public final class DataStreamWriter<T>
extends Object
Dataset
to external storage systems (e.g. file systems,
key-value stores, etc). Use Dataset.writeStream
to access this.
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.
|
public static String SOURCE_NAME_MEMORY()
public static String SOURCE_NAME_FOREACH()
public static String SOURCE_NAME_FOREACH_BATCH()
public static String SOURCE_NAME_CONSOLE()
public static String SOURCE_NAME_TABLE()
public static String SOURCE_NAME_NOOP()
public static scala.collection.Seq<String> SOURCES_ALLOW_ONE_TIME_QUERY()
public DataStreamWriter<T> partitionBy(String... colNames)
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.
colNames
- (undocumented)public DataStreamWriter<T> outputMode(OutputMode outputMode)
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.outputMode
- (undocumented)public DataStreamWriter<T> outputMode(String outputMode)
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.outputMode
- (undocumented)public DataStreamWriter<T> trigger(Trigger trigger)
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))
trigger
- (undocumented)public DataStreamWriter<T> queryName(String queryName)
StreamingQuery
that can be started with start()
.
This name must be unique among all the currently active queries in the associated SQLContext.
queryName
- (undocumented)public DataStreamWriter<T> format(String source)
source
- (undocumented)public DataStreamWriter<T> partitionBy(scala.collection.Seq<String> colNames)
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.
colNames
- (undocumented)public DataStreamWriter<T> option(String key, String value)
key
- (undocumented)value
- (undocumented)public DataStreamWriter<T> option(String key, boolean value)
key
- (undocumented)value
- (undocumented)public DataStreamWriter<T> option(String key, long value)
key
- (undocumented)value
- (undocumented)public DataStreamWriter<T> option(String key, double value)
key
- (undocumented)value
- (undocumented)public DataStreamWriter<T> options(scala.collection.Map<String,String> options)
options
- (undocumented)public DataStreamWriter<T> options(java.util.Map<String,String> options)
options
- (undocumented)public StreamingQuery start(String path)
StreamingQuery
object can be used to interact with
the stream.
path
- (undocumented)public StreamingQuery start() throws java.util.concurrent.TimeoutException
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
java.util.concurrent.TimeoutException
public StreamingQuery toTable(String tableName) throws java.util.concurrent.TimeoutException
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.
tableName
- (undocumented)java.util.concurrent.TimeoutException
public DataStreamWriter<T> foreach(ForeachWriter<T> writer)
ForeachWriter
for more details on the lifecycle and
semantics.writer
- (undocumented)public DataStreamWriter<T> foreachBatch(scala.Function2<Dataset<T>,Object,scala.runtime.BoxedUnit> function)
(Scala-specific) Sets the output of the streaming query to be processed using the provided function. This is supported only 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 to deduplicate and transactionally write the output (that is, the provided Dataset) to external systems. The output Dataset is guaranteed to be exactly the same for the same batchId (assuming all operations are deterministic in the query).
function
- (undocumented)public DataStreamWriter<T> foreachBatch(VoidFunction2<Dataset<T>,Long> function)
(Java-specific) Sets the output of the streaming query to be processed using the provided function. This is supported only 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 to deduplicate and transactionally write the output (that is, the provided Dataset) to external systems. The output Dataset is guaranteed to be exactly the same for the same batchId (assuming all operations are deterministic in the query).
function
- (undocumented)