Do you wish to understand what Hadoop MapReduce and Apache Spark are? Do you wish to understand the various factors that drive the Hadoop MapReduce vs Spark decision? If yes, then you’ve come to the right place.
Big Data has become the catalyst to business growth in all industries. Of all the tools that process Big Data, Hadoop MapReduce and Apache Spark attract the most attention.
The two are Open-source projects from Apache Software Foundation, and they form the leading products for Big Data Analytics. Hadoop has been the leading tool for Big Data Analytics for 5 years. Recent market research has shown that Hadoop has been installed by 50,000+ customers, while Apache Spark has only 10,000+ installations. However, the popularity of Apache Spark skyrocketed in 2013, overcoming that of Hadoop in only one year. Research done in 2016 shows that this trend is still ongoing. Currently, Apache Spark is leading with an installation growth rate of 47% against 14% of Hadoop.
In this article, you will learn what Hadoop MapReduce and Apache Spark are and the various factors that drive the Hadoop MapReduce vs Spark decision.
Table of contents
Introduction to Hadoop MapReduce
Hadoop MapReduce is a processing model within the Apache Hadoop project. Hadoop is a platform that was developed to handle Big Data via a network of computers that store and process data. Hadoop has affordable dedicated servers that you can use to run a Cluster. You can process your data using low-cost consumer hardware. It is a highly scalable platform using which you can start with one machine initially and increase them later as per business and data requirements.
Its two major default components are as follows:
- Hadoop MapReduce
- HDFS (Hadoop File System)
Hadoop MapReduce is a programming model that facilitates the processing of Big Data that is stored on HDFS. Hadoop MapReduce relies on the resources of multiple interconnected computers to handle large amounts of both structured and unstructured data.
Before the introduction of Apache Spark and other Big Data Frameworks, Hadoop MapReduce was the only player in Big Data Processing.
Hadoop MapReduce works by assigning data fragments across nodes in the Hadoop Cluster. The idea is to split a dataset into a number of chunks and apply an algorithm to the chunks for processing at the same time. The use of multiple machines to perform parallel processing on the data increases the processing speed.
Introduction to Spark
Apache Spark is an Open-source and Distributed System for processing Big Data workloads. It uses optimized query execution and in-memory caching to improve the speed of query processing on data of any size.
So, Apache Spark is a general and fast engine for processing data on a large scale. Apache Spark is faster than most Big Data Processing solutions, and that’s why it has taken over most of them to become the most preferred tool for Big Data Analytics.
Apache Spark is faster because it runs on memory (RAM) rather than on disk. Apache Spark can be used for multiple tasks including running distributed SQL, ingesting data into a database, creating data pipelines, working with data streams or graphs, machine learning algorithms, and much more.
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Factors that Drive the Hadoop MapReduce vs Spark Decision
To help you decide which one to choose, let’s discuss the differences between Hadoop MapReduce and Apache Spark:
1) Hadoop MapReduce vs Spark: Performance
Apache Spark is well-known for its speed. It runs 100 times faster in-memory and 10 times faster on disk than Hadoop MapReduce. The reason is that Apache Spark processes data in-memory (RAM), while Hadoop MapReduce has to persist data back to the disk after every Map or Reduce action.
Apache Spark’s processing speed delivers near Real-Time Analytics, making it a suitable tool for IoT sensors, credit card processing systems, marketing campaigns, security analytics, machine learning, social media sites, and log monitoring.
2) Hadoop MapReduce vs Spark: Ease of Use
Apache Spark comes with in-built APIs for Scala, Java, and Python, and it also includes Spark SQL (formerly called Shark) for SQL users. Apache Spark also has simple building blocks, which make it easy for users to write user-defined functions. You can use Apache Spark in interactive mode to get immediate feedback when running commands.
On the other hand, Hadoop MapReduce was written in Java and is difficult to program. Unlike Apache Spark, Hadoop MapReduce doesn’t provide a way to use it in interactive mode.
Considering the above-stated factors, it can be concluded that Apache Spark is easier to use than Hadoop MapReduce.
3) Hadoop MapReduce vs Spark: Data Processing Capabilities
With Apache Spark, you can do more than just plain data processing. Apache Spark can process graphs and also comes with its own Machine Learning Library called MLlib. Due to its high-performance capabilities, you can use Apache Spark for Batch Processing as well as near Real-Time Processing. Apache Spark is a “one size fits all” platform that can be used to perform all tasks instead of splitting tasks across different platforms.
Hadoop MapReduce is a good tool for Batch Processing. If you want to get features like Real-Time and Graph Processing, you must combine it with other tools.
4) Hadoop MapReduce vs Spark: Fault Tolerance
Apache Spark relies on speculative execution and retries for every task just like Hadoop MapReduce. However, the fact that Hadoop MapReduce relies on hard drives gives it a slight advantage over Apache Spark which relies on RAM.
In case an unforeseen event happens and a Hadoop MapReduce process crashes in the middle of execution, the process may continue where it is left off. This is not possible with Apache Spark since it must start processing from the beginning.
Hence, Hadoop MapReduce is more fault-tolerant than Apache Spark.
5) Hadoop MapReduce vs Spark: Security
Hadoop MapReduce is better than Apache Spark as far as security is concerned. For instance, Apache Spark has security set to “OFF” by default, which can make you vulnerable to attacks. Apache Spark supports authentication for RPC channels via a shared secret. It also supports event logging as a feature, and you can secure Web User Interfaces via Javax Servlet Filters. Additionally, since Apache Spark can run on Yarn and use HDFS features, it can use HDFS File Permissions, Kerberos Authentication, and encryption between nodes.
Hadoop MapReduce can use all Hadoop security features, and it can be integrated with other Hadoop Security Projects.
Hence, Hadoop MapReduce offers better security than Apache Spark.
6) Hadoop MapReduce vs Spark: Scalability
Since Big Data keeps on growing, Cluster sizes should increase in order to maintain throughput expectations. The two platforms, that is, Hadoop MapReduce and Apache Spark, offer scalability through HDFS.
However, Apache Spark uses Random Access Memory (RAM) for optimal performance setup.
7) Hadoop MapReduce vs Spark: Cost
Both Hadoop MapReduce and Apache Spark are Open-source platforms, and they come for free. However, you have to invest in hardware and personnel or outsource the development. This means you will incur the cost of hiring a team that is familiar with the Cluster administration, software and hardware purchases, and maintenance.
As far as cost is concerned, business requirements should guide you on whether to choose Hadoop MapReduce or Apache Spark. If you want to process huge volumes of data, consider using Hadoop MapReduce. The reason is that hard disk space is cheaper than RAM. If you want to perform Real-Time Processing, consider using Apache Spark.
Limitations of Hadoop MapReduce and Apache Spark
The following are the limitations of both Hadoop MapReduce and Apache Spark:
- No Support for Real-time Processing: Hadoop MapReduce is only good for Batch Processing. Apache Spark only supports near Real-Time Processing.
- Requirement of Trained Personnel: The two platforms can only be used by users with technical expertise.
- Cost: You will have to incur the cost of purchasing hardware and software tools as well as hiring trained personnel.
This article provided you with an in-depth understanding of what Hadoop MapReduce and Apache Spark are and listed various factors that drive the Hadoop MapReduce vs Spark decision for Big Data Processing capabilities. It can be concluded that any business should ideally choose Hadoop MapReduce if they are going to be processing large volumes of data but if near Real-Time Data Processing is expected, then Apache Spark would be the preferred choice. Both tools would require high investment to set up engineering teams and purchasing expensive hardware and software tools.
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