DataStreamWriter (Spark 2.2.0 JavaDoc)

Object
- org.apache.spark.sql.streaming.DataStreamWriter<T>

```
public final class DataStreamWriter<T>
extends Object
```
Interface used to write a streaming Dataset to external storage systems (e.g. file systems, key-value stores, etc). Use Dataset.writeStream to access this.

Since:

2.0.0

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`DataStreamWriter<T>`	`foreach(ForeachWriter<T> writer)` Starts the execution of the streaming query, which will continually send results to the given `ForeachWriter` as new data arrives.
`DataStreamWriter<T>`	`format(String source)` Specifies the underlying output data source.
`DataStreamWriter<T>`	`option(String key, boolean value)` Adds an output option for the underlying data source.
`DataStreamWriter<T>`	`option(String key, double value)` Adds an output option for the underlying data source.
`DataStreamWriter<T>`	`option(String key, long value)` Adds an output option for the underlying data source.
`DataStreamWriter<T>`	`option(String key, String value)` Adds an output option for the underlying data source.
`DataStreamWriter<T>`	`options(scala.collection.Map<String,String> options)` (Scala-specific) Adds output options for the underlying data source.
`DataStreamWriter<T>`	`options(java.util.Map<String,String> options)` Adds output options for the underlying data source.
`DataStreamWriter<T>`	`outputMode(OutputMode outputMode)` Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.
`DataStreamWriter<T>`	`outputMode(String outputMode)` Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.
`DataStreamWriter<T>`	`partitionBy(scala.collection.Seq<String> colNames)` Partitions the output by the given columns on the file system.
`DataStreamWriter<T>`	`partitionBy(String... colNames)` Partitions the output by the given columns on the file system.
`DataStreamWriter<T>`	`queryName(String queryName)` Specifies the name of the `StreamingQuery` that can be started with `start()`.
`StreamingQuery`	`start()` Starts the execution of the streaming query, which will continually output results to the given path as new data arrives.
`StreamingQuery`	`start(String path)` Starts the execution of the streaming query, which will continually output results to the given path as new data arrives.
`DataStreamWriter<T>`	`trigger(Trigger trigger)` Set the trigger for the stream query.

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - partitionBy
```
public DataStreamWriter<T> partitionBy(String... colNames)
```
    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.
    
    Parameters:
    
    colNames - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - outputMode
```
public DataStreamWriter<T> outputMode(OutputMode outputMode)
```
    Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. - OutputMode.Append(): only the new rows in the streaming DataFrame/Dataset will be written to the sink - OutputMode.Complete(): all the rows in the streaming DataFrame/Dataset will be written to the sink every time these is some updates - OutputMode.Update(): only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. If the query doesn't contain aggregations, it will be equivalent to OutputMode.Append() mode.
    
    Parameters:
    
    outputMode - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - outputMode
```
public DataStreamWriter<T> outputMode(String outputMode)
```
    Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink. - append: only the new rows in the streaming DataFrame/Dataset will be written to the sink - complete: all the rows in the streaming DataFrame/Dataset will be written to the sink every time these is some updates - update: only the rows that were updated in the streaming DataFrame/Dataset will be written to the sink every time there are some updates. If the query doesn't contain aggregations, it will be equivalent to append mode.
    
    Parameters:
    
    outputMode - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - trigger
```
public DataStreamWriter<T> trigger(Trigger trigger)
```
    Set the trigger for the stream query. The default value is ProcessingTime(0) and it will run the query as fast as possible.
    Scala Example:
```
   df.writeStream.trigger(ProcessingTime("10 seconds"))

   import scala.concurrent.duration._
   df.writeStream.trigger(ProcessingTime(10.seconds))
 
```
    Java Example:
```
   df.writeStream().trigger(ProcessingTime.create("10 seconds"))

   import java.util.concurrent.TimeUnit
   df.writeStream().trigger(ProcessingTime.create(10, TimeUnit.SECONDS))
 
```
    Parameters:
    
    trigger - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - queryName
```
public DataStreamWriter<T> queryName(String queryName)
```
    Specifies the name of the StreamingQuery that can be started with start(). This name must be unique among all the currently active queries in the associated SQLContext.
    
    Parameters:
    
    queryName - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - format
```
public DataStreamWriter<T> format(String source)
```
    Specifies the underlying output data source.
    
    Parameters:
    
    source - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - partitionBy
```
public DataStreamWriter<T> partitionBy(scala.collection.Seq<String> colNames)
```
    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.
    
    Parameters:
    
    colNames - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - option
```
public DataStreamWriter<T> option(String key,
                                  String value)
```
    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.
    Parameters:
    
    key - (undocumented)
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - option
```
public DataStreamWriter<T> option(String key,
                                  boolean value)
```
    Adds an output option for the underlying data source.
    
    Parameters:
    
    key - (undocumented)
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - option
```
public DataStreamWriter<T> option(String key,
                                  long value)
```
    Adds an output option for the underlying data source.
    
    Parameters:
    
    key - (undocumented)
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - option
```
public DataStreamWriter<T> option(String key,
                                  double value)
```
    Adds an output option for the underlying data source.
    
    Parameters:
    
    key - (undocumented)
    
    value - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - options
```
public DataStreamWriter<T> options(scala.collection.Map<String,String> options)
```
    (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.
    Parameters:
    
    options - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - options
```
public DataStreamWriter<T> options(java.util.Map<String,String> options)
```
    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.
    Parameters:
    
    options - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - start
```
public StreamingQuery start(String path)
```
    Starts the execution of the streaming query, which will continually output results to the given path as new data arrives. The returned StreamingQuery object can be used to interact with the stream.
    
    Parameters:
    
    path - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - start
```
public StreamingQuery start()
```
    Starts the execution of the streaming query, which will continually output results to the given path as new data arrives. The returned StreamingQuery object can be used to interact with the stream.
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0
  - foreach
```
public DataStreamWriter<T> foreach(ForeachWriter<T> writer)
```
    Starts the execution of the streaming query, which will continually send results to the given ForeachWriter as new data arrives. The ForeachWriter can be used to send the data generated by the DataFrame/Dataset to an external system.
    Scala example:
```
   datasetOfString.writeStream.foreach(new ForeachWriter[String] {

     def open(partitionId: Long, version: Long): Boolean = {
       // open connection
     }

     def process(record: String) = {
       // write string to connection
     }

     def close(errorOrNull: Throwable): Unit = {
       // close the connection
     }
   }).start()
 
```
    Java example:
```
  datasetOfString.writeStream().foreach(new ForeachWriter<String>() {

    @Override
    public boolean open(long partitionId, long version) {
      // open connection
    }

    @Override
    public void process(String value) {
      // write string to connection
    }

    @Override
    public void close(Throwable errorOrNull) {
      // close the connection
    }
  }).start();
 
```
    Parameters:
    
    writer - (undocumented)
    
    Returns:
    
    (undocumented)
    
    Since:
    
    2.0.0

Class DataStreamWriter<T>

Method Summary

Methods inherited from class Object

Method Detail

partitionBy

outputMode

outputMode

trigger

queryName

format

partitionBy

option

option

option

option

options

options

start

start

foreach