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This article will help any new developer who wants to control the volume of Spark Kafka streaming.
A Spark streaming job internally uses a micro-batch processing technique to stream and process data. The initial state of the job will be in the "queued" status, then it will then move to the "processing" status, and then it is marked with the "completed" status.
Apache Spark is a fast and general-purpose cluster computing system. In Spark, we can do the batch processing and stream processing as well. It does near real-time processing. It means that it processes the data in micro-batches. I have discussed more Spark Streaming in my previous blog. Now in this blog, I'll discuss Stateful Streaming in Spark. So let's start !!
Stateful stream processing means that a "state" is shared between events and therefore past events can influence the way current events are processed.
Fan of Apache Spark? I am too. The reason is simple. Interesting APIs to work with, fast and distributed processing, and, unlike MapReduce, there's no I/O overhead, it's fault tolerance, and much more. With this, you can do a lot in the world of big data and fast data. From "processing huge chunks of data" to "working on streaming data," Spark works flawlessly. In this post, we will be talking about the streaming power we get from Spark.
Spark provides us with two ways of working with streaming data: