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Spark Streaming vs. Structured Streaming

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Spark Streaming vs. Structured Streaming

In this post, we compare these two popular open source data platforms and the scenarios where each work best.

· Big Data Zone ·
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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:

  1. Spark Streaming
  2. Structured Streaming (introduced with Spark 2.x)

Let's discuss what these are exactly, what the differences are, and which one is better.

Spark Streaming

Spark Streaming is a separate library in Spark to process continuously flowing streaming data. It provides us with the DStream API, which is powered by Spark RDDs. DStreams provide us data divided into chunks as RDDs received from the source of streaming to be processed and, after processing, sends it to the destination. Cool, right?!

Structured Streaming

From the Spark 2.x release onwards, Structured Streaming came into the picture. Built on the Spark SQL library, Structured Streaming is another way to handle streaming with Spark. This model of streaming is based on Dataframe and Dataset APIs. Hence, with this library, we can easily apply any SQL query (using the DataFrame API) or Scala operations (using DataSet API) on streaming data.

Okay, so that was the summarized theory for both ways of streaming in Spark. Now we need to compare the two.

Distinctions

1. Real Streaming

What does real streaming imply? Data which is unbounded and is being processed upon being received from the source. This definition is satisfiable (more or less).

If we talk about Spark Streaming, this is not the case. Spark Streaming works on something we call a micro batch. The stream pipeline is registered with some operations and Spark polls the source after every batch duration (defined in the application) and then a batch is created of the received data, i.e. each incoming record belongs to a batch of DStream. Each batch represents an RDD.

Structured Streaming works on the same architecture of polling the data after some duration, based on your trigger interval, but it has some distinction from the Spark Streaming which makes it more inclined towards real streaming. In Structured Streaming, there is no batch concept. The received data in a trigger is appended to the continuously flowing data stream. Each row of the data stream is processed and the result is updated into the unbounded result table. How you want your result (updated, new result only, or all the results) depends on the mode of your operations (Complete, Update, Append).

Winner of this round: Structured Streaming.

2. RDD vs. DataFrames/DataSet

Another distinction can be the use case of different APIs in both streaming models. In summary, we read that Spark Streaming works on the DStream API, which is internally using RDDs and Structured Streaming uses DataFrame and Dataset APIs to perform streaming operations. So, it is a straight comparison between using RDDs or DataFrames. There are several blogs available which compare DataFrames and RDDs in terms of `performance` and `ease of use.` This is a good read for RDD v/s Dataframes. All those comparisons lead to one result: that DataFrames are more optimized in terms of processing and provide more options for aggregations and other operations with a variety of functions available (many more functions are now supported natively in Spark 2.4).

So Structured Streaming wins here with flying colors.

3. Processing With the Vent Time, Handling Late Data

One big issue in the streaming world is how to process data according to the event-time. Event-time is the time when the event actually happened. It is not necessary for the source of the streaming engine to prove data in real-time. There may be latencies in data generation and handing over the data to the processing engine. There is no such option in Spark Streaming to work on the data using the event-time. It only works with the timestamp when the data is received by the Spark. Based on the ingestion timestamp, Spark Streaming puts the data in a batch even if the event is generated early and belonged to the earlier batch, which may result in less accurate information as it is equal to the data loss. On the other hand, Structured Streaming provides the functionality to process data on the basis of event-time when the timestamp of the event is included in the data received. This is a major feature introduced in Structured Streaming which provides a different way of processing the data according to the time of data generation in the real world. With this, we can handle data coming in late and get more accurate results.

With event-time handling of late data, Structured Streaming outweighs Spark Streaming.

4. End-to-End Guarantees

Every application requires fault tolerance and end-to-end guarantees of data delivery. Whenever the application fails, it must be able to restart from the same point where it failed in order to avoid data loss and duplication. To provide fault tolerance, Spark Streaming and Structured Streaming both use the checkpointing to save the progress of a job. But this approach still has many holes which may cause data loss.

Other than checkpointing, Structured Streaming has applied two conditions to recover from any error:

  1. The source must be replayable.
  2. The sinks must support idempotent operations to support reprocessing in case of failures.

Here's a link to the docs to learn more.

With restricted sinks, Spark Structured Streaming always provides end-to-end, exactly once semantics. Way to go Structured Streaming!

5. Restricted or Flexible

Sink: The destination of a streaming operation. It can be external storage, a simple output to console, or any action

With Spark Streaming, there is no restriction to use any type of sink. Here we have the method foreachRDD to perform some action on the stream. This method returns us the RDDs created by each batch one-by-one and we can perform any actions over them, like saving to storage or performing some computations. We can cache an RDD and perform multiple actions on it as well (even sending the data to multiple databases).

But in Structures Streaming, until v2.3, we had a limited number of output sinks and, with one sink, only one operation could be performed and we could not save the output to multiple external storages. To use a custom sink, the user needed to implement ForeachWriter. But here comes Spark 2.4, and with it we get a new sink called foreachBatch. This sink gives us the resultant output table as a DataFrame and hence we can use this DataFrame to perform our custom operations.

With this new sink, the `restricted` Structured Streaming is now more `flexible` and gives it an edge over the Spark Streaming and other over flexible sinks.

Conclusion

We saw a fair comparison between Spark Streaming and Spark Structured Streaming. We can clearly say that Structured Streaming is more inclined to real-time streaming but Spark Streaming focuses more on batch processing. The APIs are better and optimized in Structured Streaming where Spark Streaming is still based on the old RDDs.

So to conclude this post, we can simply say that Structured Streaming is a better streaming platform in comparison to Spark Streaming.

Please make sure to comment your thoughts on this!

References:

  1. Structured Streaming Programming Guide
  2. Streaming Programming Guide
Topics:
big data ,spark streaming ,structured streaming ,apache spark

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