Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/11709#discussion_r57422058
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
---
@@ -269,6 +276,137 @@ private[sql] class DefaultSource extends FileFormat
with DataSourceRegister with
file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
}
+ /**
+ * Returns a function that can be used to read a single file in as an
Iterator of InternalRow.
+ *
+ * @param partitionSchema The schema of the partition column row that
will be present in each
+ * PartitionedFile. These columns should be
prepended to the rows that
+ * are produced by the iterator.
+ * @param dataSchema The schema of the data that should be output for
each row. This may be a
+ * subset of the columns that are present in the file
if column pruning has
+ * occurred.
+ * @param filters A set of filters than can optionally be used to reduce
the number of rows output
+ * @param options A set of string -> string configuration options.
+ * @return
+ */
+ override def buildReader(
+ sqlContext: SQLContext,
+ partitionSchema: StructType,
+ dataSchema: StructType,
+ filters: Seq[Filter],
+ options: Map[String, String]): PartitionedFile =>
Iterator[InternalRow] = {
+ val parquetConf = new
Configuration(sqlContext.sparkContext.hadoopConfiguration)
+ parquetConf.set(ParquetInputFormat.READ_SUPPORT_CLASS,
classOf[CatalystReadSupport].getName)
+ parquetConf.set(
+ CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA,
+ CatalystSchemaConverter.checkFieldNames(dataSchema).json)
+ parquetConf.set(
+ CatalystWriteSupport.SPARK_ROW_SCHEMA,
+ CatalystSchemaConverter.checkFieldNames(dataSchema).json)
+
+ // We want to clear this temporary metadata from saving into Parquet
file.
+ // This metadata is only useful for detecting optional columns when
pushdowning filters.
+ val dataSchemaToWrite =
StructType.removeMetadata(StructType.metadataKeyForOptionalField,
+ dataSchema).asInstanceOf[StructType]
+ CatalystWriteSupport.setSchema(dataSchemaToWrite, parquetConf)
+
+ // Sets flags for `CatalystSchemaConverter`
+ parquetConf.setBoolean(
+ SQLConf.PARQUET_BINARY_AS_STRING.key,
+ sqlContext.conf.getConf(SQLConf.PARQUET_BINARY_AS_STRING))
+ parquetConf.setBoolean(
+ SQLConf.PARQUET_INT96_AS_TIMESTAMP.key,
+ sqlContext.conf.getConf(SQLConf.PARQUET_INT96_AS_TIMESTAMP))
+
+ // Try to push down filters when filter push-down is enabled.
+ val pushed = if
(sqlContext.getConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key).toBoolean) {
+ filters
+ // Collects all converted Parquet filter predicates. Notice that
not all predicates can be
+ // converted (`ParquetFilters.createFilter` returns an
`Option`). That's why a `flatMap`
+ // is used here.
+ .flatMap(ParquetFilters.createFilter(dataSchema, _))
+ .reduceOption(FilterApi.and)
+ } else {
+ None
+ }
+
+ val broadcastedConf =
+ sqlContext.sparkContext.broadcast(new
SerializableConfiguration(parquetConf))
+
+ // TODO: if you move this into the closure it reverts to the default
values.
+ // If true, enable using the custom RecordReader for parquet. This
only works for
+ // a subset of the types (no complex types).
+ val enableVectorizedParquetReader: Boolean =
+
sqlContext.getConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key).toBoolean
+ val enableWholestageCodegen: Boolean =
+ sqlContext.getConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key).toBoolean
+
+ (file: PartitionedFile) => {
+ assert(file.partitionValues.numFields == partitionSchema.size)
+
+ val fileSplit =
+ new FileSplit(new Path(new URI(file.filePath)), file.start,
file.length, Array.empty)
+
+ val split =
+ new org.apache.parquet.hadoop.ParquetInputSplit(
+ fileSplit.getPath,
+ fileSplit.getStart,
+ fileSplit.getStart + fileSplit.getLength,
+ fileSplit.getLength,
+ fileSplit.getLocations,
+ null)
+
+ val attemptId = new TaskAttemptID(new TaskID(new JobID(),
TaskType.MAP, 0), 0)
+ val hadoopAttemptContext = new
TaskAttemptContextImpl(broadcastedConf.value.value, attemptId)
+
+ val parquetReader = try {
+ if (!enableVectorizedParquetReader) sys.error("Vectorized reader
turned off.")
+ val vectorizedReader = new VectorizedParquetRecordReader()
+ vectorizedReader.initialize(split, hadoopAttemptContext)
+ logDebug(s"Appending $partitionSchema ${file.partitionValues}")
+ vectorizedReader.initBatch(partitionSchema, file.partitionValues)
--- End diff --
Sorry for the confusion. When I mentioned "planning phase" what I really
meant was that ideally the data source implementation shouldn't care about
partitioning at all. But I mixed up partition discovery and partition value
appending. I agree with your comments. Thanks for the explanations.
---
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