[SPARK-13543][SQL] Support for specifying compression codec for Parqu…

…et/ORC via option()

## What changes were proposed in this pull request?

This PR adds the support to specify compression codecs for both ORC and Parquet.

## How was this patch tested?

unittests within IDE and code style tests with `dev/run_tests`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11464 from HyukjinKwon/SPARK-13543.

python/pyspark/sql/readwriter.py

@@ -454,7 +454,7 @@ def saveAsTable(self, name, format=None, mode=None, partitionBy=None, **options)
         self._jwrite.saveAsTable(name)
 
     @since(1.4)
-    def json(self, path, mode=None):
+    def json(self, path, mode=None, compression=None):
         """Saves the content of the :class:`DataFrame` in JSON format at the specified path.
 
         :param path: the path in any Hadoop supported file system
@@ -464,18 +464,19 @@ def json(self, path, mode=None):
             * ``overwrite``: Overwrite existing data.
             * ``ignore``: Silently ignore this operation if data already exists.
             * ``error`` (default case): Throw an exception if data already exists.
-
-        You can set the following JSON-specific option(s) for writing JSON files:
-            * ``compression`` (default ``None``): compression codec to use when saving to file.
-            This can be one of the known case-insensitive shorten names
-            (``bzip2``, ``gzip``, ``lz4``, and ``snappy``).
+        :param compression: compression codec to use when saving to file. This can be one of the
+                            known case-insensitive shorten names (none, bzip2, gzip, lz4,
+                            snappy and deflate).
 
         >>> df.write.json(os.path.join(tempfile.mkdtemp(), 'data'))
         """
-        self.mode(mode)._jwrite.json(path)
+        self.mode(mode)
+        if compression is not None:
+            self.option("compression", compression)
+        self._jwrite.json(path)
 
     @since(1.4)
-    def parquet(self, path, mode=None, partitionBy=None):
+    def parquet(self, path, mode=None, partitionBy=None, compression=None):
         """Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
 
         :param path: the path in any Hadoop supported file system
@@ -486,32 +487,37 @@ def parquet(self, path, mode=None, partitionBy=None):
             * ``ignore``: Silently ignore this operation if data already exists.
             * ``error`` (default case): Throw an exception if data already exists.
         :param partitionBy: names of partitioning columns
+        :param compression: compression codec to use when saving to file. This can be one of the
+                            known case-insensitive shorten names (none, snappy, gzip, and lzo).
+                            This will overwrite ``spark.sql.parquet.compression.codec``.
 
         >>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
         """
         self.mode(mode)
         if partitionBy is not None:
             self.partitionBy(partitionBy)
+        if compression is not None:
+            self.option("compression", compression)
         self._jwrite.parquet(path)
 
     @since(1.6)
-    def text(self, path):
+    def text(self, path, compression=None):
         """Saves the content of the DataFrame in a text file at the specified path.
 
         :param path: the path in any Hadoop supported file system
+        :param compression: compression codec to use when saving to file. This can be one of the
+                            known case-insensitive shorten names (none, bzip2, gzip, lz4,
+                            snappy and deflate).
 
         The DataFrame must have only one column that is of string type.
         Each row becomes a new line in the output file.
-
-        You can set the following option(s) for writing text files:
-            * ``compression`` (default ``None``): compression codec to use when saving to file.
-            This can be one of the known case-insensitive shorten names
-            (``bzip2``, ``gzip``, ``lz4``, and ``snappy``).
         """
+        if compression is not None:
+            self.option("compression", compression)
         self._jwrite.text(path)
 
     @since(2.0)
-    def csv(self, path, mode=None):
+    def csv(self, path, mode=None, compression=None):
         """Saves the content of the [[DataFrame]] in CSV format at the specified path.
 
         :param path: the path in any Hadoop supported file system
@@ -522,17 +528,19 @@ def csv(self, path, mode=None):
             * ``ignore``: Silently ignore this operation if data already exists.
             * ``error`` (default case): Throw an exception if data already exists.
 
-        You can set the following CSV-specific option(s) for writing CSV files:
-            * ``compression`` (default ``None``): compression codec to use when saving to file.
-            This can be one of the known case-insensitive shorten names
-            (``bzip2``, ``gzip``, ``lz4``, and ``snappy``).
+        :param compression: compression codec to use when saving to file. This can be one of the
+                            known case-insensitive shorten names (none, bzip2, gzip, lz4,
+                            snappy and deflate).
 
         >>> df.write.csv(os.path.join(tempfile.mkdtemp(), 'data'))
         """
-        self.mode(mode)._jwrite.csv(path)
+        self.mode(mode)
+        if compression is not None:
+            self.option("compression", compression)
+        self._jwrite.csv(path)
 
     @since(1.5)
-    def orc(self, path, mode=None, partitionBy=None):
+    def orc(self, path, mode=None, partitionBy=None, compression=None):
         """Saves the content of the :class:`DataFrame` in ORC format at the specified path.
 
         ::Note: Currently ORC support is only available together with
@@ -546,13 +554,18 @@ def orc(self, path, mode=None, partitionBy=None):
             * ``ignore``: Silently ignore this operation if data already exists.
             * ``error`` (default case): Throw an exception if data already exists.
         :param partitionBy: names of partitioning columns
+        :param compression: compression codec to use when saving to file. This can be one of the
+                            known case-insensitive shorten names (none, snappy, zlib, and lzo).
+                            This will overwrite ``orc.compress``.
 
         >>> orc_df = hiveContext.read.orc('python/test_support/sql/orc_partitioned')
         >>> orc_df.write.orc(os.path.join(tempfile.mkdtemp(), 'data'))
         """
         self.mode(mode)
         if partitionBy is not None:
             self.partitionBy(partitionBy)
+        if compression is not None:
+            self.option("compression", compression)
         self._jwrite.orc(path)
 
     @since(1.4)

sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala

@@ -455,7 +455,8 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    *
    * You can set the following JSON-specific option(s) for writing JSON files:
    * <li>`compression` (default `null`): compression codec to use when saving to file. This can be
-   * one of the known case-insensitive shorten names (`bzip2`, `gzip`, `lz4`, and `snappy`). </li>
+   * one of the known case-insensitive shorten names (`none`, `bzip2`, `gzip`, `lz4`,
+   * `snappy` and `deflate`). </li>
    *
    * @since 1.4.0
    */
@@ -468,6 +469,11 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    *   format("parquet").save(path)
    * }}}
    *
+   * You can set the following Parquet-specific option(s) for writing Parquet files:
+   * <li>`compression` (default `null`): compression codec to use when saving to file. This can be
+   * one of the known case-insensitive shorten names(`none`, `snappy`, `gzip`, and `lzo`).
+   * This will overwrite `spark.sql.parquet.compression.codec`. </li>
+   *
    * @since 1.4.0
    */
   def parquet(path: String): Unit = format("parquet").save(path)
@@ -479,6 +485,11 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    *   format("orc").save(path)
    * }}}
    *
+   * You can set the following ORC-specific option(s) for writing ORC files:
+   * <li>`compression` (default `null`): compression codec to use when saving to file. This can be
+   * one of the known case-insensitive shorten names(`none`, `snappy`, `zlib`, and `lzo`).
+   * This will overwrite `orc.compress`. </li>
+   *
    * @since 1.5.0
    * @note Currently, this method can only be used together with `HiveContext`.
    */
@@ -498,7 +509,8 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    *
    * You can set the following option(s) for writing text files:
    * <li>`compression` (default `null`): compression codec to use when saving to file. This can be
-   * one of the known case-insensitive shorten names (`bzip2`, `gzip`, `lz4`, and `snappy`). </li>
+   * one of the known case-insensitive shorten names (`none`, `bzip2`, `gzip`, `lz4`,
+   * `snappy` and `deflate`). </li>
    *
    * @since 1.6.0
    */
@@ -513,7 +525,8 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    *
    * You can set the following CSV-specific option(s) for writing CSV files:
    * <li>`compression` (default `null`): compression codec to use when saving to file. This can be
-   * one of the known case-insensitive shorten names (`bzip2`, `gzip`, `lz4`, and `snappy`). </li>
+   * one of the known case-insensitive shorten names (`none`, `bzip2`, `gzip`, `lz4`,
+   * `snappy` and `deflate`). </li>
    *
    * @since 2.0.0
    */

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/CompressionCodecs.scala

@@ -25,6 +25,8 @@ import org.apache.spark.util.Utils
 
 private[datasources] object CompressionCodecs {
   private val shortCompressionCodecNames = Map(
+    "none" -> null,
+    "uncompressed" -> null,
     "bzip2" -> classOf[BZip2Codec].getName,
     "deflate" -> classOf[DeflateCodec].getName,
     "gzip" -> classOf[GzipCodec].getName,
@@ -39,7 +41,9 @@ private[datasources] object CompressionCodecs {
     val codecName = shortCompressionCodecNames.getOrElse(name.toLowerCase, name)
     try {
       // Validate the codec name
-      Utils.classForName(codecName)
+      if (codecName != null) {
+        Utils.classForName(codecName)
+      }
       codecName
     } catch {
       case e: ClassNotFoundException =>
@@ -53,10 +57,16 @@ private[datasources] object CompressionCodecs {
    * `codec` should be a full class path
    */
   def setCodecConfiguration(conf: Configuration, codec: String): Unit = {
-    conf.set("mapreduce.output.fileoutputformat.compress", "true")
-    conf.set("mapreduce.output.fileoutputformat.compress.type", CompressionType.BLOCK.toString)
-    conf.set("mapreduce.output.fileoutputformat.compress.codec", codec)
-    conf.set("mapreduce.map.output.compress", "true")
-    conf.set("mapreduce.map.output.compress.codec", codec)
+    if (codec != null){
+      conf.set("mapreduce.output.fileoutputformat.compress", "true")
+      conf.set("mapreduce.output.fileoutputformat.compress.type", CompressionType.BLOCK.toString)
+      conf.set("mapreduce.output.fileoutputformat.compress.codec", codec)
+      conf.set("mapreduce.map.output.compress", "true")
+      conf.set("mapreduce.map.output.compress.codec", codec)
+    } else {
+      // This infers the option `compression` is set to `uncompressed` or `none`.
+      conf.set("mapreduce.output.fileoutputformat.compress", "false")
+      conf.set("mapreduce.map.output.compress", "false")
+    }
   }
 }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala

@@ -148,6 +148,19 @@ private[sql] class ParquetRelation(
     .get(ParquetRelation.METASTORE_SCHEMA)
     .map(DataType.fromJson(_).asInstanceOf[StructType])
 
+  private val compressionCodec: Option[String] = parameters
+    .get("compression")
+    .map { codecName =>
+      // Validate if given compression codec is supported or not.
+      val shortParquetCompressionCodecNames = ParquetRelation.shortParquetCompressionCodecNames
+      if (!shortParquetCompressionCodecNames.contains(codecName.toLowerCase)) {
+        val availableCodecs = shortParquetCompressionCodecNames.keys.map(_.toLowerCase)
+        throw new IllegalArgumentException(s"Codec [$codecName] " +
+          s"is not available. Available codecs are ${availableCodecs.mkString(", ")}.")
+      }
+      codecName.toLowerCase
+    }
+
   private lazy val metadataCache: MetadataCache = {
     val meta = new MetadataCache
     meta.refresh()
@@ -286,7 +299,8 @@ private[sql] class ParquetRelation(
       ParquetRelation
         .shortParquetCompressionCodecNames
         .getOrElse(
-          sqlContext.conf.parquetCompressionCodec.toLowerCase(),
+          compressionCodec
+            .getOrElse(sqlContext.conf.parquetCompressionCodec.toLowerCase),
           CompressionCodecName.UNCOMPRESSED).name())
 
     new BucketedOutputWriterFactory {

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVSuite.scala

@@ -21,6 +21,12 @@ import java.io.File
 import java.nio.charset.UnsupportedCharsetException
 import java.sql.Timestamp
 
+import scala.collection.JavaConverters._
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.io.SequenceFile.CompressionType
+import org.apache.hadoop.io.compress.GzipCodec
+
 import org.apache.spark.SparkException
 import org.apache.spark.sql.{DataFrame, QueryTest, Row}
 import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils}
@@ -396,6 +402,46 @@ class CSVSuite extends QueryTest with SharedSQLContext with SQLTestUtils {
     }
   }
 
+  test("SPARK-13543 Write the output as uncompressed via option()") {
+    val clonedConf = new Configuration(hadoopConfiguration)
+    hadoopConfiguration.set("mapreduce.output.fileoutputformat.compress", "true")
+    hadoopConfiguration
+      .set("mapreduce.output.fileoutputformat.compress.type", CompressionType.BLOCK.toString)
+    hadoopConfiguration
+      .set("mapreduce.output.fileoutputformat.compress.codec", classOf[GzipCodec].getName)
+    hadoopConfiguration.set("mapreduce.map.output.compress", "true")
+    hadoopConfiguration.set("mapreduce.map.output.compress.codec", classOf[GzipCodec].getName)
+    withTempDir { dir =>
+      try {
+        val csvDir = new File(dir, "csv").getCanonicalPath
+        val cars = sqlContext.read
+          .format("csv")
+          .option("header", "true")
+          .load(testFile(carsFile))
+
+        cars.coalesce(1).write
+          .format("csv")
+          .option("header", "true")
+          .option("compression", "none")
+          .save(csvDir)
+
+        val compressedFiles = new File(csvDir).listFiles()
+        assert(compressedFiles.exists(!_.getName.endsWith(".gz")))
+
+        val carsCopy = sqlContext.read
+          .format("csv")
+          .option("header", "true")
+          .load(csvDir)
+
+        verifyCars(carsCopy, withHeader = true)
+      } finally {
+        // Hadoop 1 doesn't have `Configuration.unset`
+        hadoopConfiguration.clear()
+        clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
+      }
+    }
+  }
+
   test("Schema inference correctly identifies the datatype when data is sparse.") {
     val df = sqlContext.read
       .format("csv")

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala

@@ -23,9 +23,10 @@ import java.sql.{Date, Timestamp}
 import scala.collection.JavaConverters._
 
 import com.fasterxml.jackson.core.JsonFactory
-import org.apache.commons.io.FileUtils
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{Path, PathFilter}
+import org.apache.hadoop.io.SequenceFile.CompressionType
+import org.apache.hadoop.io.compress.GzipCodec
 import org.scalactic.Tolerance._
 
 import org.apache.spark.rdd.RDD
@@ -1524,6 +1525,49 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     }
   }
 
+  test("SPARK-13543 Write the output as uncompressed via option()") {
+    val clonedConf = new Configuration(hadoopConfiguration)
+    hadoopConfiguration.set("mapreduce.output.fileoutputformat.compress", "true")
+    hadoopConfiguration
+      .set("mapreduce.output.fileoutputformat.compress.type", CompressionType.BLOCK.toString)
+    hadoopConfiguration
+      .set("mapreduce.output.fileoutputformat.compress.codec", classOf[GzipCodec].getName)
+    hadoopConfiguration.set("mapreduce.map.output.compress", "true")
+    hadoopConfiguration.set("mapreduce.map.output.compress.codec", classOf[GzipCodec].getName)
+    withTempDir { dir =>
+      try {
+        val dir = Utils.createTempDir()
+        dir.delete()
+
+        val path = dir.getCanonicalPath
+        primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path)
+
+        val jsonDF = sqlContext.read.json(path)
+        val jsonDir = new File(dir, "json").getCanonicalPath
+        jsonDF.coalesce(1).write
+          .format("json")
+          .option("compression", "none")
+          .save(jsonDir)
+
+        val compressedFiles = new File(jsonDir).listFiles()
+        assert(compressedFiles.exists(!_.getName.endsWith(".gz")))
+
+        val jsonCopy = sqlContext.read
+          .format("json")
+          .load(jsonDir)
+
+        assert(jsonCopy.count == jsonDF.count)
+        val jsonCopySome = jsonCopy.selectExpr("string", "long", "boolean")
+        val jsonDFSome = jsonDF.selectExpr("string", "long", "boolean")
+        checkAnswer(jsonCopySome, jsonDFSome)
+      } finally {
+        // Hadoop 1 doesn't have `Configuration.unset`
+        hadoopConfiguration.clear()
+        clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
+      }
+    }
+  }
+
   test("Casting long as timestamp") {
     withTempTable("jsonTable") {
       val schema = (new StructType).add("ts", TimestampType)