Apache Spark has managed to gain wide popularity with time due to simple reasons. It works in distributive processing mode with fast pace and attractive, intruding APIs. In addition to this, it also includes fault tolerance along with I/O overhead map-reduce. It allows us to explore the world of IoT, Big Data, etc. and see what we more we can do with it. It has a lot of things under it form processing a large amount of data to streaming data flawlessly to maintain a flow.
However, Apache Spark Services has two types in which data streaming is possible by the companies.
Spark Streaming – It is a Spark library that is separately used to process data flow in continuous mode. The Spark RDDs power up the DStream API that used for the flow. This API can divide the data into smaller chunks that are received by RDDs from the main data source. This data is then processed further before sending to the destination.
Structured Streaming – After the second release of Spark in the form of Spark 2.x, we introduced with Structured Streaming. It widely built upon the SQL Library of Spark that allows Spark to handle data in a certain flow. The whole structure based on Dataset APIs and Dataframe. It is easy to apply SQL query with this library along with scala operation that gives an easy flow for data streaming.
Structured Streaming Vs. Spark Streaming
Since we are aware of both the steaming type, let us understand the difference between them.
1. Real Streaming –The unbounded data is processed easily when extracted from the source. However, Spark Streaming doesn’t support this definition for real streaming and mainly depends upon micro-batch. Some operations easily register as stream pipeline and the source with batches. The data received is then processed in the form of batches that belong to the DStream batch that is represented by RDD.
Whereas, when it comes to Structured Streaming, it gives out the same architecture that depends upon intervals. However, it is different than Spark Streaming that ensures that it is much more apt on the road to real streaming. It has no batch system to work upon and works up with a data trigger that helps in data streaming flow continuously. These rows process the data, which helps to get a result table in unbound data. However, the operation modes derive the result of data.
2. Flexible and Restriction –The streaming operation destination can be a simple console output external storage or different actions. The Spark streaming has no sink type restrictions that might tamper it. But foreachRDD is used to get streaming action performance. The RDDs returned with this as per the batches and actions performed. Some include performing some computations, storage-saving, etc. Multiple actions can perform in this with the help of RDD cache, including sending data to multiple databases.
The Structure Streaming works up with limited output sinks numbers. However, only one sink can operate but, no multiple external storages of output saved. The ForeachWriter is implemented by users to work with a custom sink. However, for each batch comes the latest version of Spark 2.4, which helps in getting the output result table in the form of a Dataframe. It is used to perform operations in custom mode.
3. Handling late data, event time –The event-time processing of data is one of the major issues faced by data streaming world. Real-time data don't have to be offered by the source to process it in a flow. When it comes to, data generation and handling then latencies are common to a processing engine for data. Spark Streaming doesn’t support this openly in the event-time but is mainly dependent on timestamp. The batch system is used to ingest the data no matter the timing of data collection. This limit the data result and mainly work in the form of data loss.
Structured streaming is different than Spark Streaming in this case. Its data processing is done in functionalities that help in data received along with a timestamp. It becomes a different way to process data as per the generation of time. It is a great way to process data generated in real-world that can be handled and gives out accurate results.
Conclusion
These are the basic points that differ Spark Structured Streaming with Spark Streaming. Both have their functionalities and work in a different mode. The Structure Streaming works more in real-time, whereas batch processing is, done in Spark Streaming. The RDDs are mainly working up with Spark Streaming and Structured Streaming helps with optimized and better API. They, both better in a way than each other but Structured Streaming has become an ideal choice.
