SnappyStreamingContext
Related Docs:
object SnappyStreamingContext
| package streaming
Recreate a SnappyStreamingContext from a checkpoint file using an existing SparkContext.
Recreate a SnappyStreamingContext from a checkpoint file using an existing SparkContext.
Path to the directory that was specified as the checkpoint directory
Existing SparkContext
Recreate a SnappyStreamingContext from a checkpoint file.
Recreate a SnappyStreamingContext from a checkpoint file.
Path to the directory that was specified as the checkpoint directory
Recreate a SnappyStreamingContext from a checkpoint file.
Recreate a SnappyStreamingContext from a checkpoint file.
Path to the directory that was specified as the checkpoint directory
Optional, configuration object if necessary for reading from HDFS compatible filesystems
Create a SnappyStreamingContext by providing the configuration necessary for a new SparkContext.
Create a SnappyStreamingContext by providing the configuration necessary for a new SparkContext.
a org.apache.spark.SparkConf object specifying Spark parameters
the time interval at which streaming data will be divided into batches
Create a SnappyStreamingContext using an existing SparkContext.
Create a SnappyStreamingContext using an existing SparkContext.
existing SparkContext
the time interval at which streaming data will be divided into batches
Add a org.apache.spark.streaming.scheduler.StreamingListener object for receiving system events related to streaming.
Add a org.apache.spark.streaming.scheduler.StreamingListener object for receiving system events related to streaming.
Wait for the execution to stop.
Wait for the execution to stop. Any exceptions that occurs during the execution will be thrown in this thread.
Wait for the execution to stop.
Wait for the execution to stop. Any exceptions that occurs during the execution will be thrown in this thread.
time to wait in milliseconds
true if it's stopped; or throw the reported error during the execution; or false
if the waiting time elapsed before returning from the method.
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as flat binary files, assuming a fixed length per record, generating one byte array per record.
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as flat binary files, assuming a fixed length per record, generating one byte array per record. Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored.
HDFS directory to monitor for new file
length of each record in bytes
We ensure that the byte array for each record in the resulting RDDs of the DStream has the provided record length.
Set the context to periodically checkpoint the DStream operations for driver fault-tolerance.
Set the context to periodically checkpoint the DStream operations for driver fault-tolerance.
HDFS-compatible directory where the checkpoint data will be reliably stored. Note that this must be a fault-tolerant file system like HDFS.
Creates a SchemaDStream from an DStream of Product (e.g.
Creates a SchemaDStream from an DStream of Product (e.g. case classes).
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format. Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored.
Key type for reading HDFS file
Value type for reading HDFS file
Input format for reading HDFS file
HDFS directory to monitor for new file
Function to filter paths to process
Should process only new files and ignore existing files in the directory
Hadoop configuration
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format. Files must be written to the monitored directory by "moving" them from another location within the same file system.
Key type for reading HDFS file
Value type for reading HDFS file
Input format for reading HDFS file
HDFS directory to monitor for new file
Function to filter paths to process
Should process only new files and ignore existing files in the directory
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format.
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format. Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored.
Key type for reading HDFS file
Value type for reading HDFS file
Input format for reading HDFS file
HDFS directory to monitor for new file
:: DeveloperApi ::
:: DeveloperApi ::
Return the current state of the context. The context can be in three possible states -
Create an input stream from a queue of RDDs.
Create an input stream from a queue of RDDs. In each batch, it will process either one or all of the RDDs returned by the queue.
Type of objects in the RDD
Queue of RDDs. Modifications to this data structure must be synchronized.
Whether only one RDD should be consumed from the queue in every interval
Default RDD is returned by the DStream when the queue is empty. Set as null if no RDD should be returned when empty
Arbitrary RDDs can be added to queueStream, there is no way to recover data of
those RDDs, so queueStream doesn't support checkpointing.
Create an input stream from a queue of RDDs.
Create an input stream from a queue of RDDs. In each batch, it will process either one or all of the RDDs returned by the queue.
Type of objects in the RDD
Queue of RDDs. Modifications to this data structure must be synchronized.
Whether only one RDD should be consumed from the queue in every interval
Arbitrary RDDs can be added to queueStream, there is no way to recover data of
those RDDs, so queueStream doesn't support checkpointing.
Create an input stream from network source hostname:port, where data is received as serialized blocks (serialized using the Spark's serializer) that can be directly pushed into the block manager without deserializing them.
Create an input stream from network source hostname:port, where data is received as serialized blocks (serialized using the Spark's serializer) that can be directly pushed into the block manager without deserializing them. This is the most efficient way to receive data.
Type of the objects in the received blocks
Hostname to connect to for receiving data
Port to connect to for receiving data
Storage level to use for storing the received objects (default: StorageLevel.MEMORY_AND_DISK_SER_2)
Create an input stream with any arbitrary user implemented receiver.
Create an input stream with any arbitrary user implemented receiver. Find more details at http://spark.apache.org/docs/latest/streaming-custom-receivers.html
Custom implementation of Receiver
Registers and executes given SQL query and returns SchemaDStream to consume the results
Registers and executes given SQL query and returns SchemaDStream to consume the results
the query to register
Set each DStream in this context to remember RDDs it generated in the last given duration.
Set each DStream in this context to remember RDDs it generated in the last given duration. DStreams remember RDDs only for a limited duration of time and release them for garbage collection. This method allows the developer to specify how long to remember the RDDs ( if the developer wishes to query old data outside the DStream computation).
Minimum duration that each DStream should remember its RDDs
Creates an input stream from TCP source hostname:port.
Creates an input stream from TCP source hostname:port. Data is received using a TCP socket and the receive bytes it interpreted as object using the given converter.
Type of the objects received (after converting bytes to objects)
Hostname to connect to for receiving data
Port to connect to for receiving data
Function to convert the byte stream to objects
Storage level to use for storing the received objects
Creates an input stream from TCP source hostname:port.
Creates an input stream from TCP source hostname:port. Data is received using
a TCP socket and the receive bytes is interpreted as UTF8 encoded \n delimited
lines.
Hostname to connect to for receiving data
Port to connect to for receiving data
Storage level to use for storing the received objects (default: StorageLevel.MEMORY_AND_DISK_SER_2)
Return the associated Spark context
Return the associated Spark context
Start the execution of the streams.
Start the execution of the streams. Also registers population of AQP tables from stream tables if present.
IllegalStateException if the StreamingContext is already stopped
Stop the execution of the streams, with option of ensuring all received data has been processed.
Stop the execution of the streams, with option of ensuring all received data has been processed.
if true, stops the associated SparkContext. The underlying SparkContext will be stopped regardless of whether this StreamingContext has been started.
if true, stops gracefully by waiting for the processing of all received data to be completed
Stop the execution of the streams immediately (does not wait for all received data to be processed).
Stop the execution of the streams immediately (does not wait for all received data
to be processed). By default, if stopSparkContext is not specified, the underlying
SparkContext will also be stopped. This implicit behavior can be configured using the
SparkConf configuration spark.streaming.stopSparkContextByDefault.
If true, stops the associated SparkContext. The underlying SparkContext will be stopped regardless of whether this StreamingContext has been started.
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as text files (using key as LongWritable, value as Text and input format as TextInputFormat).
Create an input stream that monitors a Hadoop-compatible filesystem for new files and reads them as text files (using key as LongWritable, value as Text and input format as TextInputFormat). Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored.
HDFS directory to monitor for new file
Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams.
Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams.
Create a unified DStream from multiple DStreams of the same type and same slide duration.
Create a unified DStream from multiple DStreams of the same type and same slide duration.
Main entry point for SnappyData extensions to Spark Streaming. A SnappyStreamingContext extends Spark's org.apache.spark.streaming.StreamingContext to provides an ability to manipulate SQL like query on org.apache.spark.streaming.dstream.DStream. You can apply schema and register continuous SQL queries(CQ) over the data streams. A single shared SnappyStreamingContext makes it possible to re-use Executors across client connections or applications.