Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/8988#discussion_r41431958
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystWriteSupport.scala
---
@@ -0,0 +1,428 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import java.nio.{ByteBuffer, ByteOrder}
+import java.util
+
+import scala.collection.JavaConverters.mapAsJavaMapConverter
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.parquet.column.ParquetProperties
+import org.apache.parquet.hadoop.ParquetOutputFormat
+import org.apache.parquet.hadoop.api.WriteSupport
+import org.apache.parquet.hadoop.api.WriteSupport.WriteContext
+import org.apache.parquet.io.api.{Binary, RecordConsumer}
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{SpecializedGetters,
SpecificMutableRow}
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import
org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.{MAX_PRECISION_FOR_INT32,
MAX_PRECISION_FOR_INT64, minBytesForPrecision}
+import org.apache.spark.sql.types._
+
+/**
+ * A Parquet [[WriteSupport]] implementation that writes Catalyst
[[InternalRow]]s as Parquet
+ * messages. This class can write Parquet data in two modes:
+ *
+ * - Standard mode: Parquet data are written in standard format defined
in parquet-format spec.
+ * - Legacy mode: Parquet data are written in legacy format compatible
with Spark 1.5 and prior.
+ *
+ * This behavior can be controlled by SQL option
`spark.sql.parquet.writeLegacyParquetFormat`. The
+ * value of the option is propagated to this class by the `init()` method
and its Hadoop
+ * configuration argument.
+ */
+private[parquet] class CatalystWriteSupport extends
WriteSupport[InternalRow] with Logging {
+ // A `ValueWriter` is responsible for writing a field of an
`InternalRow` to the record consumer.
+ // Here we are using `SpecializedGetters` rather than `InternalRow` so
that we can directly access
+ // data in `ArrayData` without the help of `SpecificMutableRow`.
+ private type ValueWriter = (SpecializedGetters, Int) => Unit
+
+ // Schema of the `InternalRow`s to be written
+ private var schema: StructType = _
+
+ // `ValueWriter`s for all fields of the schema
+ private var rootFieldWriters: Seq[ValueWriter] = _
+
+ // The Parquet `RecordConsumer` to which all `InternalRow`s are written
+ private var recordConsumer: RecordConsumer = _
+
+ // Whether to write data in legacy Parquet format compatible with Spark
1.5 and prior versions
+ private var writeLegacyParquetFormat: Boolean = _
+
+ // Reusable byte array used to write timestamps as Parquet INT96 values
+ private val timestampBuffer = new Array[Byte](12)
+
+ // Reusable byte array used to write decimal values
+ private val decimalBuffer = new
Array[Byte](minBytesForPrecision(DecimalType.MAX_PRECISION))
+
+ override def init(configuration: Configuration): WriteContext = {
+ val schemaString =
configuration.get(CatalystWriteSupport.SPARK_ROW_SCHEMA)
+ this.schema = StructType.fromString(schemaString)
+ this.writeLegacyParquetFormat = {
+ // `SQLConf.PARQUET_WRITE_LEGACY_FORMAT` should always be explicitly
set in ParquetRelation
+ assert(configuration.get(SQLConf.PARQUET_WRITE_LEGACY_FORMAT.key) !=
null)
+ configuration.get(SQLConf.PARQUET_WRITE_LEGACY_FORMAT.key).toBoolean
+ }
+ this.rootFieldWriters = schema.map(_.dataType).map(makeWriter)
+
+ val messageType = new
CatalystSchemaConverter(configuration).convert(schema)
+ val metadata = Map(CatalystReadSupport.SPARK_METADATA_KEY ->
schemaString).asJava
+
+ logInfo(
+ s"""Initialized Parquet WriteSupport with Catalyst schema:
+ |${schema.prettyJson}
+ |and corresponding Parquet message type:
+ |$messageType
+ """.stripMargin)
+
+ new WriteContext(messageType, metadata)
+ }
+
+ override def prepareForWrite(recordConsumer: RecordConsumer): Unit = {
+ this.recordConsumer = recordConsumer
+ }
+
+ override def write(row: InternalRow): Unit = {
+ consumeMessage(writeFields(row, schema, rootFieldWriters))
+ }
+
+ private def writeFields(
+ row: InternalRow, schema: StructType, fieldWriters:
Seq[ValueWriter]): Unit = {
+ var i = 0
+ while (i < row.numFields) {
+ if (!row.isNullAt(i)) {
+ consumeField(schema(i).name, i) {
+ fieldWriters(i).apply(row, i)
+ }
+ }
+ i += 1
+ }
+ }
+
+ private def makeWriter(dataType: DataType): ValueWriter = {
+ dataType match {
+ case BooleanType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addBoolean(row.getBoolean(ordinal))
+
+ case ByteType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addInteger(row.getByte(ordinal))
+
+ case ShortType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addInteger(row.getShort(ordinal))
+
+ case IntegerType | DateType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addInteger(row.getInt(ordinal))
+
+ case LongType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addLong(row.getLong(ordinal))
+
+ case FloatType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addFloat(row.getFloat(ordinal))
+
+ case DoubleType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addDouble(row.getDouble(ordinal))
+
+ case StringType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+
recordConsumer.addBinary(Binary.fromByteArray(row.getUTF8String(ordinal).getBytes))
+
+ case TimestampType =>
+ (row: SpecializedGetters, ordinal: Int) => {
+ // TODO Writes `TimestampType` values as `TIMESTAMP_MICROS` once
parquet-mr implements it
+ // Currently we only support timestamps stored as INT96, which
is compatible with Hive
+ // and Impala. However, INT96 is to be deprecated. We plan to
support `TIMESTAMP_MICROS`
+ // defined in the parquet-format spec. But up until writing,
the most recent parquet-mr
+ // version (1.8.1) hasn't implemented it yet.
+
+ // NOTE: Starting from Spark 1.5, Spark SQL `TimestampType` only
has microsecond
+ // precision. Nanosecond parts of timestamp values read from
INT96 are simply stripped.
+ val (julianDay, timeOfDayNanos) =
DateTimeUtils.toJulianDay(row.getLong(ordinal))
+ val buf = ByteBuffer.wrap(timestampBuffer)
+
buf.order(ByteOrder.LITTLE_ENDIAN).putLong(timeOfDayNanos).putInt(julianDay)
+ recordConsumer.addBinary(Binary.fromByteArray(timestampBuffer))
+ }
+
+ case BinaryType =>
+ (row: SpecializedGetters, ordinal: Int) =>
+
recordConsumer.addBinary(Binary.fromByteArray(row.getBinary(ordinal)))
+
+ case DecimalType.Fixed(precision, scale) =>
+ makeDecimalWriter(precision, scale)
+
+ case t: StructType =>
+ val fieldWriters = t.map(_.dataType).map(makeWriter)
+ (row: SpecializedGetters, ordinal: Int) =>
+ consumeGroup(writeFields(row.getStruct(ordinal, t.length), t,
fieldWriters))
+
+ case t: ArrayType => makeArrayWriter(t)
+
+ case t: MapType => makeMapWriter(t)
+
+ case t: UserDefinedType[_] => makeWriter(t.sqlType)
+
+ // TODO Adds IntervalType support
+ case _ => sys.error(s"Unsupported data type $dataType.")
+ }
+ }
+
+ private def makeDecimalWriter(precision: Int, scale: Int): ValueWriter =
{
+ assert(
+ precision <= DecimalType.MAX_PRECISION,
+ s"Decimal precision $precision exceeds max precision
${DecimalType.MAX_PRECISION}")
+
+ val numBytes = minBytesForPrecision(precision)
+
+ val int32Writer =
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addInteger(row.getLong(ordinal).toInt)
+
+ val int64Writer =
+ (row: SpecializedGetters, ordinal: Int) =>
+ recordConsumer.addLong(row.getLong(ordinal))
+
+ val binaryWriterUsingUnscaledLong =
+ (row: SpecializedGetters, ordinal: Int) => {
+ // When the precision is low enough (<= 18) to squeeze the decimal
value into a `Long`, we
+ // can build a fixed-length byte array with length `numBytes`
using the unscaled `Long`
+ // value and the `decimalBuffer` for better performance.
+ val unscaled = row.getDecimal(ordinal, precision,
scale).toUnscaledLong
+ var i = 0
+ var shift = 8 * (numBytes - 1)
+
+ while (i < numBytes) {
+ decimalBuffer(i) = (unscaled >> shift).toByte
+ i += 1
+ shift -= 8
+ }
+
+ recordConsumer.addBinary(Binary.fromByteArray(decimalBuffer, 0,
numBytes))
+ }
+
+ val binaryWriterUsingUnscaledBytes =
+ (row: SpecializedGetters, ordinal: Int) => {
+ val decimal = row.getDecimal(ordinal, precision, scale)
+ val bytes = decimal.toJavaBigDecimal.unscaledValue().toByteArray
+ val fixedLengthBytes = if (bytes.length == numBytes) {
+ // If the length of the underlying byte array of the unscaled
`BigInteger` happens to be
+ // `numBytes`, just reuse it, so that we don't bother copying it
to `decimalBuffer`.
+ bytes
+ } else {
+ // Otherwise, the length must be less than `numBytes`. In this
case we copy contents of
+ // the underlying bytes with padding sign bytes to
`decimalBuffer` to form the result
+ // fixed-length byte array.
+ val signByte = if (bytes.head < 0) -1: Byte else 0: Byte
+ util.Arrays.fill(decimalBuffer, 0, numBytes - bytes.length,
signByte)
+ System.arraycopy(bytes, 0, decimalBuffer, numBytes -
bytes.length, bytes.length)
+ decimalBuffer
+ }
+
+ recordConsumer.addBinary(Binary.fromByteArray(fixedLengthBytes, 0,
numBytes))
+ }
+
+ writeLegacyParquetFormat match {
+ // Standard mode, 1 <= precision <= 9, writes as INT32
+ case false if precision <= MAX_PRECISION_FOR_INT32 => int32Writer
+
+ // Standard mode, 10 <= precision <= 18, writes as INT64
+ case false if precision <= MAX_PRECISION_FOR_INT64 => int64Writer
+
+ // Legacy mode, 1 <= precision <= 18, writes as FIXED_LEN_BYTE_ARRAY
+ case true if precision <= MAX_PRECISION_FOR_INT64 =>
binaryWriterUsingUnscaledLong
+
+ // Either standard or legacy mode, 19 <= precision <= 38, writes as
FIXED_LEN_BYTE_ARRAY
+ case _ => binaryWriterUsingUnscaledBytes
+ }
+ }
+
+ def makeArrayWriter(arrayType: ArrayType): ValueWriter = {
+ val elementWriter = makeWriter(arrayType.elementType)
+
+ def threeLevelArrayWriter(repeatedGroupName: String, elementFieldName:
String): ValueWriter =
+ (row: SpecializedGetters, ordinal: Int) => {
+ val array = row.getArray(ordinal)
+ consumeGroup {
+ // Only creates the repeated field if the array is non-empty.
--- End diff --
Note that this is because Parquet doesn't allow writing empty fields. (But
empty groups are OK.) The same applies to similar code below.
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