org.apache.spark.sql.streaming

Class DataStreamWriter<T>

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

Modifier and Type	Method and Description
DataStreamWriter<T>	foreach(ForeachWriter<T> writer) Sets the output of the streaming query to be processed using the provided writer object.
DataStreamWriter<T>	foreachBatch(scala.Function2<Dataset<T>,Object,scala.runtime.BoxedUnit> function) :: Experimental ::
DataStreamWriter<T>	foreachBatch(VoidFunction2<Dataset<T>,Long> function) :: Experimental ::
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().
static String	SOURCE_NAME_CONSOLE()
static String	SOURCE_NAME_FOREACH_BATCH()
static String	SOURCE_NAME_FOREACH()
static String	SOURCE_NAME_MEMORY()
static String	SOURCE_NAME_NOOP()
static String	SOURCE_NAME_TABLE()
static scala.collection.Seq<String>	SOURCES_ALLOW_ONE_TIME_QUERY()
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.
StreamingQuery	toTable(String tableName) Starts the execution of the streaming query, which will continually output results to the given table 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

SOURCE_NAME_MEMORY

public static String SOURCE_NAME_MEMORY()

SOURCE_NAME_FOREACH

public static String SOURCE_NAME_FOREACH()

SOURCE_NAME_FOREACH_BATCH

public static String SOURCE_NAME_FOREACH_BATCH()

SOURCE_NAME_CONSOLE

public static String SOURCE_NAME_CONSOLE()

SOURCE_NAME_TABLE

public static String SOURCE_NAME_TABLE()

SOURCE_NAME_NOOP

public static String SOURCE_NAME_NOOP()

SOURCES_ALLOW_ONE_TIME_QUERY

public static scala.collection.Seq<String> SOURCES_ALLOW_ONE_TIME_QUERY()

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 there are 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 there are 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 time zone ID to be used to format timestamps in the JSON/CSV datasources or partition values. The following formats of timeZone are supported:
  • Region-based zone ID: It should have the form 'area/city', such as 'America/Los_Angeles'.
  • Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
  Other short names like 'CST' are not recommended to use because they can be ambiguous. If it isn't set, the current value of the SQL config spark.sql.session.timeZone is used by default.

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 time zone ID to be used to format timestamps in the JSON/CSV datasources or partition values. The following formats of timeZone are supported:
  • Region-based zone ID: It should have the form 'area/city', such as 'America/Los_Angeles'.
  • Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
  Other short names like 'CST' are not recommended to use because they can be ambiguous. If it isn't set, the current value of the SQL config spark.sql.session.timeZone is used by default.

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 time zone ID to be used to format timestamps in the JSON/CSV datasources or partition values. The following formats of timeZone are supported:
  • Region-based zone ID: It should have the form 'area/city', such as 'America/Los_Angeles'.
  • Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
  Other short names like 'CST' are not recommended to use because they can be ambiguous. If it isn't set, the current value of the SQL config spark.sql.session.timeZone is used by default.

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()
                     throws java.util.concurrent.TimeoutException

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. Throws a TimeoutException if the following conditions are met: - Another run of the same streaming query, that is a streaming query sharing the same checkpoint location, is already active on the same Spark Driver - The SQL configuration spark.sql.streaming.stopActiveRunOnRestart is enabled - The active run cannot be stopped within the timeout controlled by the SQL configuration spark.sql.streaming.stopTimeout

Returns:

(undocumented)

Throws:

java.util.concurrent.TimeoutException

Since:

2.0.0

toTable

public StreamingQuery toTable(String tableName)
                       throws java.util.concurrent.TimeoutException

Starts the execution of the streaming query, which will continually output results to the given table as new data arrives. The returned StreamingQuery object can be used to interact with the stream.

For v1 table, partitioning columns provided by partitionBy will be respected no matter the table exists or not. A new table will be created if the table not exists.

For v2 table, partitionBy will be ignored if the table already exists. partitionBy will be respected only if the v2 table does not exist. Besides, the v2 table created by this API lacks some functionalities (e.g., customized properties, options, and serde info). If you need them, please create the v2 table manually before the execution to avoid creating a table with incomplete information.

Parameters:

tableName - (undocumented)

Returns:

(undocumented)

Throws:

java.util.concurrent.TimeoutException

Since:

3.1.0

foreach

public DataStreamWriter<T> foreach(ForeachWriter<T> writer)

Sets the output of the streaming query to be processed using the provided writer object. object. See ForeachWriter for more details on the lifecycle and semantics.

Parameters:

writer - (undocumented)

Returns:

(undocumented)

Since:

2.0.0

foreachBatch

public DataStreamWriter<T> foreachBatch(scala.Function2<Dataset<T>,Object,scala.runtime.BoxedUnit> function)

:: Experimental ::

(Scala-specific) Sets the output of the streaming query to be processed using the provided function. This is supported only the in the micro-batch execution modes (that is, when the trigger is not continuous). In every micro-batch, the provided function will be called in every micro-batch with (i) the output rows as a Dataset and (ii) the batch identifier. The batchId can be used deduplicate and transactionally write the output (that is, the provided Dataset) to external systems. The output Dataset is guaranteed to exactly same for the same batchId (assuming all operations are deterministic in the query).

Parameters:

function - (undocumented)

Returns:

(undocumented)

Since:

2.4.0

foreachBatch

public DataStreamWriter<T> foreachBatch(VoidFunction2<Dataset<T>,Long> function)

:: Experimental ::

(Java-specific) Sets the output of the streaming query to be processed using the provided function. This is supported only the in the micro-batch execution modes (that is, when the trigger is not continuous). In every micro-batch, the provided function will be called in every micro-batch with (i) the output rows as a Dataset and (ii) the batch identifier. The batchId can be used deduplicate and transactionally write the output (that is, the provided Dataset) to external systems. The output Dataset is guaranteed to exactly same for the same batchId (assuming all operations are deterministic in the query).

Parameters:

function - (undocumented)

Returns:

(undocumented)

Since:

2.4.0