When the first release of Spark became available in 2014, Hadoop had already enjoyed several years of growth since 2009 onwards in the commercial space. Although Hadoop solved a major hurdle in analyzing large terabyte-scale datasets efficiently, using distributed computing methods that were broadly accessible, it still had shortfalls that hindered its wider acceptance.
Limitations of Hadoop
A few of the common limitations with Hadoop were as follows:
I/O Bound operations:
Due to the reliance on local disk storage for saving and retrieving data, any operation performed in Hadoop incurred an I/O overhead. The problem became more acute in cases of larger datasets that involved thousands of blocks of data across hundreds of servers.
To be fair, the ability to co-ordinate concurrent I/O operations (via HDFS) formed the foundation of distributed computing in Hadoop world. However, leveraging the capability and tuning the Hadoop cluster in an efficient manner across different use cases and datasets required an immense and perhaps disproportionate level of expertise.
Consequently, the I/O bound nature of workloads became a deterrent factor for using Hadoop against extremely large datasets. As an example, machine learning use cases that required hundreds of iterative operations meant that the system would incur an I/O overhead for each pass of the iteration.
MapReduce programming (MR) Model:
All operations in Hadoop require expressing problems in terms of the MapReduce Programming Model – namely, the user would have to express the problem in terms of key-value pairs where each pair can be independently computed. In Hadoop, coding efficient MapReduce programs, mainly in Java, was non-trivial, especially for those new to Java or to Hadoop (or both).
Non-MR Use Cases:
Due to the reliance on MapReduce, other more common and simpler concepts such as filters, joins, and so on would have to also be expressed in terms of a MapReduce program. Thus, a join across two files across a primary key would have to adopt a key-value pair approach. This meant that operations, both simple and complex, were hard to achieve without significant programming efforts.
The use of Java as the central programming language across Hadoop meant that to be able to properly administer and use Hadoop, developers had to have a strong knowledge of Java and related topics such as JVM tuning, Garbage Collection, and others. This also meant that developers in other popular languages such as R, Python, and Scala had very little recourse for re-using or at least implementing their solution in the language they know best.
On the whole, even though the Hadoop world had championed the Big Data revolution, it fell short of being able to democratize the use of the technology for Big Data on a broad scale.
The team at AMPLab recognized these shortcomings early on, and set about creating Spark to address these and, in the process, hopefully develop a new, superior alternative.
Overcoming the limitations of Hadoop with Spark
We’ll now look at some of the limitations discussed in the earlier section and understand how Spark addresses these areas, by virtue of which it provides a superior alternative to the Hadoop ecosystem.
A key difference to bear in mind at the onset is that Spark does NOT need Hadoop in order to operate. In fact, the underlying backend from which Spark accesses data can be technologies such as HBase, Hive and Cassandra in addition to HDFS.
This means that organizations that wish to leverage a standalone Spark system can do so without building a separate Hadoop infrastructure if one does not already exist.
The Spark solutions are as follows:
I/O Bound operations:
Unlike Hadoop, Spark can store and access data stored in memory, namely RAM – which, as discussed earlier, is 1,000+ times faster than reading data from a disk. With the emergence of SSD drives, the standard in today’s enterprise systems, the difference has gone down significantly. Recent NVMe drives can deliver up to 3-5 GB (Gigabytes) of bandwidth per second.
Nevertheless, RAM, which averages about 25-30 GB per second in read speed, is still 5-10x faster compared to reading from the newer storage technologies. As a result, being able to store data in RAM provides a 5x or more improvement to the time it takes to read data for Spark operations. This is a significant improvement over the Hadoop operating model which relies on disk read for all operations. In particular, tasks that involve iterative operations as in machine learning benefit immensely from the Spark’s facility to store and read data from memory.
MapReduce programming (MR) Model:
While MapReduce is the primary programming model through which users can benefit from a Hadoop platform, Spark does not have the same requirement. This is particularly helpful for more complex use cases such as quantitative analysis involving calculations that cannot be easily parallelized, such as machine learning algorithms. By decoupling the programming model from the platform, Spark allows users to write and execute code written in various languages without forcing any specific programming model as a prerequisite.
Non-MR use cases:
Spark SQL, Spark Streaming and other components of the Spark ecosystem provide a rich set of functionalities that allow users to perform common tasks such as SQL joins, aggregations, and related database-like operations without having to leverage other, external solutions. Spark SQL queries are generally executed against data stored in Hive (JSON is another option), and the functionality is also available in other Spark APIs such as R and Python.
The most commonly used APIs in Spark are Python, Scala and Java. For R programmers, there is a separate package called SparkR that permits direct access to Spark data from R. This is a major differentiating factor between Hadoop and Spark, and by exposing APIs in these languages, Spark becomes immediately accessible to a much larger community of developers. In Data Science and Analytics, Python and R are the most prominent languages of choice, and hence, any Python or R programmer can leverage Spark with a much simpler learning curve relative to Hadoop. In addition, Spark also includes an interactive shell for ad-hoc analysis.
You enjoyed an excerpt from the book, Practical Big Data Analytics, by Nataraj Dasgupta and published by Packt Publishing. Check out the book to master your organizational Big Data using the power of data science and analytics.