Github user aarondav commented on a diff in the pull request:
https://github.com/apache/spark/pull/360#discussion_r13661910
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala ---
@@ -0,0 +1,409 @@
+/*
+ * 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.parquet
+
+import java.io.IOException
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.mapreduce.Job
+
+import parquet.hadoop.{ParquetFileReader, Footer, ParquetFileWriter}
+import parquet.hadoop.metadata.{ParquetMetadata, FileMetaData}
+import parquet.hadoop.util.ContextUtil
+import parquet.schema.{Type => ParquetType, PrimitiveType =>
ParquetPrimitiveType, MessageType}
+import parquet.schema.{GroupType => ParquetGroupType, OriginalType =>
ParquetOriginalType, ConversionPatterns}
+import parquet.schema.PrimitiveType.{PrimitiveTypeName =>
ParquetPrimitiveTypeName}
+import parquet.schema.Type.Repetition
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
Attribute}
+import org.apache.spark.sql.catalyst.types._
+
+// Implicits
+import scala.collection.JavaConversions._
+
+private[parquet] object ParquetTypesConverter extends Logging {
+ def isPrimitiveType(ctype: DataType): Boolean =
+ classOf[PrimitiveType] isAssignableFrom ctype.getClass
+
+ def toPrimitiveDataType(parquetType : ParquetPrimitiveTypeName):
DataType = parquetType match {
+ case ParquetPrimitiveTypeName.BINARY => StringType
+ case ParquetPrimitiveTypeName.BOOLEAN => BooleanType
+ case ParquetPrimitiveTypeName.DOUBLE => DoubleType
+ case ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY =>
ArrayType(ByteType)
+ case ParquetPrimitiveTypeName.FLOAT => FloatType
+ case ParquetPrimitiveTypeName.INT32 => IntegerType
+ case ParquetPrimitiveTypeName.INT64 => LongType
+ case ParquetPrimitiveTypeName.INT96 =>
+ // TODO: add BigInteger type? TODO(andre) use DecimalType instead????
+ sys.error("Potential loss of precision: cannot convert INT96")
+ case _ => sys.error(
+ s"Unsupported parquet datatype $parquetType")
+ }
+
+ /**
+ * Converts a given Parquet `Type` into the corresponding
+ * [[org.apache.spark.sql.catalyst.types.DataType]].
+ *
+ * We apply the following conversion rules:
+ * <ul>
+ * <li> Primitive types are converter to the corresponding primitive
type.</li>
+ * <li> Group types that have a single field that is itself a group,
which has repetition
+ * level `REPEATED`, are treated as follows:<ul>
+ * <li> If the nested group has name `values`, the surrounding
group is converted
+ * into an [[ArrayType]] with the corresponding field type
(primitive or
+ * complex) as element type.</li>
+ * <li> If the nested group has name `map` and two fields
(named `key` and `value`),
+ * the surrounding group is converted into a [[MapType]]
+ * with the corresponding key and value (value possibly
complex) types.
+ * Note that we currently assume map values are not
nullable.</li>
+ * <li> Other group types are converted into a [[StructType]] with the
corresponding
+ * field types.</li></ul></li>
+ * </ul>
+ * Note that fields are determined to be `nullable` if and only if their
Parquet repetition
+ * level is not `REQUIRED`.
+ *
+ * @param parquetType The type to convert.
+ * @return The corresponding Catalyst type.
+ */
+ def toDataType(parquetType: ParquetType): DataType = {
+ def correspondsToMap(groupType: ParquetGroupType): Boolean = {
+ if (groupType.getFieldCount != 1 ||
groupType.getFields.apply(0).isPrimitive) {
+ false
+ } else {
+ // This mostly follows the convention in
``parquet.schema.ConversionPatterns``
+ val keyValueGroup = groupType.getFields.apply(0).asGroupType()
+ keyValueGroup.getRepetition == Repetition.REPEATED &&
+ keyValueGroup.getName == CatalystConverter.MAP_SCHEMA_NAME &&
+ keyValueGroup.getFieldCount == 2 &&
+ keyValueGroup.getFields.apply(0).getName ==
CatalystConverter.MAP_KEY_SCHEMA_NAME &&
+ keyValueGroup.getFields.apply(1).getName ==
CatalystConverter.MAP_VALUE_SCHEMA_NAME
+ }
+ }
+
+ def correspondsToArray(groupType: ParquetGroupType): Boolean = {
+ groupType.getFieldCount == 1 &&
+ groupType.getFieldName(0) ==
CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME &&
+ groupType.getFields.apply(0).getRepetition == Repetition.REPEATED
+ }
+
+ if (parquetType.isPrimitive) {
+ toPrimitiveDataType(parquetType.asPrimitiveType.getPrimitiveTypeName)
+ } else {
+ val groupType = parquetType.asGroupType()
+ parquetType.getOriginalType match {
+ // if the schema was constructed programmatically there may be
hints how to convert
+ // it inside the metadata via the OriginalType field
+ case ParquetOriginalType.LIST => { // TODO: check enums!
+ assert(groupType.getFieldCount == 1)
+ val field = groupType.getFields.apply(0)
+ new ArrayType(toDataType(field))
+ }
+ case ParquetOriginalType.MAP => {
+ assert(
+ !groupType.getFields.apply(0).isPrimitive,
+ "Parquet Map type malformatted: expected nested group for
map!")
+ val keyValueGroup = groupType.getFields.apply(0).asGroupType()
+ assert(
+ keyValueGroup.getFieldCount == 2,
+ "Parquet Map type malformatted: nested group should have 2
(key, value) fields!")
+ val keyType = toDataType(keyValueGroup.getFields.apply(0))
+ assert(keyValueGroup.getFields.apply(0).getRepetition ==
Repetition.REQUIRED)
+ val valueType = toDataType(keyValueGroup.getFields.apply(1))
+ assert(keyValueGroup.getFields.apply(1).getRepetition ==
Repetition.REQUIRED)
+ new MapType(keyType, valueType)
+ }
+ case _ => {
+ // Note: the order of these checks is important!
+ if (correspondsToMap(groupType)) { // MapType
+ val keyValueGroup = groupType.getFields.apply(0).asGroupType()
+ val keyType = toDataType(keyValueGroup.getFields.apply(0))
+ assert(keyValueGroup.getFields.apply(0).getRepetition ==
Repetition.REQUIRED)
+ val valueType = toDataType(keyValueGroup.getFields.apply(1))
+ assert(keyValueGroup.getFields.apply(1).getRepetition ==
Repetition.REQUIRED)
+ new MapType(keyType, valueType)
+ } else if (correspondsToArray(groupType)) { // ArrayType
+ val elementType = toDataType(groupType.getFields.apply(0))
+ new ArrayType(elementType)
+ } else { // everything else: StructType
+ val fields = groupType
+ .getFields
+ .map(ptype => new StructField(
+ ptype.getName,
+ toDataType(ptype),
+ ptype.getRepetition != Repetition.REQUIRED))
+ new StructType(fields)
+ }
+ }
+ }
+ }
+ }
+
+ /**
+ * For a given Catalyst [[org.apache.spark.sql.catalyst.types.DataType]]
return
+ * the name of the corresponding Parquet primitive type or None if the
given type
+ * is not primitive.
+ *
+ * @param ctype The type to convert
+ * @return The name of the corresponding Parquet primitive type
+ */
+ def fromPrimitiveDataType(ctype: DataType):
+ Option[ParquetPrimitiveTypeName] = ctype match {
+ case StringType => Some(ParquetPrimitiveTypeName.BINARY)
+ case BooleanType => Some(ParquetPrimitiveTypeName.BOOLEAN)
+ case DoubleType => Some(ParquetPrimitiveTypeName.DOUBLE)
+ case ArrayType(ByteType) =>
+ Some(ParquetPrimitiveTypeName.FIXED_LEN_BYTE_ARRAY)
+ case FloatType => Some(ParquetPrimitiveTypeName.FLOAT)
+ case IntegerType => Some(ParquetPrimitiveTypeName.INT32)
+ // There is no type for Byte or Short so we promote them to INT32.
+ case ShortType => Some(ParquetPrimitiveTypeName.INT32)
+ case ByteType => Some(ParquetPrimitiveTypeName.INT32)
+ case LongType => Some(ParquetPrimitiveTypeName.INT64)
+ case _ => None
+ }
+
+ /**
+ * Converts a given Catalyst
[[org.apache.spark.sql.catalyst.types.DataType]] into
+ * the corresponding Parquet `Type`.
+ *
+ * The conversion follows the rules below:
+ * <ul>
+ * <li> Primitive types are converted into Parquet's primitive
types.</li>
+ * <li> [[org.apache.spark.sql.catalyst.types.StructType]]s are
converted
+ * into Parquet's `GroupType` with the corresponding field
types.</li>
+ * <li> [[org.apache.spark.sql.catalyst.types.ArrayType]]s are
converted
+ * into a 2-level nested group, where the outer group has the
inner
+ * group as sole field. The inner group has name `values` and
+ * repetition level `REPEATED` and has the element type of
+ * the array as schema. We use Parquet's `ConversionPatterns` for
this
+ * purpose.</li>
+ * <li> [[org.apache.spark.sql.catalyst.types.MapType]]s are converted
+ * into a nested (2-level) Parquet `GroupType` with two fields: a
key
+ * type and a value type. The nested group has repetition level
+ * `REPEATED` and name `map`. We use Parquet's
`ConversionPatterns`
+ * for this purpose</li>
+ * </ul>
+ * Parquet's repetition level is generally set according to the
following rule:
+ * <ul>
+ * <li> If the call to `fromDataType` is recursive inside an enclosing
`ArrayType` or
+ * `MapType`, then the repetition level is set to `REPEATED`.</li>
+ * <li> Otherwise, if the attribute whose type is converted is
`nullable`, the Parquet
+ * type gets repetition level `OPTIONAL` and otherwise `REQUIRED`.</li>
+ * </ul>
+ *
+ *@param ctype The type to convert
+ * @param name The name of the
[[org.apache.spark.sql.catalyst.expressions.Attribute]]
+ * whose type is converted
+ * @param nullable When true indicates that the attribute is nullable
+ * @param inArray When true indicates that this is a nested attribute
inside an array.
+ * @return The corresponding Parquet type.
+ */
+ def fromDataType(
+ ctype: DataType,
+ name: String,
+ nullable: Boolean = true,
+ inArray: Boolean = false): ParquetType = {
+ val repetition =
+ if (inArray) {
+ Repetition.REPEATED
+ } else {
+ if (nullable) Repetition.OPTIONAL else Repetition.REQUIRED
+ }
+ val primitiveType = fromPrimitiveDataType(ctype)
+ if (primitiveType.isDefined) {
+ new ParquetPrimitiveType(repetition, primitiveType.get, name)
+ } else {
+ ctype match {
+ case ArrayType(elementType) => {
+ val parquetElementType = fromDataType(
+ elementType,
+ CatalystConverter.ARRAY_ELEMENTS_SCHEMA_NAME,
+ nullable = false,
+ inArray = true)
+ ConversionPatterns.listType(repetition, name,
parquetElementType)
+ }
+ case StructType(structFields) => {
+ val fields = structFields.map {
+ field => fromDataType(field.dataType, field.name,
field.nullable, inArray = false)
+ }
+ new ParquetGroupType(repetition, name, fields)
+ }
+ case MapType(keyType, valueType) => {
+ val parquetKeyType =
+ fromDataType(
+ keyType,
+ CatalystConverter.MAP_KEY_SCHEMA_NAME,
+ nullable = false,
+ inArray = false)
+ val parquetValueType =
+ fromDataType(
+ valueType,
+ CatalystConverter.MAP_VALUE_SCHEMA_NAME,
+ nullable = false,
+ inArray = false)
+ ConversionPatterns.mapType(
+ repetition,
+ name,
+ parquetKeyType,
+ parquetValueType)
+ }
+ case _ => sys.error(s"Unsupported datatype $ctype")
+ }
+ }
+ }
+
+ def convertToAttributes(parquetSchema: ParquetType): Seq[Attribute] = {
+ parquetSchema
+ .asGroupType()
+ .getFields
+ .map(
+ field =>
+ new AttributeReference(
+ field.getName,
+ toDataType(field),
+ field.getRepetition != Repetition.REQUIRED)())
+ }
+
+ def convertFromAttributes(attributes: Seq[Attribute]): MessageType = {
+ val fields = attributes.map(
+ attribute =>
+ fromDataType(attribute.dataType, attribute.name,
attribute.nullable))
+ new MessageType("root", fields)
+ }
+
+ def convertFromString(string: String): Seq[Attribute] = {
+ DataType(string) match {
+ case s: StructType => s.toAttributes
+ case other => sys.error(s"Can convert $string to row")
+ }
+ }
+
+ def convertToString(schema: Seq[Attribute]): String = {
+ StructType.fromAttributes(schema).toString
+ }
+
+ def writeMetaData(attributes: Seq[Attribute], origPath: Path, conf:
Configuration): Unit = {
+ if (origPath == null) {
+ throw new IllegalArgumentException("Unable to write Parquet
metadata: path is null")
+ }
+ val fs = origPath.getFileSystem(conf)
+ if (fs == null) {
+ throw new IllegalArgumentException(
+ s"Unable to write Parquet metadata: path $origPath is incorrectly
formatted")
+ }
+ val path = origPath.makeQualified(fs)
+ if (fs.exists(path) && !fs.getFileStatus(path).isDir) {
+ throw new IllegalArgumentException(s"Expected to write to directory
$path but found file")
+ }
+ val metadataPath = new Path(path,
ParquetFileWriter.PARQUET_METADATA_FILE)
+ if (fs.exists(metadataPath)) {
+ try {
+ fs.delete(metadataPath, true)
+ } catch {
+ case e: IOException =>
+ throw new IOException(s"Unable to delete previous
PARQUET_METADATA_FILE at $metadataPath")
+ }
+ }
+ val extraMetadata = new java.util.HashMap[String, String]()
+ extraMetadata.put("path", path.toString)
+ extraMetadata.put(
+ RowReadSupport.SPARK_METADATA_KEY,
+ ParquetTypesConverter.convertToString(attributes))
+ // TODO: add extra data, e.g., table name, date, etc.?
+
+ val parquetSchema: MessageType =
+ ParquetTypesConverter.convertFromAttributes(attributes)
+ val metaData: FileMetaData = new FileMetaData(
+ parquetSchema,
+ extraMetadata,
+ "Spark")
+
+ ParquetRelation.enableLogForwarding()
+ ParquetFileWriter.writeMetadataFile(
+ conf,
+ path,
+ new Footer(path, new ParquetMetadata(metaData, Nil)) :: Nil)
+ }
+
+ /**
+ * Try to read Parquet metadata at the given Path. We first see if there
is a summary file
+ * in the parent directory. If so, this is used. Else we read the actual
footer at the given
+ * location.
+ * @param origPath The path at which we expect one (or more) Parquet
files.
+ * @return The `ParquetMetadata` containing among other things the
schema.
+ */
+ def readMetaData(origPath: Path): ParquetMetadata = {
+ if (origPath == null) {
+ throw new IllegalArgumentException("Unable to read Parquet metadata:
path is null")
+ }
+ val job = new Job()
+ // TODO: since this is called from ParquetRelation (LogicalPlan) we
don't have access
+ // to SparkContext's hadoopConfig; in principle the default FileSystem
may be different(?!)
--- End diff --
This was not changed in this PR, but this actually makes it so that we
can't use S3 credentials set in the SparkContext, or add new FileSystems at
runtime, for instance.
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