In this advent of big data, large volumes of data are being generated in various forms at a very fast rate thanks to more than 50 billion IoT devices and this is only one source. Other sources include social media platforms and business transactions. This data carries insights that need to be unearthed to be useful for any purpose intended for it. For this to happen, there is a need for tools and techniques that can process such complex and large capacity volumes of data to extract meaningful insights that enterprises can take advantage of to rise above the competition.
Hadoop MapReduce and Apache Spark are two popular tools developed for handling big data effectively. Most industries like healthcare, banking, government, telecommunications, and eCommerce sites like Amazon and eBay that are making use of big data processing tools are keen on real-time analytics and this is why Spark which was developed almost a decade after Hadoop’s MapReduce has grown to be preferred over Hadoop. As such, professionals with Spark certification and/or experience have great prospects with more businesses opting to hop onto the Spark bandwagon.
What is MapReduce?
MapReduce is a component of Hadoop along with the Hadoop distributed file system (HDFS) and Hadoop YARN. MapReduce is Hadoop’s processor. It is the component of Hadoop that processes large data sets and is capable of processing data in a parallel and distributed manner. It does this in two steps, Map and Reduce.
- Map phase. During this phase, input data is converted into smaller data sets known as input splits. A mapping function is then applied to each split to generate output values.
- Reduce. Once output values are generated in the mapping phase, they are shuffled and a reduce function applied to them before being stored in the HDFS. The reduce task only takes place after a mapping task is complete.
One of the outstanding advantages of the MapReduce program is its scalability. It scales fast and easily from a few to thousands or tens of thousands of computing nodes in clusters with a simple configuration change. Also, MapReduce programs support various programming languages including Python, Java, Ruby, and C++.
What is Spark?
While Spark is also an open-source distributed and cluster computing framework for big data with a rich API library, it is independent and does not come with an integrated cluster resource manager and storage system like Hadoop in which MapReduce is a component. This gives you the flexibility of choosing a cluster resource manager and storage system that you prefer for instance the Hadoop YARN cluster resource manager, Apache YARN, Azure resource manager, and Hadoop Distributed File System (HDFS), Amazon S3, or Google cloud storage.
Spark supports various programming languages including Java, Python, Scala, and R but also features both batch and streaming modes for graph processing, machine learning, and SQL querying processes thus is a general-purpose framework.
The greatest advantage that Spark offers over Hadoop is that it is built for batch and real-time processing. It also flaunts a 100 times faster batch processing speed for large data sets compared to MapReduce. In essence, Apache Spark was developed in response to the limitations of Hadoop.
MapReduce vs Spark
| MapReduce | Spark | |
| Data processing | Supports only batch processing and will require Apache Mahout installation to perform machine learning processes | Supports both batch, real-time processing, graph processing, iterative processing, streaming, and integrated APIs for machine learning all within a cluster |
| SQL Querying | Supports SQL querying using Hive Query language | Supports SQL querying using Spark SQL |
| Interactive processing | Does not have an interactive mode | Performs interactive data processing |
| Best for | Linear processing of large data sets | Data analytics including interactive, graph, iterative, and real-time data processing |
| Processing speed | MapReduce runs on disk storage (performs reads and writes on the HDFS) which makes it slower because this increases disk latency. | Spark processing speed can go up to 100 times faster than MapReduce because it runs on the internal memory. |
| Scalability | Scalability is capped at 1000 nodes for each cluster | Scalability is capped at 1000 nodes for each cluster |
| Developed in | Java language | Scala language |
| Languages supported | C, C++, Ruby, Python, Perl | Scala, Python, R, and Java |
| Fault tolerance | Applies replication to achieve fault tolerance and is relatively better than Spark | Uses RDD (Resilient Distributed Dataset) to achieve fault tolerance. RDDs are designed to recover the partitions of failed nodes |
| Hardware | Runs on commodity hardware | Runs on mid to high-level hardware |
| Scheduler | Uses external schedulers for its workflows | Being that it runs on RAM, Spark comes with an inbuilt scheduler |
| Ease of operation | Requires core Java coding skills as it is written in Java hence handling the Java APIs can be a bit complex | Spark has a rich API library that makes it easier to operate |
| Cost | Cheaper than Spark | Costly as it requires investment in RAM |
| Security | MapReduce is more secure as it features several security features including Kerberos authentication protocol and access control lists (ACLs) | Spark features only the shared secret password authentication security feature |
| Caching | Does not support caching of data hence a slower processing speed | Supports caching of data in the RAM which enhances processing speed |
| OS compatibility | Can be deployed on various platforms including Windows and Unix platforms like Linux | Can be deployed on various platforms including Windows and Unix platforms like Linux |
Conclusion
Both MapReduce and Apache Spark are both useful and very popular tools for big data storage and processing. While Spark is a more advanced function compared to MapReduce, its security features are yet to evolve to maturity. Still, it is preferred thanks to its flexibility to handle a variety of processes including streaming, machine learning, iterative, batch, and graph processes with impressive speed.
However, Spark and Hadoop complement each other and can be used together for even better performance. This is because Spark supports data sources that implement Hadoop’s distributed file system format and will offer RAM real-time processing in return. This makes both Spark and Hadoop a powerful combination for big data processing.
