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Extension of the core Spark API
- Spark Streaming is an extension of the core Spark API that allows data engineers and data scientists to process real-time data from various sources including (but not limited to) Kafka, Flume, and Amazon Kinesis. This processed data can be pushed out to file systems, databases, and live dashboards.
www.databricks.com/glossary/what-is-spark-streaming
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Apache Spark Streaming is a scalable fault-tolerant streaming processing system that natively supports both batch and streaming workloads.
- Structured Streaming Documentation
Apache Spark provides a Structured Streaming Programming...
- Streaming
This tutorial module introduces Structured Streaming, the...
- Structured Streaming Documentation
- Overview
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Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Data can be ingested from many sources like Kafka, Flume, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like map, reduce, join and window. F...
Internally, it works as follows. Spark Streaming receives live input data streams and divides the data into batches, which are then processed by the Spark engine to generate the final stream of results in batches.
If you have already downloaded and built Spark, you can run this example as follows. You will first need to run Netcat (a small utility found in most Unix-like systems) as a data server by using Any operation applied on a DStream translates to operations on the underlying RDDs. For example, in the earlier example of converting a stream of lines to ...
For an up-to-date list, please refer to the Maven repository for the full list of supported sources and artifacts. For more details on streams from sockets and files, see the API documentations of the relevant functions in StreamingContext for Scala, JavaStreamingContext for Java, and StreamingContext for Python.
To initialize a Spark Streaming program, a StreamingContext object has to be created which is the main entry point of all Spark Streaming functionality.
Discretized Stream or DStream is the basic abstraction provided by Spark Streaming. It represents a continuous stream of data, either the input data stream received from source, or the processed data stream generated by transforming the input stream. Internally, a DStream is represented by a continuous series of RDDs, which is Sparks abstraction of...
Spark Streaming provides two categories of built-in streaming sources. We are going to discuss some of the sources present in each category later in this section.
Note that, if you want to receive multiple streams of data in parallel in your streaming application, you can create multiple input DStreams (discussed further in the Performance Tuning section). This will create multiple receivers which will simultaneously receive multiple data streams. But note that a Spark worker/executor is a long-running task,...
For reading data from files on any file system compatible with the HDFS API (that is, HDFS, S3, NFS, etc.), a DStream can be created as via StreamingContext.fileStream[KeyClass, ValueClass, InputFormatClass].
Spark Streaming will monitor the directory dataDirectory and process any files created in that directory.
Full Filesystems such as HDFS tend to set the modification time on their files as soon as the output stream is created. When a file is opened, even before data has been completely written, it may be included in the DStream - after which updates to the file within the same window will be ignored. That is: changes may be missed, and data omitted from...
To guarantee that changes are picked up in a window, write the file to an unmonitored directory, then, immediately after the output stream is closed, rename it into the destination directory. Provided the renamed file appears in the scanned destination directory during the window of its creation, the new data will be picked up. Note that using upda...
In contrast, Object Stores such as Amazon S3 and Azure Storage usually have slow rename operations, as the data is actually copied. Furthermore, renamed object may have the time of the rename() operation as its modification time, so may not be considered part of the window which the original create time implied they were.
Careful testing is needed against the target object store to verify that the timestamp behavior of the store is consistent with that expected by Spark Streaming. It may be that writing directly into a destination directory is the appropriate strategy for streaming data via the chosen object store.
Python API As of Spark 2.4.3, out of these sources, Kafka, Kinesis and Flume are available in the Python API.
This category of sources require interfacing with external non-Spark libraries, some of them with complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts of dependencies, the functionality to create DStreams from these sources has been moved to separate libraries that can be linked to explicitly when nec...
There can be two kinds of data sources based on their reliability. Sources (like Kafka and Flume) allow the transferred data to be acknowledged. If the system receiving data from these reliable sources acknowledges the received data correctly, it can be ensured that no data will be lost due to any kind of failure. This leads to two kinds of receive...
In every batch, Spark will apply the state update function for all existing keys, regardless of whether they have new data in a batch or not. If the update function returns None then the key-value pair will be eliminated.
The transform operation (along with its variations like transformWith) allows arbitrary RDD-to-RDD functions to be applied on a DStream. It can be used to apply any RDD operation that is not exposed in the DStream API. For example, the functionality of joining every batch in a data stream with another dataset is not directly exposed in the DStream ...
Spark Structured Streaming abstracts away complex streaming concepts such as incremental processing, checkpointing, and watermarks so that you can build streaming applications and pipelines without learning any new concepts or tools.
Apr 30, 2023 · Spark Streaming is an extension of the Apache Spark cluster computing system that enables processing of real-time data streams. It allows you to process and analyze...
- Creating streaming DataFrames and streaming Datasets. Streaming DataFrames can be created through the DataStreamReader interface (Scala/Java/Python docs) returned by SparkSession.readStream().
- Operations on streaming DataFrames/Datasets. You can apply all kinds of operations on streaming DataFrames/Datasets – ranging from untyped, SQL-like operations (e.g.
- Starting Streaming Queries. Once you have defined the final result DataFrame/Dataset, all that is left is for you to start the streaming computation. To do that, you have to use the DataStreamWriter (Scala/Java/Python docs) returned through Dataset.writeStream().
- Managing Streaming Queries. The StreamingQuery object created when a query is started can be used to monitor and manage the query. query = df.writeStream.format("console").start() # get the query object query.id() # get the unique identifier of the running query that persists across restarts from checkpoint data query.runId() # get the unique id of this run of the query, which will be generated at every start/restart query.name() # get the name of the auto-generated or user-specified name query.explain() # print detailed explanations of the query query.stop() # stop the query query.awaitTermination() # block until query is terminated, with stop() or with error query.exception() # the exception if the query has been terminated with error query.recentProgress # a list of the most recent progress updates for this query query.lastProgress # the most recent progress update of this streaming query.
This tutorial module introduces Structured Streaming, the main model for handling streaming datasets in Apache Spark. In Structured Streaming, a data stream is treated as a table that is being continuously appended. This leads to a stream processing model that is very similar to a batch processing model.
Spark Streaming was an extension of the core Apache Spark API. It’s what enabled Spark to receive real-time streaming data from sources like Kafta, Flume and the Hadoop Distributed File System. It also allowed Spark to push out data to live dashboards, file systems and databases, providing near real-time data ingestion.
