Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

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2.  final def ##(): Int

   Definition Classes

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3.  final def ==(arg0: Any): Boolean

   Definition Classes

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4.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

5.  def bucketBy(numBuckets: Int, colName: String, colNames: String*): DataFrameWriter[T]

   Buckets the output by the given columns.

   Buckets the output by the given columns. If specified, the output is laid out on the file system similar to Hive's bucketing scheme, but with a different bucket hash function and is not compatible with Hive's bucketing.

   This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark 2.1.0.

   Annotations

   @varargs()

   Since

   2.0

6.  def clone(): AnyRef

   Attributes

   protected[lang]

   Definition Classes

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   Annotations

   @throws( ... ) @native()

7.  def csv(path: String): Unit

   Saves the content of the DataFrame in CSV format at the specified path.

   Saves the content of the DataFrame in CSV format at the specified path. This is equivalent to:

   format("csv").save(path)

   You can find the CSV-specific options for writing CSV files in Data Source Option in the version you use.

   Since

   2.0.0

8.  final def eq(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

9.  def equals(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

10.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

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    Annotations

    @throws( classOf[java.lang.Throwable] )

11.  def format(source: String): DataFrameWriter[T]

    Specifies the underlying output data source.

    Specifies the underlying output data source. Built-in options include "parquet", "json", etc.

    Since

    1.4.0

12.  final def getClass(): Class[_]

    Definition Classes

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    Annotations

    @native()

13.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

14.  def insertInto(tableName: String): Unit

    Inserts the content of the DataFrame to the specified table.

    Inserts the content of the DataFrame to the specified table. It requires that the schema of the DataFrame is the same as the schema of the table.

    Since

    1.4.0

    Note

    Unlike saveAsTable, insertInto ignores the column names and just uses position-based resolution. For example:

    ,

    SaveMode.ErrorIfExists and SaveMode.Ignore behave as SaveMode.Append in insertInto as insertInto is not a table creating operation.

    scala> Seq((1, 2)).toDF("i", "j").write.mode("overwrite").saveAsTable("t1")
    scala> Seq((3, 4)).toDF("j", "i").write.insertInto("t1")
    scala> Seq((5, 6)).toDF("a", "b").write.insertInto("t1")
    scala> sql("select * from t1").show
    +---+---+
    |  i|  j|
    +---+---+
    |  5|  6|
    |  3|  4|
    |  1|  2|
    +---+---+

    Because it inserts data to an existing table, format or options will be ignored.

15.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

16.  def jdbc(url: String, table: String, connectionProperties: Properties): Unit

    Saves the content of the DataFrame to an external database table via JDBC.

    Saves the content of the DataFrame to an external database table via JDBC. In the case the table already exists in the external database, behavior of this function depends on the save mode, specified by the mode function (default to throwing an exception).

    Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.

    JDBC-specific option and parameter documentation for storing tables via JDBC in Data Source Option in the version you use.

    table

    Name of the table in the external database.

    connectionProperties

    JDBC database connection arguments, a list of arbitrary string tag/value. Normally at least a "user" and "password" property should be included. "batchsize" can be used to control the number of rows per insert. "isolationLevel" can be one of "NONE", "READ_COMMITTED", "READ_UNCOMMITTED", "REPEATABLE_READ", or "SERIALIZABLE", corresponding to standard transaction isolation levels defined by JDBC's Connection object, with default of "READ_UNCOMMITTED".

    Since

    1.4.0

17.  def json(path: String): Unit

    Saves the content of the DataFrame in JSON format ( JSON Lines text format or newline-delimited JSON) at the specified path.

    Saves the content of the DataFrame in JSON format ( JSON Lines text format or newline-delimited JSON) at the specified path. This is equivalent to:

    format("json").save(path)

    You can find the JSON-specific options for writing JSON files in Data Source Option in the version you use.

    Since

    1.4.0

18.  def mode(saveMode: String): DataFrameWriter[T]

    Specifies the behavior when data or table already exists.

    Specifies the behavior when data or table already exists. Options include:

    • overwrite: overwrite the existing data.
    • append: append the data.
    • ignore: ignore the operation (i.e. no-op).
    • error or errorifexists: default option, throw an exception at runtime.

    Since

    1.4.0

19.  def mode(saveMode: SaveMode): DataFrameWriter[T]

    Specifies the behavior when data or table already exists.

    Specifies the behavior when data or table already exists. Options include:

    • SaveMode.Overwrite: overwrite the existing data.
    • SaveMode.Append: append the data.
    • SaveMode.Ignore: ignore the operation (i.e. no-op).
    • SaveMode.ErrorIfExists: throw an exception at runtime.

    The default option is ErrorIfExists.

    Since

    1.4.0

20.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

21.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

22.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

23.  def option(key: String, value: Double): DataFrameWriter[T]

    Adds an output option for the underlying data source.

    Adds an output option for the underlying data source.

    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.

    Since

    2.0.0

24.  def option(key: String, value: Long): DataFrameWriter[T]

    Adds an output option for the underlying data source.

    Adds an output option for the underlying data source.

    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.

    Since

    2.0.0

25.  def option(key: String, value: Boolean): DataFrameWriter[T]

    Adds an output option for the underlying data source.

    Adds an output option for the underlying data source.

    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.

    Since

    2.0.0

26.  def option(key: String, value: String): DataFrameWriter[T]

    Adds an output option for the underlying data source.

    Adds an output option for the underlying data source.

    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.

    Since

    1.4.0

27.  def options(options: Map[String, String]): DataFrameWriter[T]

    Adds output options for the underlying data source.

    Adds output options for the underlying data source.

    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.

    Since

    1.4.0

28.  def options(options: Map[String, String]): DataFrameWriter[T]

    (Scala-specific) Adds output options for the underlying data source.

    (Scala-specific) Adds output options for the underlying data source.

    All options are maintained in a case-insensitive way in terms of key names. If a new option has the same key case-insensitively, it will override the existing option.

    Since

    1.4.0

29.  def orc(path: String): Unit

    Saves the content of the DataFrame in ORC format at the specified path.

    Saves the content of the DataFrame in ORC format at the specified path. This is equivalent to:

    format("orc").save(path)

    ORC-specific option(s) for writing ORC files can be found in Data Source Option in the version you use.

    Since

    1.5.0

30.  def parquet(path: String): Unit

    Saves the content of the DataFrame in Parquet format at the specified path.

    Saves the content of the DataFrame in Parquet format at the specified path. This is equivalent to:

    format("parquet").save(path)

    Parquet-specific option(s) for writing Parquet files can be found in Data Source Option in the version you use.

    Since

    1.4.0

31.  def partitionBy(colNames: String*): DataFrameWriter[T]

    Partitions the output by the given columns on the file system.

    Partitions the output by the given columns on the file system. If specified, the output is laid out on the file system similar to Hive's partitioning scheme. As an example, when we partition a dataset by year and then month, the directory layout would look like:

    • year=2016/month=01/
    • year=2016/month=02/

    Partitioning is one of the most widely used techniques to optimize physical data layout. It provides a coarse-grained index for skipping unnecessary data reads when queries have predicates on the partitioned columns. In order for partitioning to work well, the number of distinct values in each column should typically be less than tens of thousands.

    This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark 2.1.0.

    Annotations

    @varargs()

    Since

    1.4.0

32.  def save(): Unit

    Saves the content of the DataFrame as the specified table.

    Saves the content of the DataFrame as the specified table.

    Since

    1.4.0

33.  def save(path: String): Unit

    Saves the content of the DataFrame at the specified path.

    Saves the content of the DataFrame at the specified path.

    Since

    1.4.0

34.  def saveAsTable(tableName: String): Unit

    Saves the content of the DataFrame as the specified table.

    Saves the content of the DataFrame as the specified table.

    In the case the table already exists, behavior of this function depends on the save mode, specified by the mode function (default to throwing an exception). When mode is Overwrite, the schema of the DataFrame does not need to be the same as that of the existing table.

    When mode is Append, if there is an existing table, we will use the format and options of the existing table. The column order in the schema of the DataFrame doesn't need to be same as that of the existing table. Unlike insertInto, saveAsTable will use the column names to find the correct column positions. For example:

    scala> Seq((1, 2)).toDF("i", "j").write.mode("overwrite").saveAsTable("t1")
    scala> Seq((3, 4)).toDF("j", "i").write.mode("append").saveAsTable("t1")
    scala> sql("select * from t1").show
    +---+---+
    |  i|  j|
    +---+---+
    |  1|  2|
    |  4|  3|
    +---+---+

    In this method, save mode is used to determine the behavior if the data source table exists in Spark catalog. We will always overwrite the underlying data of data source (e.g. a table in JDBC data source) if the table doesn't exist in Spark catalog, and will always append to the underlying data of data source if the table already exists.

    When the DataFrame is created from a non-partitioned HadoopFsRelation with a single input path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC and Parquet), the table is persisted in a Hive compatible format, which means other systems like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL specific format.

    Since

    1.4.0

35.  def sortBy(colName: String, colNames: String*): DataFrameWriter[T]

    Sorts the output in each bucket by the given columns.

    Sorts the output in each bucket by the given columns.

    This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark 2.1.0.

    Annotations

    @varargs()

    Since

    2.0

36.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

37.  def text(path: String): Unit

    Saves the content of the DataFrame in a text file at the specified path.

    Saves the content of the DataFrame in a text file at the specified path. The DataFrame must have only one column that is of string type. Each row becomes a new line in the output file. For example:

    // Scala:
    df.write.text("/path/to/output")
    
    // Java:
    df.write().text("/path/to/output")

    The text files will be encoded as UTF-8.

    You can find the text-specific options for writing text files in Data Source Option in the version you use.

    Since

    1.6.0

38.  def toString(): String

    Definition Classes

    AnyRef → Any

39.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

40.  final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

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    Annotations

    @throws( ... )

41.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()