Is Apache Spark Lacking Something? Let’s Find Out

Apache Spark is an extremely advanced big data processing tool that contains superb features. The tool is known worldwide for its high speed of processing. Being a solution for general-purpose, Apache Spark Services is adopted by a vast majority of the companies. Spark is a cluster computing solution that eases the process of big data processing. Apache Spark is known for its high-level API. It is a tool which is used for running several Spark app. There have been a lot of discussions regarding the similarities of the tool with Hadoop.

Apache Spark has been compared extensively with Hadoop; however, it is a lot faster than Hadoop. Its speed is super high when compares to other tools, in fact, its speed is even higher than the pace at which the data is being accessed from the disk. This big data processing system is written in Scala. However, it provides APIs in other languages like Python and Java as well.

Though, as mentioned above as well, there are several reasons that make Apache Spark a superb solution for big data processing, but, at the same time, there are a couple of shortcomings of this solution as well, and in this article, we will discuss the cons of using Spark or the areas where it can improve.

Here’re a few of the shortcomings:

• Streaming issues, specifically related to the real-time data processing

Spark streaming can be improved as well; especially real-time streaming. Though Spark is majorly known for real-time streaming of the data, still there is a lot of scope of enhancement of the real-time streaming feature. For the streaming of data, it is first segmented in different batches. The batches are of the pre-defined interval. And, every batch for Spark streaming is treated as an RDD. The Resilient Distributed Database ensures that the data is processed in batches, therefore, the data processing may not be considered in real-time. Instead, there is a halt in the processing of the live data, thus, the speed is affected a little bit. And, real-time processing is not exactly ‘real-time’ as the tool follows the process of micro-batch processing.

Thus, there is a scope of improvement and the tool can be upgraded to process the data on a real-time basis. Thus, batch processing can be replaced with a faster machine or it can be evolved further. We will have to wait and see how different or better the future of Apache Spark is.

• Debugging can be a bit tough

Generally, the distributed cloud systems are a little bit more complicated; therefore, debugging is a little tougher. At times, the error notifications that the users receive are not very easy to understand. Additionally, the introspection into the existing processes is not very quick. Debugging Spark can be frustrating. Though, the users can identify some errors while checking for the Data Frame tasks in PySpark. But, the prominent memory issues, as well as the problems that occur within the user-defined operations, can be a little tough to identify and solve. Also, at times the local tests may even fail while running on a cluster and testers would be left with no choice than to identify the cause of the problem.

• Memory related problems

There have been a couple of discussions related to the in-memory capabilities of Apache Spark as well. Memory issues can become an issue when it comes to the processing of the data. Also, it hampers the cost-effectiveness of the data processing process. At the end of the day, storing the huge volume of data isn’t easy and it can be a bit expensive as well. Therefore, the makers can improve the memory capabilities of Spark to ensure that the data is saved economically. The tool may require a lot of RAM and the cost can be higher. Therefore, it is important to focus on boosting the memory to make sure that the memory issues are resolved.

Apache Spark is an advanced solution and it is being upgraded at regular intervals. The tool is evolved to make sure that it meets the new requirements of the businesses. Also, the tool has to be made fit for the ever-changing industry needs; therefore, it is being evolved constantly. This distributed computing framework is used to offer a clean big data processing experience to the data scientists.

The distributed architecture makes Spark a preferred tool as it is extremely easy to use. Also, the tool is known for its high performance. But, at the same time, the tool is being upgraded to eradicate the shortcomings. After all, if all the cons are removed, then Spark will become a paramount big data processing tool. Thus, a lot of steps have been taking to overcome the capability issues or the performance issues and the future of Spark seems to be pretty bright.