Github user marmbrus commented on a diff in the pull request: https://github.com/apache/spark/pull/11709#discussion_r56427859 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala --- @@ -268,6 +275,140 @@ 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. + val useUnsafeReader: Boolean = + sqlContext.getConf(SQLConf.PARQUET_UNSAFE_ROW_RECORD_READER_ENABLED) + + // If true, enable using the custom RecordReader for parquet. This only works for + // a subset of the types (no complex types). + val enableUnsafeRowParquetReader: Boolean = + sqlContext.getConf(SQLConf.PARQUET_UNSAFE_ROW_RECORD_READER_ENABLED.key).toBoolean + 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) => { + 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 (!useUnsafeReader) sys.error("Unsafe reader turned off.") + val unsafeReader = new UnsafeRowParquetRecordReader() + unsafeReader.initialize(split, hadoopAttemptContext) + + if (enableVectorizedParquetReader) { + unsafeReader.initBatch(partitionSchema, file.partitionValues) + // Whole stage codegen (PhysicalRDD) is able to deal with batches directly + // TODO: fix column appending + if (enableWholestageCodegen) { + unsafeReader.enableReturningBatches() + } + } + unsafeReader + } catch { + case NonFatal(e) => + logError(s"Falling back to parquet-mr: $e", e) + val reader = pushed match { + case Some(filter) => + new ParquetRecordReader[InternalRow]( + new CatalystReadSupport, + FilterCompat.get(filter, null)) + case _ => + new ParquetRecordReader[InternalRow](new CatalystReadSupport) + } + reader.initialize(split, hadoopAttemptContext) + reader + } + + val iter = new RecordReaderIterator(parquetReader) + val fullSchema = dataSchema.toAttributes ++ partitionSchema.toAttributes + val joinedRow = new JoinedRow() + val appendPartitionColumns = GenerateUnsafeProjection.generate(fullSchema, fullSchema) + + // UnsafeRowParquetRecordReader appends the columns internally to avoid another copy. + if (parquetReader.isInstanceOf[UnsafeRowParquetRecordReader]) { + iter.asInstanceOf[Iterator[InternalRow]] + } else { + // This is a horrible erasure hack... if we type the iterator above, then it actually check + // the type in next() and we get a class cast exception. If we make that function return + // Object, then we can defer the cast until later! + iter.asInstanceOf[Iterator[InternalRow]] + .map(d => appendPartitionColumns(joinedRow(d, file.partitionValues))) + .map(r => r.copy()) --- End diff -- Yeah, I was surprised by this, but a lot of tests fail without this.
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