Spark vs. Hadoop MapReduce: Which big data framework to choose

Announcement: Apache Spark and Hadoop MapReduce are popular and efficient tools for processing big data. However, they differ considerably in their technical characteristics and capabilities. In this review, you’ll get to know the difference between the two products and learn to select the one that fits best your particular needs.

Big data has become omnipresent. Each year, it penetrates new spheres of our life and we need more powerful and credible solutions to process and analyze it. Hadoop MapReduce and Apache Spark were both built to work with massive databases, process information at an impressive velocity, and find efficient solutions to diverse problems. However, these projects differ significantly in their technical characteristics and opportunities. The review below will explain the distinction between the two products and help prospective customers to decide which one suits best for their particular purposes.

Goals and Benefits of Both Products

Hadoop MapReduce is a software framework designed to create applications whose preliminary aim is to process multiple terabytes of data. The information is simultaneously processed on thousands of massive clusters with a minimized risk of faults. The product saw light in 2004 thanks to two Google staff members and subsequently joined Apache’s Hadoop framework.

The name of this solution derives from the words “Map” plus “Reduce”. The duty of the former is to extract the data and transform it into key-value pairs. The latter сompresses the volume and performs a summary operation over it. Bulky datasets end up being separated in smaller fragments, all of which are processed simultaneously which considerably accelerates the velocity of the system.

Spark is an open-source project that was initially built at UC Berkeley’s AMPLab and released for general use in 2010. It is marketed as “a unified analytics engine for large-scale data processing” and it was supposed to turn into a more advanced and productive alternative for MapReduce.

Key Differences

In a nutshell, Spark is more complicated and costly if juxtaposed to MapReduce. The distinction boils down to the ensuing parameters:

• Data Processing. MapReduce is capable exclusively of batch processing, Spark can also process data in real time.
• Rapidity. If you run Spark on disk, it is 10 times quicker than its counterpart. In memory, it is 100 times quicker. MapReduce is notorious for its latency.
• Spark features an in-built API for machine learning, while MapReduce doesn’t.
• Scheduler. Spark possesses an inbuilt one, while MapReduce relies on an external one.
• Fault tolerance. Spark relies on RDD and data storage models, while MapReduce resorts to replication.
• APIs. Spark features Rich APIs and is thus more user-friendly. MapReduce included Java APIs that are slightly more confusing.
• Code. The code of Spark is concise and easy to debug. The code of MapReduce is more voluminous and tough to deal with.

MapReduce is a data processing engine, while Spark is a data analytics one. The price of the latter is inevitably higher due to the considerable RAM volume. Another weak aspect of Spark is its security that still leaves much to be desired and requires constant improvement. MapReduce is much better protected and thus more reliable.

Nevertheless, certain similarities can be found between them as well. Both work with nearly any file formats and data sources. The scaling of both is limited to a maximum of 1000 nodes with one cluster.

Rational Choice

The affordable and reliable MapReduce is ideal for linear processing of bulky datasets when you are interested only in the final solution and not the intermediary ones. While some consider this product outdated, many modern enterprises keep using it in their daily working process. Forte Group software company, for instance, believes that MapReduce sometimes helps to distribute the resources more reasonably and achieve certain goals with minimal exertion.

If your priority is real-time, quick, interactive data processing, you should stick to Spark. Opt for this product when dealing with iterative jobs, joined datasets, machine learning and graph processing. Currently, Apache Spark enjoys greater popularity than MapReduce and keeps constantly evolving. The demand for Spark is highly likely to increase due to the subsequent spheres and challenges:

• Risk management. Decision-makers and analysts need to calculate all the potential threats and outcomes with great precision to avoid excessive hazards and develop risk-free strategies.
• Industrial big data analysis. This can be treated as a particular case of risk management, but in such instances, the potential problems are connected with machinery breakdowns. Managers need to deploy large-scale smart systems that will analyze sensor readings and timely foresee impending failures.
• Fraud detection. Machine-learning algorithms study historical data and learn to identify the symptoms of potential frauds. If they notice a deviation in a product that is about to hit the shelves, they signalize it immediately.
• Customer segmentation. This is an indispensable tool of sales that contributes to creating a unique customer experience. Certain segments of customers display distinctive traits of behavior. By analyzing these traits and building patterns, business owners can fine-tune their products and target their advertising campaigns more efficiently.

All of this requires real-time processing or at least processing at the highest possible speed. But such forecasts don’t necessarily mean that the sluggish Hadoop will become completely obsolete. Its ecosystems are compatible with Spark, which means in the future hybrid products can appear on the market, featuring the benefits of both approaches.

Conclusion

Apache Spark can be regarded as an advanced and sophisticated version of Hadoop MapReduce. However, these products were built by different teams and develop along with their individual roadmaps. When selecting between these two solutions, please consider the needs and goals of your particular project and decide which will be your priority: the rapidity and productivity of Spark or the more affordable cost and a greater level of security of MapReduce.