ParquetFileFormat¶

ParquetFileFormat is the FileFormat of Parquet Data Source.

ParquetFileFormat is splitable.

ParquetFileFormat is Serializable.

Short Name¶

shortName(): String

ParquetFileFormat is a DataSourceRegister with the short name:

parquet

Metadata Columns¶

metadataSchemaFields: Seq[StructField]

metadataSchemaFields is the following metadata columns:

• The default FileFormat-specific metadata columns
• row_index

row_index¶

row_index is a ParquetFileFormat-specific metadata column.

_tmp_metadata_row_index¶

isSplitable¶

isSplitable(
  sparkSession: SparkSession,
  options: Map[String, String],
  path: Path): Boolean

ParquetFileFormat is splitable (true).

Building Data Reader With Partition Values¶

buildReaderWithPartitionValues(
  sparkSession: SparkSession,
  dataSchema: StructType,
  partitionSchema: StructType,
  requiredSchema: StructType,
  filters: Seq[Filter],
  options: Map[String, String],
  hadoopConf: Configuration): (PartitionedFile) => Iterator[InternalRow]

buildReaderWithPartitionValues sets the following configuration options in the input hadoopConf.

buildReaderWithPartitionValues requests ParquetWriteSupport to setSchema.

buildReaderWithPartitionValues tries to push filters down to create a Parquet FilterPredicate (aka pushed).

With spark.sql.parquet.filterPushdown configuration property enabled, buildReaderWithPartitionValues takes the input Spark data source filters and converts them to Parquet filter predicates if possible. Otherwise, the Parquet filter predicate is not specified.

buildReaderWithPartitionValues broadcasts the input hadoopConf Hadoop Configuration.

In the end, buildReaderWithPartitionValues gives a function that takes a PartitionedFile and does the following:

1. Creates a Hadoop FileSplit for the input PartitionedFile

2. Creates a Parquet ParquetInputSplit for the Hadoop FileSplit created

3. Gets the broadcast Hadoop Configuration

4. Creates a flag that says whether to apply timezone conversions to int96 timestamps or not (aka convertTz)

5. Creates a Hadoop TaskAttemptContextImpl (with the broadcast Hadoop Configuration and a Hadoop TaskAttemptID for a map task)

6. Sets the Parquet FilterPredicate (only when spark.sql.parquet.filterPushdown configuration property is enabled and it is by default)

The function then branches off on whether Parquet vectorized reader is enabled or not.

With Parquet vectorized reader enabled, the function does the following:

• Creates a VectorizedParquetRecordReader and a RecordReaderIterator

• Requests VectorizedParquetRecordReader to initialize (with the Parquet ParquetInputSplit and the Hadoop TaskAttemptContextImpl)

• Prints out the following DEBUG message to the logs:

  Appending [partitionSchema] [partitionValues]
  

• Requests VectorizedParquetRecordReader to initBatch

• (only with supportBatch enabled) Requests VectorizedParquetRecordReader to enableReturningBatches

• In the end, the function gives the RecordReaderIterator (over the VectorizedParquetRecordReader) as the Iterator[InternalRow]

With Parquet vectorized reader disabled, the function does the following:

• FIXME (since Parquet vectorized reader is enabled by default it's of less interest)

supportBatch¶

supportBatch(
  sparkSession: SparkSession,
  schema: StructType): Boolean

supportBatch supports vectorized parquet decoding in whole-stage code generation when the following all hold:

1. spark.sql.parquet.enableVectorizedReader configuration property is enabled

2. spark.sql.codegen.wholeStage internal configuration property is enabled

3. The number of fields in the schema is at most spark.sql.codegen.maxFields internal configuration property

4. All the fields in the output schema are of AtomicType

Vector Types¶

vectorTypes(
  requiredSchema: StructType,
  partitionSchema: StructType,
  sqlConf: SQLConf): Option[Seq[String]]

vectorTypes creates a collection of the names of OffHeapColumnVector or OnHeapColumnVector when spark.sql.columnVector.offheap.enabled property is enabled or disabled, respectively.

The size of the collection are all the fields of the given requiredSchema and partitionSchema schemas.

mergeSchemasInParallel¶

mergeSchemasInParallel(
  parameters: Map[String, String],
  filesToTouch: Seq[FileStatus],
  sparkSession: SparkSession): Option[StructType]

mergeSchemasInParallel mergeSchemasInParallel with the given filesToTouch and a multi-threaded parquet footer reader.

mergeSchemasInParallel is used when:

• ParquetUtils is requested to infer schema

Logging¶

Enable ALL logging level for org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat logger to see what happens inside.

Add the following line to conf/log4j2.properties:

logger.ParquetFileFormat.name = org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat
logger.ParquetFileFormat.level = all

Refer to Logging.