DataFrameWriter

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
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final def ##(): Int

Definition Classes
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final def ==(arg0: Any): Boolean

Definition Classes
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final def asInstanceOf[T0]: T0

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

Annotations
@varargs()
Since
2.0
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( ... )
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 set the following CSV-specific option(s) for writing CSV files:
- sep (default ,): sets a single character as a separator for each field and value.
- quote (default "): sets a single character used for escaping quoted values where the separator can be part of the value. If an empty string is set, it uses u0000 (null character).
- escape (default \): sets a single character used for escaping quotes inside an already quoted value.
- charToEscapeQuoteEscaping (default escape or \0): sets a single character used for escaping the escape for the quote character. The default value is escape character when escape and quote characters are different, \0 otherwise.
- escapeQuotes (default true): a flag indicating whether values containing quotes should always be enclosed in quotes. Default is to escape all values containing a quote character.
- quoteAll (default false): a flag indicating whether all values should always be enclosed in quotes. Default is to only escape values containing a quote character.
- header (default false): writes the names of columns as the first line.
- nullValue (default empty string): sets the string representation of a null value.
- emptyValue (default ""): sets the string representation of an empty value.
- encoding (by default it is not set): specifies encoding (charset) of saved csv files. If it is not set, the UTF-8 charset will be used.
- compression (default null): 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).
- dateFormat (default yyyy-MM-dd): sets the string that indicates a date format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to date type.
- 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.
- ignoreLeadingWhiteSpace (default true): a flag indicating whether or not leading whitespaces from values being written should be skipped.
- ignoreTrailingWhiteSpace (default true): a flag indicating defines whether or not trailing whitespaces from values being written should be skipped.
Since
2.0.0
final def eq(arg0: AnyRef): Boolean

Definition Classes
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def equals(arg0: Any): Boolean

Definition Classes
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def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( classOf[java.lang.Throwable] )
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
final def getClass(): Class[_]

Definition Classes
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def hashCode(): Int

Definition Classes
AnyRef → Any
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:
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.
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
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.
You can set the following JDBC-specific option(s) for storing JDBC:
- truncate (default false): use TRUNCATE TABLE instead of DROP TABLE.
In case of failures, users should turn off truncate option to use DROP TABLE again. Also, due to the different behavior of TRUNCATE TABLE among DBMS, it's not always safe to use this. MySQLDialect, DB2Dialect, MsSqlServerDialect, DerbyDialect, and OracleDialect supports this while PostgresDialect and default JDBCDirect doesn't. For unknown and unsupported JDBCDirect, the user option truncate is ignored.
url
JDBC database url of the form jdbc:subprotocol:subname
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
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 set the following JSON-specific option(s) for writing JSON files:
- compression (default null): 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).
- dateFormat (default yyyy-MM-dd): sets the string that indicates a date format. Custom date formats follow the formats at java.text.SimpleDateFormat. This applies to date type.
- 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.
- encoding (by default it is not set): specifies encoding (charset) of saved json files. If it is not set, the UTF-8 charset will be used.
- lineSep (default \n): defines the line separator that should be used for writing.
Since
1.4.0
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
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: default option, throw an exception at runtime.
Since
1.4.0
final def ne(arg0: AnyRef): Boolean

Definition Classes
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final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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.

Since
2.0.0
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.

Since
2.0.0
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.

Since
2.0.0
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.
You can set the following option(s):
- timeZone (default session local timezone): sets the string that indicates a timezone to be used to format timestamps in the JSON/CSV datasources or partition values.
Since
1.4.0
def options(options: Map[String, String]): DataFrameWriter[T]

Adds output options for the underlying data source.
Adds output options for the underlying data source.
You can set the following option(s):
- timeZone (default session local timezone): sets the string that indicates a timezone to be used to format timestamps in the JSON/CSV datasources or partition values.
Since
1.4.0
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.
You can set the following option(s):
- timeZone (default session local timezone): sets the string that indicates a timezone to be used to format timestamps in the JSON/CSV datasources or partition values.
Since
1.4.0
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)
```
You can set the following ORC-specific option(s) for writing ORC files:
- compression (default is the value specified in spark.sql.orc.compression.codec): 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 override orc.compress and spark.sql.orc.compression.codec. If orc.compress is given, it overrides spark.sql.orc.compression.codec.
Since
1.5.0
Note
Currently, this method can only be used after enabling Hive support
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)
```
You can set the following Parquet-specific option(s) for writing Parquet files:
- compression (default is the value specified in spark.sql.parquet.compression.codec): compression codec to use when saving to file. This can be one of the known case-insensitive shorten names(none, uncompressed, snappy, gzip, lzo, brotli, lz4, and zstd). This will override spark.sql.parquet.compression.codec.
Since
1.4.0
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
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
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
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
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
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
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")
```
You can set the following option(s) for writing text files:
- compression (default null): 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).
- lineSep (default \n): defines the line separator that should be used for writing.
Since
1.6.0
def toString(): String

Definition Classes
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final def wait(): Unit

Definition Classes
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@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
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@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
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Annotations
@throws( ... )

Related Doc: package sql

final class DataFrameWriter[T] extends AnyRef

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

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

def clone(): AnyRef

def csv(path: String): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def format(source: String): DataFrameWriter[T]

final def getClass(): Class[_]

def hashCode(): Int

def insertInto(tableName: String): Unit

final def isInstanceOf[T0]: Boolean

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

def json(path: String): Unit

def mode(saveMode: String): DataFrameWriter[T]

def mode(saveMode: SaveMode): DataFrameWriter[T]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

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

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

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

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

def orc(path: String): Unit

def parquet(path: String): Unit

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

def save(): Unit

def save(path: String): Unit

def saveAsTable(tableName: String): Unit

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

final def synchronized[T0](arg0: ⇒ T0): T0

def text(path: String): Unit

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

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