When it comes to data processing, two names stand above all else; Hadoop and Spark. The comparison below looks at both tools across different categories, making it easier for you to decide which tool represents a better fit for your needs:
A Brief Introduction
Before evaluating which of the two services are better for your project and company, it is helpful to have a little insight into how both of these started off. Hadoop began as a Yahoo! Project back in 2006. Through consistent success, it soon became a top-level Apache open-source project later on.
It's a general-purpose form of distributed processing that is based on several components. These include the Hadoop Distributed File System (HDFS), which stores all files in a Hadoop-native format across the cluster; YARN, a schedule that coordinates project runtimes; and finally, MapReduce, an algorithm meant to process data in parallel. Since Hadoop is built in Java, it can be accessed via all major languages such as Python, C, C#, and Mathematica.
On the other hand, Spark is a relatively newer entry into the market. It was first introduced in the market in 2012 at UC Berkeley's AMPLab. It is also a top-level Apache project focusing on data processing. The most significant difference between the two is that Spark can work in-memory. Thanks to a process known as Resilient Distributed Dataset (RDD), it can process data in RAM while using the Mesos protocol for scheduling purposes.
All files that pass through HDFS are split into blocks. Each of these blocks is replicated across the cluster based on both their size and replication factor. This information is simultaneously fed into the NameNode. Thanks to High availability being implemented in 2012, NameNode can now failover into a backup Node to track the entire cluster.
MapReduce, consisting of a JobTracker, sits atop HDFS along with a JobTracker. Once written, Hadoop accepts the JobTracker and allocates work instantaneously while storing the data in the Hive warehouse. Finally, YARM allocates the resources JobTracker requests, ensuring the most efficient results. The entire data is then compiled and aggregately written on the disk in HDFS.
As mentioned earlier, Spark uses almost the same methodology, except carrying out the computations in memory and keeping them stored there. Spark starts by reading from a file in HDFS, S3, or any other major filestore. Separately, Spark creates the RDD that can be run parallel.
Once this is done, Spark also creates a Directed Acyclic Graph (DAG) to visualize the operations and their inter-relations. Once this action is done, a new sub-action called DataFrames initiates. Similar to Python, DataFrames can organize the data better. Dusan Stanar, CEO of VSS Monitoring says “Hadoop is used mainly for disk-heavy operations with the MapReduce paradigm, whereas, Spark is a more flexible, but more expensive in-memory processing architecture”
Jake Smith, Managing Director at Absolute Reg says “Spark is unquestionably the better of the two when it comes down to performance. It can process and sort nearly 100 TB of data 3 times faster than Hadoop's MapReduce”. This performance is further exacerbated to 100 times faster when it's done in memory.
As if that weren't enough, Spark runs even better when used on Machine Learning applications such as k-means and Naïve Bayes.
However, if you plan on using Sparks using YARN with other shared devices, you'll see a noticeable downgrade in performance. This can lead to eventual memory leaks via the RAM. In such a case of batch processing, Hadoop will prove to be a more efficient system. Cost Since they're both available as open-source Apache projects, they're technically free. This means zero installation charges. However, it does not mean you won't have to incur any costs at all. Both of these will come with an extraordinary amount of maintenance, hardware, software, and human resource expenditures.
Daniel Foley, CEO of Daniel Foley says “Hadoop will require more memory in the disk as a general rule of thumb, while Spark needs more RAM” In layman's terms, this means setting up Spark to use could end up being far more expensive, even though the technology is newer and more efficient. This is generally because the individuals who have a working knowledge of operating it effectively are rare and command a better demand in the market.
If you're looking to avoid paying such fees, you can always opt to outsource these to external vendors. The most popular choices in the market include Cloudera for Hadoop and DataBricks for Spark. Alternatively, going with Amazon Web Services and running EMR/MapReduce processes on the cloud is also an option worth looking into.
As far as hardware is concerned, you'll need specialized machines to operate both of them properly. A properly optimized cluster for Hadoop will cost you $0.026 per hour for the smaller instance, i.e., c4.large. Comparatively, the same arrangement for Spark will cost you approximately $0.067 per hour.
Using Hadoop & Spark Combined
While it is rare, you may find yourself in a situation where you will have to use both tools simultaneously in tandem. While the verdict is still out on whether Spark can replace Hadoop, there is far less debate on them being perfect complements of one another when used together. Spark's processing power and Hadoop's architecture are in harmony and can deliver the best results in the end.
If you're looking to conduct stream analysis or batch analysis, you'll be well advised to use both the tools together. Stewart Dunlop, Chief Founder of AirSoftPal says “Hadoop can help you deal with heavier operations because of its lower prices, while Spark's processing prowess can help you deliver the best-optimized results for instantaneous processes”
Since both processes use YARN, it'll be easier for you to archive and analyze the archived results.
There's no one-for-all answer here. Both Hadoop and Spark are the best data processing systems available on the market today. Each has its benefits, as well as flaws. Hadoop is better for disk-heavy operations thanks to its MapReduce paradigm, while Spark excels as the better value-proposition thanks to its more flexible processing architecture.
The best advice is to study your requirements independently and adequately evaluate which program can deliver your needs' best results.
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