Today, data is one of the most crucial assets available to an organization. As organisations generate a vast amount of unstructured data, commonly known as big data, they must find ways to process and use it effectively. As organizations today generate big data at a rapid pace, the need to process it for effective data analysis becomes crucial. While there are several tools that help organisations to process data easily, two of the most popular ones are Hadoop MapReduce and Apache Spark. So which one should organizations invest in? Let’s compare the two on some key parameters.
An open source distributed data infrastructure, Hadoop distributes data across multiple nodes within a cluster of commodity servers. The distributed storage approach is crucial to big data since it allows vast datasets to be stored across innumerable hard drives, thus saving organizations the cost of maintaining a single expensive hardware.
Hadoop’s primary modules are Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop YARN, and Hadoop MapReduce. Hadoop MapReduce can process a large volume of data on a cluster of commodity hardware and in batch mode. Other modules include Cassandra, Avro, Pig, Hive, Oozie etc., all of which support Hadoop’s capacity to process large data sets. Initially designed to deal with crawling and searching billions of web pages, Hadoop is useful to organizations that have large and complex data sets.
Apache Spark is also an open source data-processing tool that operates on the distributed storage framework. It offers faster computation and covers a wide range of workloads. However, it doesn’t offer its own distributed data storage system and needs a third party to provide the file system for analysis. A cluster-computing framework, Spark is often in close competition with MapReduce. Perhaps the greatest difference between Spark and MapReduce is that Spark uses Resilient Distributed Datasets (RDDs) whereas MapReduce uses persistent storage.
MapReduce was designed to handle large quantities of archived data and is ideal for batch processing. This is fit for static data such as the average income increase in the past two decades. MapReduce is very efficient in handling a large amount of data because it can break the data up into smaller segments and run it parallel to each other on separate nodes.
Spark can perform operations on memory as well as disk while MapReduce only performs on disk processing. This means that Spark can cache data on memory without having to access the hard drive. This means it can perform more types of processing including batch, stream, and iterative. Spark does both real-time and batch processing, which means one platform can be used instead of splitting tasks across different platforms. This enables users to process data in real-time and get results quickly as well as treat the data several times. Therefore, Spark offers a greater range of options but MapReduce is more effective when handling large amounts of data.
Spark requires a lot of memory; if it runs with resource-demanding services or if the memory proves to be insufficient for data, there could be major performance issues. On the other hand, MapReduce kills its processes once a task is done so it easily runs in parallel with other services. When it comes to iterative computations on dedicated clusters, Spark has the upper hand but when it comes to ETL-like jobs, MapReduce is the superior alternative.
Both the platforms are based on open source software so the program itself doesn’t cost anything but need manpower and machines.
MapReduce requires a greater number of systems with more disk space but relatively low RAM capacity. Spark, on the other hand, requires a smaller number of devices with standard disk space but a much higher RAM capacity. The capacity needs to be big enough to store the entire data on it. Disk space is a cheaper commodity than RAM space, therefore MapReduce is the cheaper option out of the two.
Because the Spark cluster has high memory needs for optimal performance, Hadoop proves to be the affordable option for processing big data since hard disk space is cheaper than memory. Moreover, there are plenty of Hadoop-as-a-service offerings which don’t need hardware and staff, as opposed to few Spark-as-a-service options.
Spark is equipped with easily manageable APIs such as Scala, Python and Spark SQL. Along with this, it has an interactive mode for receiving immediate responses. Thus, it is considerably easy to write user-defined programmes in Spark. MapReduce is written in Java and is relatively more intricate. There is no interactive mode, but Hive enables a command line interface through which the user can issue commands in successive lines of text. So while Spark is easier to program, MapReduce’s many tools make programming easier.
Hadoop has its own file storage system called Hadoop Distributed File System (HDFS). HDFS takes in data, breaks it into segments, and stores it in the various nodes in the system. HDFS supports MapReduce effectively along with the other tools in the Hadoop ecosystem to ensure that they can all be integrated easily. Spark doesn’t have a file storage system built specifically for it, but it can be easily incorporated into HDFS or any other cloud storage system.
MapReduce uses HDFS which is a highly reliable system for fault tolerance. It replicates data and stores it on individual nodes on separate servers to ensure a copy is available in the event of a crash. Hadoop also supports Kerberos authentication along with traditional Access Control Lists (ACL), making it a very secure platform.
Spark uses Resilient Distributed Datasets (RDD) which retraces the code if there is any fault or crash detected. It also uses replication similar to HDFS, but it isn’t as reliable. The security features of Spark are a major drawback as it offers only shared secret authentication. If it is used on a third-party cloud, then it relies on the security features offered by it.
Spark is excellent on performance and iterative processing and is cost-effective. It’s also compatible with Hadoop’s data sources and file formats, offering graph processing and machine-learning capabilities. On the other hand, MapReduce is a more mature platform designed for batch processing. When it comes to big data, it is a more cost-effective option than Spark. Furthermore, the Hadoop ecosystem is more extensive with numerous supporting projects, tools, and services. Ultimately, deciding between the two platforms comes down to the needs of your organisation.