As time goes on, there are many different distributed systems that are on the market to help manage data volume, velocity, and variety. Among all of the different systems that are out there, Spark and Hadoop are the two that will get most of the talk in the market. With such great options out there, how are you going to choose the one that is best for you?
In this article, we are going to take some time to look more at the idea of Hadoop and Spark and see how each one will benefit your work and which one to choose.
What is Hadoop?
First, we will take a look at Hadoop. Hadoop was a project for Yahoo in 2006 and before long it became a top-level Apache open-source project. It is used for distributed processing that has several different components. These components will include the Hadoop Distributed File system, which will store some files in the Hadoop format and will parallelize them across the cluster, YARN, which helps to schedule the right application runtimes, and MapReduce, which is an algorithm that will help to process the data in parallel.
Hadoop is a platform that is built with the help of Java and is accessible through many languages for writing the codes you want. Python is one of the most popular, though the programmer is able to choose the one that works with them.
Of course, these are just a few of the basics of Hadoop. This platform also includes Hive, which is simple to use like SQL, Sqoop, which will move all of your relational data to the HDFS, and Mahout to help with some of the machine learning you would like to do. It can even work with Azure and S3 buckets to add some more functionality to your work.
What is Spark?
We can also take a look at Spark and see how this compares or is different from Hadoop. Spark is a bit newer, being developed in 2012. It is also a top-level Apache project that is meant to focus on processing data in a parallel across the cluster. However, a big difference between this and Hadoop is that it will work in-memory instead.
While Hadoop is designed to write and read the files in its own HDFS, Spark is going to process the data using the RAM or the Resilient Distributed Dataset, or RDD. Spark can work with a cluster of Hadoop or it can run as a stand-alone source.
The whole idea is that Spark is structured to work around Spark Core, the engine that drives the RDD abstraction, optimizations, and scheduling and then can connect Spark to the right file system when it is all done.
There are also a few libraries that work won top of Spark Core, including Spark SQL, which works similarly to SQL as well. There are several APIs that will help you get the work done and because there are a ton of data scientists that will work with this, R and Python endpoints were added as well.
We can also look at the performance of these two options. Spark is able to run about 100 times faster in-memory and even 10 times faster on the disk compared to Hadoop. It has been used to sort out 100 TB of data much faster than what Hadoop is able to do, and works the best with some of the more complex machine learning options, including the k-means and Naïve Bayes.
Spark performance, when we measure it with processing speed, has been found to be the best over Hadoop for a few reasons. This includes the idea that Spark is not bound by the concerns of input and output each time it runs, which allows it to be faster for completing a lot of applications.
The Spark DAGs enable optimization between the steps. Hadoop doesn’t really have a cyclical connection that has to happen between the steps, so you don’t need to worry about performance tuning. However, if you use Spark and have it running on the YARN with other shared services, the performance can go down due to RAM having a lot of overhead memory leaks.
The neat thing here is that both of these platforms have free open-sourced projects. This means that developers are able to at least get started without any installation costs. You do need to consider here the total cost of ownership, which includes other options like hiring a team that can handle this, the software and hardware purchases, and maintenance as well.
The general rule of thumb is that Hadoop is going to require more memory on a disk and Spark will need more RAM. In this way, Spark clusters are going to be more expensive. And since Spark is newer than Hadoop, finding experts is harder and more expensive as well.
If you need more than the free version of this, you can pay for some of it as well. The price will depend on how memory and more that you would like to use.
Many people find that Hadoop works well because it is fault-tolerant. The reason for this is that Hadoop has been designed to replicate data through a variety of nodes. Each file will be split into blocks so that you can replicate it many times across many machines. This means that if even one machine goes down in the system, you are able to rebuild from the other blocks.
Spark’s fault tolerance can happen but it is through RDD operations. As the RDD is built, the lineage will be as well, which will remember how the set was constructed. And since this is seen as immutable, you can easily rebuild it from scratch as necessary. This makes it easier to reuse as well without having to start from scratch at any time.
The security is good as well. Hadoop is seen as slightly better, though Spark is not one to let the security go down on any project you choose.
Both of these programs work well for machine learning as well. Because of better performance and speed, many data scientists choose to work with Spark over Hadoop. But you will find that Hadoop does come with some of the great tools and features that you need to handle machine learning. In a world of machine learning and data science, this is always a good thing.
So, are you going to work with Hadoop or Spark? It is possible to use both of these together in some cases, which can be a lot of fun, but since they are different, many professionals will need to choose one or another.
The two systems are some of the best, and it really depends on what you want to get done. If you need something that is more flexible, then you would want to go with Spark. If you would like something that costs less for the memory processing architecture and works for some of the heavier operations, then Hadoop is the choice for you.