There are various approaches in the world of big data which make it even more complex yet interesting. As Big Data has matured over the past few years, so has the need for different approaches to manipulate and process data and its vastness. Hadoop MapReduce and Apache Spark are two such approaches.
Spark and Hadoop are popular Apache projects in the big data ecosystem. Spark is an improvement on the original Hadoop MapReduce component and proves advantageous in interactive data interrogation on in-memory datasets and multi-pass iterative machine learning algorithms.
Apache Spark has garnered a lot of excitement around itself since its launch. There is a hot debate going on about Spark vs Hadoop and on whether spark can mount a challenge to Hadoop and become the top Big Data analytics tool. However, before we dig a little deep into this Spark vs Hadoop debate, let’s define each of them.
Hadoop is a distributed data infrastructure. It distributes massive data collections across multiple nodes within a cluster of commodity servers. Distributed storage is fundamental to most of the Big Data projects as it allows vast datasets to be stored across an almost infinite number of everyday computer hard drives, saving the cost of maintaining an expensive custom hardware to hold it all on one device.
Hadoop consists several modules that work together to create the Hadoop framework. The primary modules comprising the Hadoop framework are:
- Hadoop Common
- Hadoop Distributed File System (HDFS)
- Hadoop YARN
- Hadoop MapReduce
In addition to the above four modules that comprise Hadoop’s core, there are several other modules such as Cassandra, Ambari, Avro, Pig, Hive, Oozie, Flume, and Sqoop. These modules further amplify Hadoop’s capacity to process large data sets. Hadoop has become the de facto standard for big data applications.
Hadoop was initially designed to deal with crawling and searching billions of web pages and collecting information into a database. It is useful for organizations that have extremely large and complex data sets.
A spark is a data-processing tool and it operates on distributed data collections. It does not provide its own distributed data storage system. Therefore it requires a third party to provide the file system for analysis.
Spark is a standalone solution in itself. However, it can also run in Hadoop clusters through YARN. This makes it tricky to compare and contrast Spark with Hadoop. Spark is basically a cluster-computing framework, which means that it competes more with MapReduce than with the entire Hadoop ecosystem.
Spark is known for in-memory processing while it can also use disks for processing. On the other hand, Hadoop MapReduce is strictly disk-based. The major difference between Spark and MapReduce is that Spark uses Resilient Distributed Datasets (RDDs) whereas MapReduce uses persistent storage.
Comparing Hadoop MapReduce and Spark
There is an increasing curiosity among Big Data professionals to choose the best framework between Apache Spark and Hadoop, often mistaking them to be the same. However, with considerable similarities, Hadoop and Spark are not directly comparable products. Hadoop and Spark are both Big Data frameworks but they do not necessarily perform exactly the same task. Neither are they exclusive of each other.
Several Big Data projects require installing Spark on top of Hadoop so that the advanced analytics applications of Spark can work on the data stored by Hadoop Distributed File System (HDFS). However, Hadoop and Spark can also work without the other.
In addition to HDFS, Hadoop also comes with a processing component called MapReduce to get the data-processing done. On a similar note, Spark can also be integrated into any cloud-based data platform, besides HDFS, whose data can be used for its analytics function.
To shed some more light on the Spark vs Hadoop debate, let’s take a look at each of them separately.
Spark vs Hadoop MapReduce: In Terms of Performance
Hadoop MapReduce: MapReduce writes all of the data back to the physical storage medium after each operation. This was initially done to ensure a full failure recovery, as electronically held data is more volatile than that stored on disks.
Spark: Spark is 100 times speedier than Hadoop when it comes to processing data. Spark performs most of its operations in memory. It copies data from the distributed physical storage into the logical RAM memory. This reduces the amount of time consumed in writing and reading data to and from the mechanical hard drives that need to be done under Hadoop’s MapReduce system.
Spark vs Hadoop MapReduce: Ease of Use
Hadoop MapReduce: Hadoop MapReduce is written in Java, which is comparatively difficult to program. Although Pig makes it easier, it mandates some effort to learn the syntax. However, some of the Hadoop tools are capable of running MapReduce tasks without any programming. MapReduce does not have an interactive mode as such, although Hive includes a command line interface.
Spark: Spark possesses comfortable APIs for Java, Scala, and Python, and includes Spark SQL for the SQL-savvy users. It is comparatively easy to write user-defined functions in Spark. Spark also comes with an interactive mode for running commands with immediate feedback.
When it comes to installation and maintenance, Spark is not bound to Hadoop. However, both Spark and Hadoop MapReduce are included in distributions by Cloudera (CDH 5) and Hortonworks (HDP 2.2).
Spark vs Hadoop MapReduce: Resilience or Failure Recovery
Both Spark and Hadoop MapReduce have good fault tolerance ability, but Hadoop MapReduce seems to be a little more tolerant than Spark.
Hadoop MapReduce: Hadoop is naturally resilient to system faults or failures as data are written to disk after every operation.
Spark: Spark has built-in resiliency by virtue of the fact that it arranges data in Resilient Distributed Datasets, distributed across the data clusters, which can be recovered following failure.
Spark vs Hadoop MapReduce: Data Processing
Hadoop MapReduce: Hadoop MapReduce is an excellent batch processing engine. It follows sequential steps. MapReduce performs batch processing by reading data from the cluster, performing its operation on the data, writing the results back to the cluster, reading updated data from the cluster, performing the next data operation, writing those results back to the cluster and so on.
Spark: Spark also performs operations similar to Hadoop MapReduce, but it performs all the tasks in one step and in memory. It reads data from the cluster, performs its operation on the data, and then writes it back to the cluster. Additionally, Spark includes its own graph computation library, namely GraphX. GraphX enables users to view data as graphs and collections. It also allows users to transform and join graphs with Resilient Distributed Datasets (RDDs).
Spark vs Hadoop MapReduce: Cost
Both Hadoop MapReduce and Spark are Apache projects and they are free and open source software products. While both the software incur costs as such, there are costs associated with running either platform in personnel and in hardware. Both Spark and Hadoop MapReduce are designed to run on commodity hardware, like low-cost white box server systems.
Hadoop MapReduce: MapReduce uses definite amounts of memory as its processing is disk-based. Therefore, it requires organizations running MapReduce to purchase faster disks and a lot of disk space. MapReduce also needs more systems to distribute the disk I/O over multiple systems.
Spark: Spark, on the other hand, requires a lot of memory but can be managed with a standard amount of disk that runs at standard speed. However, Spark systems incur more cost because of the large amounts of RAM required to run everything in memory. But, Spark reduces the number of systems required which may reduce costs per unit of computation in the long run.
Apache Spark Edging Over Hadoop MapReduce
Spark’s real-time stream processing functionality and machine learning capabilities are way ahead of Hadoop. Spark can process and analyze data the moment it is captured, and insights are immediately fed back to the user through a dashboard, to allow an action to be taken. This feature is increasingly luring the Big Data industry. Spark also has its own machine learning libraries, called MLib, whereas Hadoop systems need to be interfaced with a third-party machine learning library like Apache Mahout.
Open Source, No Competition
Although the existence of two Big Data frameworks is often seen as a battle for dominance of the one over the other, it is important to understand that both of them are non-commercial, open source products, lying beyond the competition bandwagon. They do have some crossover of functions but both of these free-to-use systems are often offered by the same corporate entities to allow users to pick the functionality depending on the requirement of their projects.
In fact, Spark and MapReduce have a symbiotic relationship with each other. Hadoop provides features that Spark does not have, such as a distributed file system while Spark offers real-time, in-memory processing for those data sets that require it. Therefore, the perfect big data scenario is intended for Hadoop and Spark to work together on the same team.
If you are still confused by the question – which one to choose between Hadoop MapReduce and Apache Spark – then the answer is simple – it all depends on the user-based case.