The base logical plan which will be used across batch runs. Once the value is set, it should not be modified.

Run the activated stream until stopped.

The list of stream instances which will be used across batch runs. Once the value is set, it should not be modified.

Stops the execution of this query if it is running. This waits until the termination of the query execution threads or until a timeout is hit.

By default stop will block indefinitely. You can configure a timeout by the configuration spark.sql.streaming.stopTimeout. A timeout of 0 (or negative) milliseconds will block indefinitely. If a TimeoutException is thrown, users can retry stopping the stream. If the issue persists, it is advisable to kill the Spark application.

A lock used to wait/notify when batches complete. Use a fair lock to avoid thread starvation.

Waits for the termination of this query, either by query.stop() or by an exception. If the query has terminated with an exception, then the exception will be thrown. Otherwise, it returns whether the query has terminated or not within the timeoutMs milliseconds.

If the query has terminated, then all subsequent calls to this method will either return true immediately (if the query was terminated by stop()), or throw the exception immediately (if the query has terminated with exception).

Waits for the termination of this query, either by query.stop() or by an exception. If the query has terminated with an exception, then the exception will be thrown.

If the query has terminated, then all subsequent calls to this method will either return immediately (if the query was terminated by stop()), or throw the exception immediately (if the query has terminated with exception).

Returns the path of a file with name in the checkpoint directory.

Any clean up that needs to happen when the query is stopped or exits

A log that records the batch ids that have completed. This is used to check if a batch was fully processed, and its output was committed to the sink, hence no need to process it again. This is used (for instance) during restart, to help identify which batch to run next.

Tracks how much data we have processed and committed to the sink or state store from each input source. Only the scheduler thread should modify this field, and only in atomic steps. Other threads should make a shallow copy if they are going to access this field more than once, since the field's value may change at any time.

Returns the StreamingQueryException if the query was terminated by an exception.

Prints the physical plan to the console for debugging purposes.

Prints the physical plan to the console for debugging purposes.

Returns the unique id of this query that persists across restarts from checkpoint data. That is, this id is generated when a query is started for the first time, and will be the same every time it is restarted from checkpoint data. Also see runId.

Interrupts the query execution thread and awaits its termination until until it exceeds the timeout. The timeout can be set on "spark.sql.streaming.stopTimeout".

Whether the query is currently active or not

Returns the most recent StreamingQueryProgress update of this streaming query.

Returns the user-specified name of the query, or null if not specified. This name can be specified in the org.apache.spark.sql.streaming.DataStreamWriter as dataframe.writeStream.queryName("query").start(). This name, if set, must be unique across all active queries.

A flag to indicate that a batch has completed with no new data available.

A write-ahead-log that records the offsets that are present in each batch. In order to ensure that a given batch will always consist of the same data, we write to this log *before* any processing is done. Thus, the Nth record in this log indicated data that is currently being processed and the N-1th entry indicates which offsets have been durably committed to the sink.

Pretty identified string of printing in logs. Format is If name is set "queryName [id = xyz, runId = abc]" else "[id = xyz, runId = abc]"

Blocks until all available data in the source has been processed and committed to the sink. This method is intended for testing. Note that in the case of continually arriving data, this method may block forever. Additionally, this method is only guaranteed to block until data that has been synchronously appended data to a org.apache.spark.sql.execution.streaming.Source prior to invocation. (i.e. getOffset must immediately reflect the addition).

The thread that runs the micro-batches of this stream. Note that this thread must be org.apache.spark.util.UninterruptibleThread to workaround KAFKA-1894: interrupting a running KafkaConsumer may cause endless loop.

Returns an array of the most recent StreamingQueryProgress updates for this query. The number of progress updates retained for each stream is configured by Spark session configuration spark.sql.streaming.numRecentProgressUpdates.

Returns the unique id of this run of the query. That is, every start/restart of a query will generate a unique runId. Therefore, every time a query is restarted from checkpoint, it will have the same id but different runIds.

Returns the SparkSession associated with this.

Starts the execution. This returns only after the thread has started and QueryStartedEvent has been posted to all the listeners.

Defines the internal state of execution

Returns the current status of the query.

Stops all streaming sources safely.

Metadata associated with the whole query

Used to report metrics to coda-hale. This uses id for easier tracking across restarts.

A list of unique sources in the query plan. This will be set when generating logical plan.

A map of current watermarks, keyed by the position of the watermark operator in the physical plan.

This state is 'soft state', which does not affect the correctness and semantics of watermarks and is not persisted across query restarts. The fault-tolerant watermark state is in offsetSeqMetadata.