org.apache.spark.sql.streaming
Starts the execution of the streaming query, which will continually send results to the given
ForeachWriter as new data arrives.
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();
2.0.0
Specifies the underlying output data source.
Specifies the underlying output data source.
2.0.0
Adds an output option for the underlying data source.
Adds an output option for the underlying data source.
2.0.0
Adds an output option for the underlying data source.
Adds an output option for the underlying data source.
2.0.0
Adds an output option for the underlying data source.
Adds an output option for the underlying data source.
2.0.0
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.2.0.0
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.2.0.0
(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.2.0.0
Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.
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 sinkcomplete: all the rows in the streaming DataFrame/Dataset will be written to the sink
every time these is some updatesupdate: 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.2.0.0
Specifies how data of a streaming DataFrame/Dataset is written to a streaming sink.
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 sinkOutputMode.Complete(): all the rows in the streaming DataFrame/Dataset will be written
to the sink every time these is some updatesOutputMode.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.
2.0.0
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:
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.
2.0.0
Specifies the name of the StreamingQuery that can be started with start().
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.
2.0.0
Starts the execution of the streaming query, which will continually output results to the given path as new data arrives.
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.
2.0.0
Starts the execution of the streaming query, which will continually output results to the given path as new data arrives.
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.
2.0.0
Set the trigger for the stream query.
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))
2.0.0
Interface used to write a streaming
Datasetto external storage systems (e.g. file systems, key-value stores, etc). UseDataset.writeStreamto access this.2.0.0