You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@hudi.apache.org by GitBox <gi...@apache.org> on 2022/07/22 03:35:25 UTC
[GitHub] [hudi] alexeykudinkin commented on a diff in pull request #5708: [HUDI-4420][Stacked on 5430] Fixing table schema delineation on partition/data schema for Spark relations
alexeykudinkin commented on code in PR #5708:
URL: https://github.com/apache/hudi/pull/5708#discussion_r927259929
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieBaseRelation.scala:
##########
@@ -564,42 +538,57 @@ abstract class HoodieBaseRelation(val sqlContext: SQLContext,
// we have to eagerly initialize all of the readers even though only one specific to the type
// of the file being read will be used. This is required to avoid serialization of the whole
// relation (containing file-index for ex) and passing it to the executor
- val reader = tableBaseFileFormat match {
- case HoodieFileFormat.PARQUET =>
- HoodieDataSourceHelper.buildHoodieParquetReader(
- sparkSession = spark,
- dataSchema = dataSchema.structTypeSchema,
- partitionSchema = partitionSchema,
- requiredSchema = requiredSchema.structTypeSchema,
- filters = filters,
- options = options,
- hadoopConf = hadoopConf,
- // We're delegating to Spark to append partition values to every row only in cases
- // when these corresponding partition-values are not persisted w/in the data file itself
- appendPartitionValues = shouldExtractPartitionValuesFromPartitionPath
- )
+ val (read: (PartitionedFile => Iterator[InternalRow]), schema: StructType) =
+ tableBaseFileFormat match {
+ case HoodieFileFormat.PARQUET =>
+ (
+ HoodieDataSourceHelper.buildHoodieParquetReader(
+ sparkSession = spark,
+ dataSchema = dataSchema.structTypeSchema,
+ partitionSchema = partitionSchema,
+ requiredSchema = requiredSchema.structTypeSchema,
+ filters = filters,
+ options = options,
+ hadoopConf = hadoopConf,
+ // We're delegating to Spark to append partition values to every row only in cases
+ // when these corresponding partition-values are not persisted w/in the data file itself
+ appendPartitionValues = shouldExtractPartitionValuesFromPartitionPath
+ ),
+ // Since partition values by default are omitted, and not persisted w/in data-files by Spark,
+ // data-file readers (such as [[ParquetFileFormat]]) have to inject partition values while reading
+ // the data. As such, actual full schema produced by such reader is composed of
+ // a) Prepended partition column values
+ // b) Data-file schema (projected or not)
+ StructType(partitionSchema.fields ++ requiredSchema.structTypeSchema.fields)
Review Comment:
It was actually a typo -- partition columns are actually appended (Spark's ParquetFileFormat has it incorrectly [here](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala#L237)
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieBaseRelation.scala:
##########
@@ -564,42 +538,57 @@ abstract class HoodieBaseRelation(val sqlContext: SQLContext,
// we have to eagerly initialize all of the readers even though only one specific to the type
// of the file being read will be used. This is required to avoid serialization of the whole
// relation (containing file-index for ex) and passing it to the executor
- val reader = tableBaseFileFormat match {
- case HoodieFileFormat.PARQUET =>
- HoodieDataSourceHelper.buildHoodieParquetReader(
- sparkSession = spark,
- dataSchema = dataSchema.structTypeSchema,
- partitionSchema = partitionSchema,
- requiredSchema = requiredSchema.structTypeSchema,
- filters = filters,
- options = options,
- hadoopConf = hadoopConf,
- // We're delegating to Spark to append partition values to every row only in cases
- // when these corresponding partition-values are not persisted w/in the data file itself
- appendPartitionValues = shouldExtractPartitionValuesFromPartitionPath
- )
+ val (read: (PartitionedFile => Iterator[InternalRow]), schema: StructType) =
+ tableBaseFileFormat match {
+ case HoodieFileFormat.PARQUET =>
+ (
+ HoodieDataSourceHelper.buildHoodieParquetReader(
+ sparkSession = spark,
+ dataSchema = dataSchema.structTypeSchema,
+ partitionSchema = partitionSchema,
+ requiredSchema = requiredSchema.structTypeSchema,
+ filters = filters,
+ options = options,
+ hadoopConf = hadoopConf,
+ // We're delegating to Spark to append partition values to every row only in cases
+ // when these corresponding partition-values are not persisted w/in the data file itself
+ appendPartitionValues = shouldExtractPartitionValuesFromPartitionPath
+ ),
+ // Since partition values by default are omitted, and not persisted w/in data-files by Spark,
+ // data-file readers (such as [[ParquetFileFormat]]) have to inject partition values while reading
+ // the data. As such, actual full schema produced by such reader is composed of
+ // a) Prepended partition column values
+ // b) Data-file schema (projected or not)
+ StructType(partitionSchema.fields ++ requiredSchema.structTypeSchema.fields)
Review Comment:
It was actually a typo -- partition columns are actually appended (Spark's ParquetFileFormat has it incorrectly [here](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala#L237))
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: commits-unsubscribe@hudi.apache.org
For queries about this service, please contact Infrastructure at:
users@infra.apache.org