Since its early beginnings some 10 years ago, the MapReduce Hadoop implementation has become the go to enterprise grade solution for storing, managing and processing massively large data volumes. Today, as organizations face the growing need for real-time data analysis to achieve competitive advantage, a new open source Hadoop data processing engine, Apache Spark, has entered the arena. For organizations looking to adopt a big data analytics functionality, here’s a comparative look at Apache Spark vs. MapReduce.
MapReduce is the massively scalable, parallel processing framework that comprises the core of Apache Hadoop 2.0, in conjunction with HDFS and YARN.
In this conventional Hadoop environment, data storage and computation both reside on the same physical nodes within the cluster. By leveraging this proximity of data, MapReduce is capable of efficiently processing massive volumes of both structured and unstructured data.
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With regards to unstructured data, MapReduce Hadoop has the fundamental flexibility to handle unstructured data regardless of the data source or native format. Additionally, disparate types of data stored on unrelated systems can all be deposited in the Hadoop cluster without the need to predetermine how the data will be queried.
MapReduce Hadoop is designed to run batch jobs that address every file in the system. Since that process takes time, MapReduce is well suited for large distributed data processing where fast performance is not an issue, such as running end-of day transactional reports. MapReduce is also ideal for scanning historical data and performing analytics where a short time-to-insight isn’t vital.
Another plus for businesses challenged by increasing data demands is MapReduce Hadoop’s scalable infrastructure. Scalable architecture allows servers to be added on demand in order to handle growing workloads.
The functions and capabilities of MapReduce Hadoop make it ideal for a number of real-world big data applications. However, as data continues to explode in volume, variety and velocity, the one area in which MapReduce, with its high-latency batch model, falls short is real-time data analysis.
Developed in 2009 in UC Berkeley’s AMPLab and open sourced in 2010, Apache Spark, unlike MapReduce, is all about performing sophisticated analytics at lightning fast speed. According to stats on Apache.org, Spark can “run programs up to 100 times faster than Hadoop MapReduce in memory, or 10 times faster on disk.”
Spark was purposely designed to support in-memory processing. The net benefit of keeping everything in memory is the ability to perform iterative computations at blazing fast speeds—something MapReduce is not designed to do. Plus, Spark permits programmers and developers to write applications in Java, Python or Scala and to build parallel applications designed to take full and fast advantage of a distributed environment.
Along with supporting simple “map” and “reduce” operations, Spark supports SQL queries, streaming data, and complex analytics such as graph algorithms and machine learning. Since Spark runs on existing Hadoop clusters and is compatible with HDFS, HBase and any Hadoop storage system, users can combine all capabilities into a single workflow while accessing and processing all data in the current Hadoop environment.
Unlike MapReduce, Spark is designed for advanced, real-time analytics and has the framework and tools to deliver when shorter time-to-insight is critical. Included in Spark’s integrated framework are the Machine Learning Library (MLlib), the graph engine GraphX, the Spark Streaming analytics engine, and the real-time analytics tool, Shark. With this all-in-one platform, Spark is said to deliver greater consistency in product results across various types of analysis.
Over the years, MapReduce Hadoop has enjoyed widespread adoption in the enterprise, and that will continue to be the case. Going forward, as the need for advanced real-time analytics tools escalates, Spark is positioned to meet that challenge.
Among Spark’s quickest adopters will most likely be those companies that have already implemented conventional Hadoop and are now looking to gain even greater insights and competitive advantage from volumes of valuable data.