December 20, 2019 Last updated December 19th, 2019 517 Reads share

The Imperfections of Apache Spark You Should be Aware!

Apache Spark ImperfectionsImage Credit: DepositPhotos

Apache Spark is a widely used big data tool. It is has been in the big data industry for quite a long time. Spark is an advanced big data tool with tons of amazing features. All its superb features have made it a top choice of the industry. However, there are some limitations of Apache Spark as well. There are certain things for which Spark may not be a perfect choice. We will discuss the shortcomings of Apache Spark in this article. But, let’s start by understanding how it works.

Apache Spark is almost 10 years old, however, it has evolved over a period of time. Spark has been upgraded constantly; therefore, it becomes an eminent part of most of the big data projects across the globe. As per a few of the analytics reports, Apache Spark was almost valued at somewhere around $2.75 billion.

Let’s get closer to Apache Spark

Apache Spark is a general-purpose system. Also, it is known for lightning-fast speed. The tool contains a high-level. It is a perfect solution for running the Spark apps. What makes Spark a top choice of the industry is the fact that it is a lot faster, almost 100 times faster than other big data tools like Hadoop. It is even quicker than accessing the data from the disk. The tool is developed using Scala; however, it provides extensive APIs in different languages like Python, Java, R, etc. Additionally, Spark can be easily integrated with Hadoop. Thus, the tool is programmed to even process the present Hadoop HDFS data, and it can even access data from Hive, Cassandra, Tachyon, etc.

In order to become an Apache Spark expert, you would have to understand the complete ecosystem components. There are several Spark modules, Spark video tutorials as well as the certification courses that teach you how to use Spark to the fullest. Also, you should be learning the actions in Apache Spark RDD, transformations and plenty of other things. You need to study the Spark Overview in detail. And, later on, focus on studying the history of Spark, its architecture as well as the process of the deployment.

What are the shortcomings of Apache Spark?

Lack of an advanced optimization process

One of the most talked-about shortcomings of Spark is the lack of an automatic optimization process. Though the other big data tools contain an automatic system, therefore, they score higher than Spark. When it comes to using Apache Spark Services, you would have to optimize the code manually. Since there isn’t an option to automatically write the code optimization process. Therefore, the process becomes a bit more time consuming and it is not as reliable as it would have been if the optimization was automatic. As most of the other big data tools are integrating automated features and techniques, therefore, this lack of automatic optimization process may result in less adoption of Spark.

Bugs here and there

Apache Spark has a few issues as well, and one of them is ‘the bugs’. A few of the companies, like Walmart, have experienced some problems with Spark. They had identified a possible memory leak somewhere when they were making the demand forecasting system. Though, there was some evidence that showed that Spark did not run as reliable as it was planned to run. Walmart’s experts felt that there are some bugs in the system, but they were not able to spot them quickly. Additionally, it was a little tough for them to solve the problems and get the results without hassle. The issue was that Spark was running as expected for some time, and then suddenly it was generating ‘garbage’ in between. So, the experts were a bit confused too, but they tried everything possible to solve the issue.

Tiny files create bigger trouble in Spark

Though Apache Spark is used for a wide range of different files, there seems to be some problem with the small-sized files, especially, if it is being used along with Hadoop. HDFS offers only a restricted number of big-sized files instead of a host of tiny files. Additionally, whenever the data is stored in S3, the situation becomes a little tough for Spark specially if we are talking about the gzipped files. And, when there are a large number of these sorts of files, then the users face some issues. Though, Spark has to keep these files and safe and then try and uncompress them. However, such files could only be unzipped of all the whole files are sited on the one core. Therefore, a lot of time has to be invested in unzipping. Also, a lot of partitions happen automatically within the RDD.

Spark is considered a bit costly

Big data collection, management, and analysis come with a certain cost involved. First of all, you will need the right sources, the right tools and the right techniques to start the process. Also, the in-memory capability is also an issue for the firms who want to adopt cost-effective methods for big data storing and processing. Holding on to the data in memory is a costly affair, simply because the memory consumption is on a higher side. Additionally, it is not even managed in a very friendly manner. Spark needs a large volume of the RAM to run in-memory; therefore, there is no doubt about the fact that it is termed as a bit costly for the processing of big data.

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Conclusion

Inspire of some of the shortcomings, IBM has been using Apache Spark for a host of its big data projects. Simply, because they think that Spark will suit their needs. The company has been trusting Spark and the tool is turning out to be quite helpful for them. The tool is expected to be used more and more in the coming future. However, the company will have to definitely invest a lot of energy and time in overcoming the shortcomings of the tool and the tool will turn out to be pretty beneficial for the businesses.

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James Warner

James Warner

Business Analyst / Business Intelligence Analyst as well as Experienced programming and software developer with Excellent knowledge on Hadoop/Big data analysis, Data Warehousingt/Data Staging/ETL tool, design and development, testing and deployment of software systems from the development stage to production stage with giving emphasis on Object-oriented paradigm.

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