fix.

R/pkg/R/SQLContext.R

@@ -334,7 +334,7 @@ setMethod("toDF", signature(x = "RDD"),
 #'
 #' Loads a JSON file, returning the result as a SparkDataFrame
 #' By default, (\href{http://jsonlines.org/}{JSON Lines text format or newline-delimited JSON}
-#' ) is supported. For JSON (one record per file), set a named property \code{wholeFile} to
+#' ) is supported. For JSON (one record per file), set a named property \code{multiLine} to
 #' \code{TRUE}.
 #' It goes through the entire dataset once to determine the schema.
 #'
@@ -348,7 +348,7 @@ setMethod("toDF", signature(x = "RDD"),
 #' sparkR.session()
 #' path <- "path/to/file.json"
 #' df <- read.json(path)
-#' df <- read.json(path, wholeFile = TRUE)
+#' df <- read.json(path, multiLine = TRUE)
 #' df <- jsonFile(path)
 #' }
 #' @name read.json
@@ -598,7 +598,7 @@ tableToDF <- function(tableName) {
 #' df1 <- read.df("path/to/file.json", source = "json")
 #' schema <- structType(structField("name", "string"),
 #'                      structField("info", "map<string,double>"))
-#' df2 <- read.df(mapTypeJsonPath, "json", schema, wholeFile = TRUE)
+#' df2 <- read.df(mapTypeJsonPath, "json", schema, multiLine = TRUE)
 #' df3 <- loadDF("data/test_table", "parquet", mergeSchema = "true")
 #' }
 #' @name read.df

python/pyspark/sql/readwriter.py

@@ -174,12 +174,12 @@ def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
              allowComments=None, allowUnquotedFieldNames=None, allowSingleQuotes=None,
              allowNumericLeadingZero=None, allowBackslashEscapingAnyCharacter=None,
              mode=None, columnNameOfCorruptRecord=None, dateFormat=None, timestampFormat=None,
-             wholeFile=None):
+             multiLine=None):
         """
         Loads JSON files and returns the results as a :class:`DataFrame`.
 
         `JSON Lines <http://jsonlines.org/>`_ (newline-delimited JSON) is supported by default.
-        For JSON (one record per file), set the ``wholeFile`` parameter to ``true``.
+        For JSON (one record per file), set the ``multiLine`` parameter to ``true``.
 
         If the ``schema`` parameter is not specified, this function goes
         through the input once to determine the input schema.
@@ -230,7 +230,7 @@ def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
                                 formats follow the formats at ``java.text.SimpleDateFormat``.
                                 This applies to timestamp type. If None is set, it uses the
                                 default value, ``yyyy-MM-dd'T'HH:mm:ss.SSSXXX``.
-        :param wholeFile: parse one record, which may span multiple lines, per file. If None is
+        :param multiLine: parse one record, which may span multiple lines, per file. If None is
                           set, it uses the default value, ``false``.
 
         >>> df1 = spark.read.json('python/test_support/sql/people.json')
@@ -248,7 +248,7 @@ def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
             allowSingleQuotes=allowSingleQuotes, allowNumericLeadingZero=allowNumericLeadingZero,
             allowBackslashEscapingAnyCharacter=allowBackslashEscapingAnyCharacter,
             mode=mode, columnNameOfCorruptRecord=columnNameOfCorruptRecord, dateFormat=dateFormat,
-            timestampFormat=timestampFormat, wholeFile=wholeFile)
+            timestampFormat=timestampFormat, multiLine=multiLine)
         if isinstance(path, basestring):
             path = [path]
         if type(path) == list:

python/pyspark/sql/streaming.py

@@ -401,12 +401,12 @@ def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
              allowComments=None, allowUnquotedFieldNames=None, allowSingleQuotes=None,
              allowNumericLeadingZero=None, allowBackslashEscapingAnyCharacter=None,
              mode=None, columnNameOfCorruptRecord=None, dateFormat=None, timestampFormat=None,
-             wholeFile=None):
+             multiLine=None):
         """
         Loads a JSON file stream and returns the results as a :class:`DataFrame`.
 
         `JSON Lines <http://jsonlines.org/>`_ (newline-delimited JSON) is supported by default.
-        For JSON (one record per file), set the ``wholeFile`` parameter to ``true``.
+        For JSON (one record per file), set the ``multiLine`` parameter to ``true``.
 
         If the ``schema`` parameter is not specified, this function goes
         through the input once to determine the input schema.
@@ -458,7 +458,7 @@ def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
                                 formats follow the formats at ``java.text.SimpleDateFormat``.
                                 This applies to timestamp type. If None is set, it uses the
                                 default value, ``yyyy-MM-dd'T'HH:mm:ss.SSSXXX``.
-        :param wholeFile: parse one record, which may span multiple lines, per file. If None is
+        :param multiLine: parse one record, which may span multiple lines, per file. If None is
                           set, it uses the default value, ``false``.
 
         >>> json_sdf = spark.readStream.json(tempfile.mkdtemp(), schema = sdf_schema)
@@ -473,7 +473,7 @@ def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
             allowSingleQuotes=allowSingleQuotes, allowNumericLeadingZero=allowNumericLeadingZero,
             allowBackslashEscapingAnyCharacter=allowBackslashEscapingAnyCharacter,
             mode=mode, columnNameOfCorruptRecord=columnNameOfCorruptRecord, dateFormat=dateFormat,
-            timestampFormat=timestampFormat, wholeFile=wholeFile)
+            timestampFormat=timestampFormat, multiLine=multiLine)
         if isinstance(path, basestring):
             return self._df(self._jreader.json(path))
         else:

python/pyspark/sql/tests.py

@@ -457,10 +457,10 @@ def test_udf_registration_returns_udf(self):
             df.select(add_three("id").alias("plus_three")).collect()
         )
 
-    def test_wholefile_json(self):
+    def test_multiLine_json(self):
         people1 = self.spark.read.json("python/test_support/sql/people.json")
         people_array = self.spark.read.json("python/test_support/sql/people_array.json",
-                                            wholeFile=True)
+                                            multiLine=True)
         self.assertEqual(people1.collect(), people_array.collect())
 
     def test_multiline_csv(self):

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JSONOptions.scala

@@ -81,7 +81,7 @@ private[sql] class JSONOptions(
     FastDateFormat.getInstance(
       parameters.getOrElse("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss.SSSXXX"), timeZone, Locale.US)
 
-  val wholeFile = parameters.get("wholeFile").map(_.toBoolean).getOrElse(false)
+  val multiLine = parameters.get("multiLine").map(_.toBoolean).getOrElse(false)
 
   /** Sets config options on a Jackson [[JsonFactory]]. */
   def setJacksonOptions(factory: JsonFactory): Unit = {

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

@@ -295,7 +295,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
    * Loads JSON files and returns the results as a `DataFrame`.
    *
    * <a href="http://jsonlines.org/">JSON Lines</a> (newline-delimited JSON) is supported by
-   * default. For JSON (one record per file), set the `wholeFile` option to true.
+   * default. For JSON (one record per file), set the `multiLine` option to true.
    *
    * This function goes through the input once to determine the input schema. If you know the
    * schema in advance, use the version that specifies the schema to avoid the extra scan.
@@ -335,7 +335,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
    * <li>`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss.SSSXXX`): sets the string that
    * indicates a timestamp format. Custom date formats follow the formats at
    * `java.text.SimpleDateFormat`. This applies to timestamp type.</li>
-   * <li>`wholeFile` (default `false`): parse one record, which may span multiple lines,
+   * <li>`multiLine` (default `false`): parse one record, which may span multiple lines,
    * per file</li>
    * </ul>
    *

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

@@ -86,7 +86,7 @@ abstract class JsonDataSource extends Serializable {
 
 object JsonDataSource {
   def apply(options: JSONOptions): JsonDataSource = {
-    if (options.wholeFile) {
+    if (options.multiLine) {
       WholeFileJsonDataSource
     } else {
       TextInputJsonDataSource

sql/core/src/main/scala/org/apache/spark/sql/streaming/DataStreamReader.scala

@@ -163,7 +163,7 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo
    * Loads a JSON file stream and returns the results as a `DataFrame`.
    *
    * <a href="http://jsonlines.org/">JSON Lines</a> (newline-delimited JSON) is supported by
-   * default. For JSON (one record per file), set the `wholeFile` option to true.
+   * default. For JSON (one record per file), set the `multiLine` option to true.
    *
    * This function goes through the input once to determine the input schema. If you know the
    * schema in advance, use the version that specifies the schema to avoid the extra scan.
@@ -205,7 +205,7 @@ final class DataStreamReader private[sql](sparkSession: SparkSession) extends Lo
    * <li>`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss.SSSXXX`): sets the string that
    * indicates a timestamp format. Custom date formats follow the formats at
    * `java.text.SimpleDateFormat`. This applies to timestamp type.</li>
-   * <li>`wholeFile` (default `false`): parse one record, which may span multiple lines,
+   * <li>`multiLine` (default `false`): parse one record, which may span multiple lines,
    * per file</li>
    * </ul>
    *

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

@@ -1814,7 +1814,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
 
       assert(new File(path).listFiles().exists(_.getName.endsWith(".gz")))
 
-      val jsonDF = spark.read.option("wholeFile", true).json(path)
+      val jsonDF = spark.read.option("multiLine", true).json(path)
       val jsonDir = new File(dir, "json").getCanonicalPath
       jsonDF.coalesce(1).write
         .option("compression", "gZiP")
@@ -1836,7 +1836,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
         .write
         .text(path)
 
-      val jsonDF = spark.read.option("wholeFile", true).json(path)
+      val jsonDF = spark.read.option("multiLine", true).json(path)
       val jsonDir = new File(dir, "json").getCanonicalPath
       jsonDF.coalesce(1).write.json(jsonDir)
 
@@ -1865,7 +1865,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
         .write
         .text(path)
 
-      val jsonDF = spark.read.option("wholeFile", true).json(path)
+      val jsonDF = spark.read.option("multiLine", true).json(path)
       // no corrupt record column should be created
       assert(jsonDF.schema === StructType(Seq()))
       // only the first object should be read
@@ -1886,7 +1886,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
         .write
         .text(path)
 
-      val jsonDF = spark.read.option("wholeFile", true).option("mode", "PERMISSIVE").json(path)
+      val jsonDF = spark.read.option("multiLine", true).option("mode", "PERMISSIVE").json(path)
       assert(jsonDF.count() === corruptRecordCount)
       assert(jsonDF.schema === new StructType()
         .add("_corrupt_record", StringType)
@@ -1917,7 +1917,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
         .write
         .text(path)
 
-      val jsonDF = spark.read.option("wholeFile", true).option("mode", "DROPMALFORMED").json(path)
+      val jsonDF = spark.read.option("multiLine", true).option("mode", "DROPMALFORMED").json(path)
       checkAnswer(jsonDF, Seq(Row("test")))
     }
   }
@@ -1940,15 +1940,15 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       // `FAILFAST` mode should throw an exception for corrupt records.
       val exceptionOne = intercept[SparkException] {
         spark.read
-          .option("wholeFile", true)
+          .option("multiLine", true)
           .option("mode", "FAILFAST")
           .json(path)
       }
       assert(exceptionOne.getMessage.contains("Failed to infer a common schema"))
 
       val exceptionTwo = intercept[SparkException] {
         spark.read
-          .option("wholeFile", true)
+          .option("multiLine", true)
           .option("mode", "FAILFAST")
           .schema(schema)
           .json(path)