[SPARK-2927][SQL] Add a conf to configure if we always read Binary co…

…lumns stored in Parquet as String columns This PR adds a new conf flag `spark.sql.parquet.binaryAsString`. When it is `true`, if there is no parquet metadata file available to provide the schema of the data, we will always treat binary fields stored in parquet as string fields. This conf is used to provide a way to read string fields generated without UTF8 decoration. JIRA: https://issues.apache.org/jira/browse/SPARK-2927 Author: Yin Huai <[email protected]> Closes #1855 from yhuai/parquetBinaryAsString and squashes the following commits: 689ffa9 [Yin Huai] Add missing "=". 80827de [Yin Huai] Unit test. 1765ca4 [Yin Huai] Use .toBoolean. 9d3f199 [Yin Huai] Merge remote-tracking branch 'upstream/master' into parquetBinaryAsString 5d436a1 [Yin Huai] The initial support of adding a conf to treat binary columns stored in Parquet as string columns.
apache · Aug 14, 2014 · add75d4 · add75d4
1 parent 078f3fb
commit add75d4
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Showing 5 changed files with 87 additions and 22 deletions.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -31,6 +31,7 @@ private[spark] object SQLConf {
   val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions"
   val CODEGEN_ENABLED = "spark.sql.codegen"
   val DIALECT = "spark.sql.dialect"
+  val PARQUET_BINARY_AS_STRING = "spark.sql.parquet.binaryAsString"
 
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
@@ -87,8 +88,7 @@ trait SQLConf {
    *
    * Defaults to false as this feature is currently experimental.
    */
-  private[spark] def codegenEnabled: Boolean =
-    if (getConf(CODEGEN_ENABLED, "false") == "true") true else false
+  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, "false").toBoolean
 
   /**
    * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to
@@ -108,6 +108,12 @@ trait SQLConf {
   private[spark] def defaultSizeInBytes: Long =
     getConf(DEFAULT_SIZE_IN_BYTES, (autoBroadcastJoinThreshold + 1).toString).toLong
 
+  /**
+   * When set to true, we always treat byte arrays in Parquet files as strings.
+   */
+  private[spark] def isParquetBinaryAsString: Boolean =
+    getConf(PARQUET_BINARY_AS_STRING, "false").toBoolean
+
   /** ********************** SQLConf functionality methods ************ */
 
   /** Set Spark SQL configuration properties. */

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -60,7 +60,11 @@ private[sql] case class ParquetRelation(
       .getSchema
 
   /** Attributes */
-  override val output = ParquetTypesConverter.readSchemaFromFile(new Path(path), conf)
+  override val output =
+    ParquetTypesConverter.readSchemaFromFile(
+      new Path(path),
+      conf,
+      sqlContext.isParquetBinaryAsString)
 
   override def newInstance = ParquetRelation(path, conf, sqlContext).asInstanceOf[this.type]
 

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
@@ -80,9 +80,10 @@ private[parquet] class RowReadSupport extends ReadSupport[Row] with Logging {
       }
     }
     // if both unavailable, fall back to deducing the schema from the given Parquet schema
+    // TODO: Why it can be null?
     if (schema == null)  {
       log.debug("falling back to Parquet read schema")
-      schema = ParquetTypesConverter.convertToAttributes(parquetSchema)
+      schema = ParquetTypesConverter.convertToAttributes(parquetSchema, false)
     }
     log.debug(s"list of attributes that will be read: $schema")
     new RowRecordMaterializer(parquetSchema, schema)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
@@ -43,10 +43,13 @@ private[parquet] object ParquetTypesConverter extends Logging {
   def isPrimitiveType(ctype: DataType): Boolean =
     classOf[PrimitiveType] isAssignableFrom ctype.getClass
 
-  def toPrimitiveDataType(parquetType: ParquetPrimitiveType): DataType =
+  def toPrimitiveDataType(
+      parquetType: ParquetPrimitiveType,
+      binayAsString: Boolean): DataType =
     parquetType.getPrimitiveTypeName match {
       case ParquetPrimitiveTypeName.BINARY
-        if parquetType.getOriginalType == ParquetOriginalType.UTF8 => StringType
+        if (parquetType.getOriginalType == ParquetOriginalType.UTF8 ||
+          binayAsString) => StringType
       case ParquetPrimitiveTypeName.BINARY => BinaryType
       case ParquetPrimitiveTypeName.BOOLEAN => BooleanType
       case ParquetPrimitiveTypeName.DOUBLE => DoubleType
@@ -85,7 +88,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
    * @param parquetType The type to convert.
    * @return The corresponding Catalyst type.
    */
-  def toDataType(parquetType: ParquetType): DataType = {
+  def toDataType(parquetType: ParquetType, isBinaryAsString: Boolean): DataType = {
     def correspondsToMap(groupType: ParquetGroupType): Boolean = {
       if (groupType.getFieldCount != 1 || groupType.getFields.apply(0).isPrimitive) {
         false
@@ -107,7 +110,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
     }
 
     if (parquetType.isPrimitive) {
-      toPrimitiveDataType(parquetType.asPrimitiveType)
+      toPrimitiveDataType(parquetType.asPrimitiveType, isBinaryAsString)
     } else {
       val groupType = parquetType.asGroupType()
       parquetType.getOriginalType match {
@@ -116,7 +119,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
         case ParquetOriginalType.LIST => { // TODO: check enums!
           assert(groupType.getFieldCount == 1)
           val field = groupType.getFields.apply(0)
-          ArrayType(toDataType(field), containsNull = false)
+          ArrayType(toDataType(field, isBinaryAsString), containsNull = false)
         }
         case ParquetOriginalType.MAP => {
           assert(
@@ -126,9 +129,9 @@ private[parquet] object ParquetTypesConverter extends Logging {
           assert(
             keyValueGroup.getFieldCount == 2,
             "Parquet Map type malformatted: nested group should have 2 (key, value) fields!")
-          val keyType = toDataType(keyValueGroup.getFields.apply(0))
+          val keyType = toDataType(keyValueGroup.getFields.apply(0), isBinaryAsString)
           assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
-          val valueType = toDataType(keyValueGroup.getFields.apply(1))
+          val valueType = toDataType(keyValueGroup.getFields.apply(1), isBinaryAsString)
           assert(keyValueGroup.getFields.apply(1).getRepetition == Repetition.REQUIRED)
           // TODO: set valueContainsNull explicitly instead of assuming valueContainsNull is true
           // at here.
@@ -138,22 +141,22 @@ private[parquet] object ParquetTypesConverter extends Logging {
           // 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))
+            val keyType = toDataType(keyValueGroup.getFields.apply(0), isBinaryAsString)
             assert(keyValueGroup.getFields.apply(0).getRepetition == Repetition.REQUIRED)
-            val valueType = toDataType(keyValueGroup.getFields.apply(1))
+            val valueType = toDataType(keyValueGroup.getFields.apply(1), isBinaryAsString)
             assert(keyValueGroup.getFields.apply(1).getRepetition == Repetition.REQUIRED)
             // TODO: set valueContainsNull explicitly instead of assuming valueContainsNull is true
             // at here.
             MapType(keyType, valueType)
           } else if (correspondsToArray(groupType)) { // ArrayType
-            val elementType = toDataType(groupType.getFields.apply(0))
+            val elementType = toDataType(groupType.getFields.apply(0), isBinaryAsString)
             ArrayType(elementType, containsNull = false)
           } else { // everything else: StructType
             val fields = groupType
               .getFields
               .map(ptype => new StructField(
               ptype.getName,
-              toDataType(ptype),
+              toDataType(ptype, isBinaryAsString),
               ptype.getRepetition != Repetition.REQUIRED))
             StructType(fields)
           }
@@ -276,15 +279,15 @@ private[parquet] object ParquetTypesConverter extends Logging {
     }
   }
 
-  def convertToAttributes(parquetSchema: ParquetType): Seq[Attribute] = {
+  def convertToAttributes(parquetSchema: ParquetType, isBinaryAsString: Boolean): Seq[Attribute] = {
     parquetSchema
       .asGroupType()
       .getFields
       .map(
         field =>
           new AttributeReference(
             field.getName,
-            toDataType(field),
+            toDataType(field, isBinaryAsString),
             field.getRepetition != Repetition.REQUIRED)())
   }
 
@@ -404,7 +407,10 @@ private[parquet] object ParquetTypesConverter extends Logging {
    * @param conf The Hadoop configuration to use.
    * @return A list of attributes that make up the schema.
    */
-  def readSchemaFromFile(origPath: Path, conf: Option[Configuration]): Seq[Attribute] = {
+  def readSchemaFromFile(
+      origPath: Path,
+      conf: Option[Configuration],
+      isBinaryAsString: Boolean): Seq[Attribute] = {
     val keyValueMetadata: java.util.Map[String, String] =
       readMetaData(origPath, conf)
         .getFileMetaData
@@ -413,7 +419,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
       convertFromString(keyValueMetadata.get(RowReadSupport.SPARK_METADATA_KEY))
     } else {
       val attributes = convertToAttributes(
-        readMetaData(origPath, conf).getFileMetaData.getSchema)
+        readMetaData(origPath, conf).getFileMetaData.getSchema, isBinaryAsString)
       log.info(s"Falling back to schema conversion from Parquet types; result: $attributes")
       attributes
     }

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -21,8 +21,6 @@ import org.scalatest.{BeforeAndAfterAll, FunSuiteLike}
 
 import parquet.hadoop.ParquetFileWriter
 import parquet.hadoop.util.ContextUtil
-import parquet.schema.MessageTypeParser
-
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.mapreduce.Job
 
@@ -33,7 +31,6 @@ import org.apache.spark.sql.catalyst.analysis.{Star, UnresolvedAttribute}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.types.{BooleanType, IntegerType}
 import org.apache.spark.sql.catalyst.util.getTempFilePath
-import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext._
 import org.apache.spark.util.Utils
@@ -138,6 +135,57 @@ class ParquetQuerySuite extends QueryTest with FunSuiteLike with BeforeAndAfterA
     }
   }
 
+  test("Treat binary as string") {
+    val oldIsParquetBinaryAsString = TestSQLContext.isParquetBinaryAsString
+
+    // Create the test file.
+    val file = getTempFilePath("parquet")
+    val path = file.toString
+    val range = (0 to 255)
+    val rowRDD = TestSQLContext.sparkContext.parallelize(range)
+      .map(i => org.apache.spark.sql.Row(i, s"val_$i".getBytes))
+    // We need to ask Parquet to store the String column as a Binary column.
+    val schema = StructType(
+      StructField("c1", IntegerType, false) ::
+      StructField("c2", BinaryType, false) :: Nil)
+    val schemaRDD1 = applySchema(rowRDD, schema)
+    schemaRDD1.saveAsParquetFile(path)
+    val resultWithBinary = parquetFile(path).collect
+    range.foreach {
+      i =>
+        assert(resultWithBinary(i).getInt(0) === i)
+        assert(resultWithBinary(i)(1) === s"val_$i".getBytes)
+    }
+
+    TestSQLContext.setConf(SQLConf.PARQUET_BINARY_AS_STRING, "true")
+    // This ParquetRelation always use Parquet types to derive output.
+    val parquetRelation = new ParquetRelation(
+      path.toString,
+      Some(TestSQLContext.sparkContext.hadoopConfiguration),
+      TestSQLContext) {
+      override val output =
+        ParquetTypesConverter.convertToAttributes(
+          ParquetTypesConverter.readMetaData(new Path(path), conf).getFileMetaData.getSchema,
+          TestSQLContext.isParquetBinaryAsString)
+    }
+    val schemaRDD = new SchemaRDD(TestSQLContext, parquetRelation)
+    val resultWithString = schemaRDD.collect
+    range.foreach {
+      i =>
+        assert(resultWithString(i).getInt(0) === i)
+        assert(resultWithString(i)(1) === s"val_$i")
+    }
+
+    schemaRDD.registerTempTable("tmp")
+    checkAnswer(
+      sql("SELECT c1, c2 FROM tmp WHERE c2 = 'val_5' OR c2 = 'val_7'"),
+      (5, "val_5") ::
+      (7, "val_7") :: Nil)
+
+    // Set it back.
+    TestSQLContext.setConf(SQLConf.PARQUET_BINARY_AS_STRING, oldIsParquetBinaryAsString.toString)
+  }
+
   test("Read/Write All Types with non-primitive type") {
     val tempDir = getTempFilePath("parquetTest").getCanonicalPath
     val range = (0 to 255)