Big Data is like the omnipresent Big Brother in the modern world. The ever-increasing use cases of Big Data across various industries has further given birth to numerous Big Data technologies, of which Hadoop MapReduce and Apache Spark are the most popular. While both MapReduce and Spark are open-source flagship projects developed by the Apache Software Foundation, they are the strongest contenders of each other as well.
In this post, first, we will talk about the MapReduce and Spark frameworks, then we shall move on to discussing the key differences between them.
What are Spark & MapReduce?
Spark is a Big Data framework specially designed for enabling fast computation. It serves as a general-purpose data processing engine that can handle different workloads, including batch, interactive, iterative and streaming. A key feature of Spark is speed – it executes in-memory computations to increase the speed of data processing. As a result, it works well on a cluster of computer nodes and allows faster processing of large datasets.
Resilient Distributed Dataset (RDD) is the primary data structure of Spark. RDD is an immutable distributed collection of objects wherein each node is divided into smaller chunks that can be computed on different nodes of a cluster. This facilitates independent data processing within a cluster.
MapReduce is an open-source framework designed for processing vast amounts of data in a parallel and distributed environment. It can process data only in batch mode. There are two primary components of Hadoop MapReduce – HDFS and YARN.
MapReduce programming consists of two parts – Mapper and the Reducer. While the Mapper handles the task of sorting the data, the Reducer combines the sorted data and converts it into smaller fragments.
As for the fundamental difference between these two frameworks, it is their innate approach to data processing. While MapReduce processes data by reading from and writing on the disk, Spark can do in in-memory. Thus, Spark gets an advantage over MapReduce – of speedy processing.
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But does that mean Spark is better than MapReduce? Unfortunately, the debate is not that simple. To shed more light on this issue, we will break down the differences between them point by point.
Spark: As we mentioned earlier, Spark is more of a hybrid and general-purpose processing framework. Through in-memory computation and processing optimization, it speeds up the data processing in real-time. It is excellent for streaming workloads, running interactive queries, and ML algorithms. However, the RDD only allows Spark to store data on the disk temporarily by writing only the vital data on the disk. So, it loads a process in the memory and retains it in the cache. This makes Spark pretty much memory-intensive.
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MapReduce: MapReduce is the native batch processing engine of Hadoop. It’s components (HDFS and YARN) enable smoother processing of batch data. However, since the data processing takes place in several subsequent steps, the process is quite slow. An advantage of MapReduce is that it allows for permanent storage – it stores data on disk. This makes it suitable for handling massive datasets. As soon as a task is completed, MapReduce kills its processes and hence, it can run simultaneously with other services.
Ease of Use
Spark: When it comes to ease of use, Spark takes the crown. It comes with many user-friendly APIs for Scala (native language), Java, Python, and Spark SQL. Since Spark allows streaming, batch processing, and machine learning in the same cluster, you can easily simplify the data processing infrastructure according to your needs. Also, Spark includes an interactive REPL (Read–eval–print loop) mode for running commands that offers prompt feedback to users.
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MapReduce: Since Hadoop MapReduce is written in Java, it takes time to learn the syntax. Hence, initially, many may find it quite challenging to program. Although MapReduce lacks an interactive mode, tools like Pig and Hive make working with it a little easier. There are other tools (for instance, Xplenty) as well that can run MapReduce tasks without requiring any programming.
Spark: Spark employs RDD and different data storage models for fault tolerance by reducing network I/O. If there is a partition loss of an RDD, the RDD will rebuild that partition from the information stored in memory. Thus, if a process crashes midway, Spark will have to start processing from the very beginning.
MapReduce: Unlike Spark, MapReduce uses the replication concept for fault tolerance through Node Manager and ResourceManager. Here, if a process fails to execute midway, MapReduce will continue from where it left off, thereby saving time.
Spark: Since Spark is still at its infancy, its security factor is not highly developed. It supports authentication via a shared secret (password authentication) sheet. As for the web UI, it can be protected through javax servlet filters. Spark’s YARN and HDFS features allow for Kerberos authentication, HDFS file-level permissions, and encryption between nodes.
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MapReduce: MapReduce is far more developed and hence, it has better security features than Spark. It enjoys all the security perks of Hadoop and can be integrated with Hadoop security projects, including Knox Gateway and Sentry. Through valid third-party vendors, organizations can even use Active Directory Kerberos and LDAP for authentication.
Although both Spark and MapReduce are open-source projects, there are certain costs you must incur for both. For instance, Spark required large amounts of RAM to run tasks in-memory, and as it goes, RAM is costlier than hard disks. On the contrary, Hadoop is disk-oriented – while you will not need to buy expensive RAM, you will have to invest more in systems for distributing the disk I/O across multiple systems.
So, with respect to cost, it largely depends on the requirements of the organization. If an organization needs to process massive amounts of big data, Hadoop will be the cost-efficient option since buying hard disk space is way cheaper than buying expansive memory space. Moreover, MapReduce comes with a host of Hadoop-as-a-service offerings and Hadoop-based services that allow you to skip the hardware and staffing requirements. Compared to this, there are only a handful of Spark-as-a-service choices.
As far as compatibility goes, both Spark and MapReduce are compatible with one another. Spark can be seamlessly integrated with all the data sources and file formats supported by Hadoop. Also, both are scalable. So, Spark’s compatibility with data types and data sources is pretty much the same as that of Hadoop MapReduce.
As you can see, both Spark and MapReduce have unique features that set them apart from each other. For instance, Spark offers real-time analytics that MapReduce lacks, whereas MapReduce comes with a file system that Spark lacks. Both frameworks are excellent in their distinct way, and both come with their unique set of advantages and disadvantages. Ultimately, the debate between Spark vs MapReduce all comes down to your specific business needs and the kind of tasks you wish to accomplish.
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What is the misconception about Hadoop?
Hadoop being open-source derives many misconceptions. Regardless of its ease of use, managing Hadoop’s servers could add up to the cost. Big Data management can extend the costs to INR 4 Lakh when working with features like network storage. Hadoop is not a database. It is, instead, a data warehouse where data is kept, monitored, analysed, and distributed. Another misconception about Hadoop is the difficulty of setting it up. Hadoop’s management could be daunting at higher levels; however, working with MapReduce programming at certain levels is very easy. Lastly, Hadoop’s exclusive access. Big Data is not for big companies. Small companies and businesses can use Hadoop to expedite their workflows. Also, features like Excel could contribute to bringing additional power.
Why should you study Hadoop or Spark?
Hadoop is reasonable. Businesses can use Hadoop to store their data without worrying about the cost. Hadoop is an excellent opportunity to grab the rapidly growing data market. Also, Hadoop professionals are in huge demand in the industry right now. On the other hand, there are plenty of reasons to learn Spark. Using Spark, companies can get rid of Big Data issues. Plus, it opens the door to explore all the opportunities in the market. Spark developers are the need of business organisations who are skilled with an understanding of the technology. Compared to Hadoop, Spark’s data processing speed is extensive. Finally, earning a high figure salary as a professional with Apache Spark experience wouldn’t be difficult.
What are the critical limitations of Apache Spark?
Apache Spark misses out on having its own file management system, making it hard to store data. To meet the requirements depends on Hadoop. Apache Spark isn’t capable of handling multiple users at once. This limits multiple users from using the platform at once. Spark’s inefficiency in working with small files is another demerit that it isn’t effective.