When you read the word comparison, you might expect the article to identify the pros and cons of the selected items and then tell you which one is better.
Well, with big data frameworks, there is no right or wrong. Every framework has its unique features and is applicable to a particular set of tasks and needs.
For this reason, this post is only a comparison of Spark vs. Hadoop MapReduce and the definition of their features. After reading the article and considering your business needs, you will be able to select the framework that works better for your particular case.
Yet, if you will be still in doubt, drop a line to our software experts and will help you to make the right choice.
The most important thing to remember about Hadoop and Spark is that they solve different business problems. While both operate within the area of big data processing, their purposes are different.
In Hadoop, the goal is to shape the infrastructure of the distributed data. This framework does not require any additional specialized equipment since it can distribute large collections of data among multiple nodes that form a cluster of servers. Hadoop reads from and writes everything to a disk. In a nutshell, Hadoop is more efficient to process and analyze large volumes of data.
Spark is more of a processing tool for big data. It can help to perform diverse operations on distributed data collections; however, you don’t get distributed storage for your data. This means that Spark is up to 100 times faster in data processing. Yet, be ready that Spark might not handle a large data scope as correctly as Hadoop MapReduce.
Enumeration of the strong and weak points of Apache Spark and Hadoop MapReduce will likely be of little use since each technology has its perks. So instead of just listing the pros and cons, we decided to compare the two based on some essential characteristics that will come in handy when you need to choose one, including:
In terms of operational speed, Spark overruns Hadoop MapReduce significantly. It is up to 100 times faster for data in RAM, and when it comes to data in storage, Spark is faster in up to 10 times. This means that if your business requires immediate results and awareness, then Spark’s in-memory processing is the right choice. This Apache’s system also outruns the competitor in typical graph processing since Spark has an API for graph computation called GraphX.
Hadoop MapReduce is always consistent and consecutive in its data delivery. Yes, its speed loses to Spark’s capabilities, but if you do not need speed and require quality, Hadoop is the right choice. For example, you can consider running operations at night to get the results by the next morning.
Whenever your business operates with substantial data sets, you need the system to run multiple checks at the same time and give the most exhaustive results. And this is what Hadoop does.
Hadoop breaks large chunks of data in smaller pieces to process them separately on different nodes; once the results are received, they are gathered from different nodes to return you a single result. Whenever your data chunk is bigger than RAM, it is only Hadoop that can complete the job.
Flexibility is Spark’s strong competitive advantage. A pretty large number of API functions and ease of use are among the main qualities of this system. Spark allows developers to switch from Python with Pandas easily, there is API DataSet; the rest of the functions after the basic acquaintance would seem even simpler than SQL, especially considering that you can supplement them with your UDF files.
With Java in mind, Spark offers great flexibility in terms of day-to-day operations for anyone, from the beginner to the pro.
Machine learning requires iterative data processing to keep educating the machine constantly. If your business is related to machine learning, then the system of your choice should be Apache Spark. Resilient Distributed Datasets (RDDs) of Spark allow enabling multiple map operations in the tool’s memory. Hadoop, for example, is destined to write interim results to a disk and hence cannot reprocess data so efficiently.
Be ready that both frameworks are not perfect in terms of technology usage. Spark can easily use RAM, disk, and even the processor. This means that before you go with Spark, ensure that your computer has two processors and strong technical characteristics to run such a tool.
Hadoop MapReduce also requires quite a lot of computer space owing to its main advantage—HDFS. This file system helps to store extensive product data but will need lots of storage space for it.
In terms of security, MapReduce beats its competitor. Even if the system fails and software is reinstalled, this system will save up to 99% of all the files. For a business that runs lots of data, losing all the analysis files would be a disaster that Hadoop MapReduce can prevent from happening.
As it was said in the introduction, you should consider which perks and characteristics are more valuable for your business. If you thrive for security and operations with large data sets, go with Hadoop MapReduce. Should you need fast and efficient results within a flexible system, Spark is your choice.