val timeTravelCommit2 = spark.read.format("hudi")
.option(DataSourceReadOptions.TIME_TRAVEL_AS_OF_INSTANT.key, commit2)
.load(basePath)
.filter("nested_record.level = 'INFO'")
.select("key", "ts", "level", "int_field", "string_field", "nested_record")
.orderBy("key")
.collect()

val expectedAfterCommit2 = Array(
Row("key1", 10L, "L1", 100, "str1", Row(10, "INFO")),
Row("key3", 3L, "L3", 3, "str3", Row(30, "INFO")),
Row("key5", 5L, "L5", 5, "str5", Row(50, "INFO")),
Row("key6", 6L, "L6", 6, "str6", Row(60, "INFO"))
)
assertEquals(expectedAfterCommit2.length, timeTravelCommit2.length,
s"Time travel to commit2 (INFO) count mismatch for $tableType")
expectedAfterCommit2.zip(timeTravelCommit2).foreach { case (expected, actual) =>
assertEquals(expected, actual)
}

// Incremental query - from commit1 to commit2 (only key1 update and key6 insert; both INFO)
val incrementalCommit1To2 = spark.read.format("hudi")
.option(DataSourceReadOptions.QUERY_TYPE.key, DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL)
.option(DataSourceReadOptions.START_COMMIT.key, commit1)
.option(DataSourceReadOptions.END_COMMIT.key, commit2)
.load(basePath)
.filter("nested_record.level = 'INFO'")
.select("key", "ts", "level", "int_field", "string_field", "nested_record")
.orderBy("key")
.collect()

val expectedInc1To2 = Array(
Row("key1", 10L, "L1", 100, "str1", Row(10, "INFO")),
Row("key6", 6L, "L6", 6, "str6", Row(60, "INFO"))
)
assertEquals(expectedInc1To2.length, incrementalCommit1To2.length,
s"Incremental (commit1->commit2, INFO) count mismatch for $tableType")
expectedInc1To2.zip(incrementalCommit1To2).foreach { case (expected, actual) =>
assertEquals(expected, actual)
}

// Incremental query - from commit2 to commit3 (only key3 update and key7 insert; both INFO)
val incrementalCommit2To3 = spark.read.format("hudi")
.option(DataSourceReadOptions.QUERY_TYPE.key, DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL)
.option(DataSourceReadOptions.START_COMMIT.key, commit2)
.option(DataSourceReadOptions.END_COMMIT.key, commit3)
.load(basePath)
.filter("nested_record.level = 'INFO'")
.select("key", "ts", "level", "int_field", "string_field", "nested_record")
.orderBy("key")
.collect()

val expectedInc2To3 = Array(
Row("key3", 30L, "L3", 300, "str3", Row(30, "INFO")),
Row("key7", 7L, "L7", 7, "str7", Row(70, "INFO"))
)
assertEquals(expectedInc2To3.length, incrementalCommit2To3.length,
s"Incremental (commit2->commit3, INFO) count mismatch for $tableType")
expectedInc2To3.zip(incrementalCommit2To3).foreach { case (expected, actual) =>
assertEquals(expected, actual)
}
}
}

object TestCOWDataSource {
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