Both technologies are equipped with amazing features. However, with the increased need of real-time analytics, these two are giving tough competition to each other. Read a comparative analysis of Hadoop MapReduce and Apache Spark.
Big data is everywhere. Wait till 2020 and you will have over 50 billion Internet-connected devices, thanks to Internet of Things (IoT). All this relates to one thing—data is on a scale that is unprecedented in the history of humankind. For instance, 90 percent of the data that is in existence today was created in the last two years alone.
All this means that there needs to be a radical new way to handle all that data, process it in hitherto unheard volumes, and derive meaningful insights from it to help businesses leap forward in this cut-throat corporate scenario. This is where the argument comes into the picture: whether Apache MapReduce has run its course and is being taken over by a nimbler rival technology, Apache Spark.
Some of the interesting facts about these two technologies are as follows:
- Spark Machine Learning abilities are obtained by MLlib.
- Apache Spark can be embedded in any OS.
- Execution of a Map task is followed by a Reduce task to produce the final output.
- Output from the Map task is written to a local disk, while the output from the Reduce task is written to HDFS.
Spark Vs. MapReduce
Check out the detailed comparison between these two technologies.
|Key Features||Apache Spark||Hadoop MapReduce|
|Speed||10–100 times faster than MapReduce||Slower|
|Analytics||Supports streaming, Machine Learning, complex analytics, etc.||Comprises simple Map and Reduce tasks|
|Suitable for||Real-time streaming||Batch processing|
|Coding||Lesser lines of code||More lines of code|
|Processing Location||In-memory||Local disk|
What Are MapReduce and Spark?
The above table clearly points out that Apache Spark is way better than Hadoop MapReduce or, in other words, more suitable for the real-time analytics. However, it would be interesting to know what makes Spark better than MapReduce. But, before that you should know what exactly these technologies are. Read below:
MapReduce is a methodology for processing huge amounts of data in a parallel and distributed setting. Two tasks undertaken in the MapReduce programming are the Mapper and the Reducer. Mapper takes up the job of sorting data that is available, and Reducer is entrusted with the task of combining the data and converting it into smaller chunks. MapReduce, HDFS, and YARN are the three important components of Hadoop systems.
Spark is a new and rapidly growing open-source technology that works well on cluster of computer nodes. Speed is one of the hallmarks of Apache Spark. Developers working in this environment get an application programming interface that is based on the framework of RDD (Resilient Distributed Dataset). RDD is nothing but the abstraction provided by Spark that lets you segregate nodes into smaller divisions on the cluster in order to independently process data.
What Makes MapReduce Lag Behind in the Race
So far, you must have perceived a clear picture of Spark and MapReduce workflows. It is clear that MapReduce is not suitable according to the evolving real-time big data needs. Following are the reasons behind this fact:
- Response time today has to be super fast.
- There are scenarios when the data from the graph has to be extracted.
- Sometimes, mapping generates a lot of keys which take time to sort.
- There are times when diverse sets of data need to be combined.
- When there is Machine Learning involved, then this technology fails.
- For repeated processing of data, it takes too much for the iterations.
- For tasks that have to be cascaded, there are a lot of inefficiencies involved.
Some of the benefits of Apache Spark over Hadoop MapReduce are given below:
- Processing at high speeds: The process of Spark execution can be up to 100 times faster due to its inherent ability to exploit the memory rather than using the disk storage. MapReduce has a big drawback since it has to operate with the entire set of data in the Hadoop Distributed File System on the completion of each task, which increases the time and cost of processing data.
- Powerful caching: When dealing with Big Data, there is a lot of caching involved and this increases the workload while using MapReduce, but Spark does it in-memory.
- Increased iteration cycles: There is a need to work on the same data again and again, especially in Machine Learning scenarios, and Spark is perfectly suitable for such applications.
- Multiple operations using in-built libraries: MapReduce is capable of using in-built libraries for batch processing tasks. Whereas, Spark provides the option of utilizing the in-built libraries to build interactive queries in SQL, Machine Learning, streaming, and batch processing, among other things.
Some Other Obvious Benefits of Spark over MapReduce
Spark is not tied to Hadoop unlike MapReduce which cannot work outside of Hadoop. So, there are talks going around with subject matter experts claiming that Spark might one day even phase out Hadoop, but there is still a long way ahead. Spark lets you write an application in a language of your choice like Java, Python, and so on. It supports streaming data and SQL queries and an extensive use of data analytics in order to make sense of the data, and it might even support the machine-led learning like the IBM Watson cognitive computing technology.
Spark is able to access diverse data sources and make sense of them all. This is especially important in a world where IoT is gaining a steady groundswell and machine-to-machine communications amount for a bulk of data. This also means that MapReduce is not up to the challenge to take on the Big Data exigencies of the future.
In the race to achieve the fastest way of doing things, using the least amount of resources, there will always be a clash of the Titans. The future belongs to those technologies that are nimble, adaptable, resourceful, and most of all that which can cater to the diverse needs of enterprises without a hitch, and Apache Spark seems to be ticking all the checkboxes and possibly the future belongs to it.
Apache Spark Use Cases
Known as one of the fastest Big Data processing engine, Apache Spark is widely used across organizations in myriad of ways.
Apache Spark has gained immense popularity over the years and is being implemented by many competing companies across the world. Many organizations such as eBay, Yahoo, and Amazon are running this technology on their big data clusters.
Spark, the utmost lively Apache project at the moment across the world with a flourishing open-source community known for its ‘lightning-fast cluster computing,’ has surpassed Hadoop by running with 100 times faster speed in memory and 10 times faster speed in disks.
Spark has originated as one of the strongest Big Data technologies in a very short span of time as it is an open-source substitute to MapReduce associated to build and run fast and secure apps on Hadoop. Spark comes with a Machine Learning library, graph algorithms, and real-time streaming and SQL app, through Spark Streaming and Shark, respectively.
For instance, a simple program for printing ‘Hello World!’ requires more lines of code in MapReduce but much lesser in Spark. Here’s the example:
.flatmap(line => line.split(“ “))
Further Use Cases of Apache Spark
For every new arrival of technology, the innovation done should be clear for the test cases in the marketplace. There must be proper approach and analysis on how the new product would hit the market and at what time it should with fewer alternatives.
Now when you think about Spark, you should know why it is deployed, where it would stand in the crowded marketplace, and whether it would be able to differentiate itself from its competitors?
With these questions in mind, go on with the chief deployment modules that illustrate the uses cases of Apache Spark.
Apache Spark is easy to use and brings up a language-integrated API to stream processing. It is also fault-tolerant, i.e., it helps semantics without extra work and recovers data easily.
This technology is used to process the streaming data. Spark streaming has the potential to handle additional workloads. Among all, the common ways used in businesses are:
- Streaming ETL
- Data enrichment
- Trigger event detection
- Complex session analysis
- Spark provides an easy way to study APIs, and also it is a strong tool for interactive data analysis. It is available in Python or Scala.
- MapReduce is made to handle batch processing and SQL on Hadoop engines which are usually considered to be slow. Hence, with Spark, it is fast to perform any identification queries against live data without sampling.
- Structured streaming is also a new feature that helps in web analytics by allowing customers to run a user-friendly query with web visitors.
- Fog computing runs a program 100 times faster in memory and 10 times faster in the disk than Hadoop. It helps write apps quickly in Java, Scala, Python, and R.
- It includes SQL, streaming, and hard analytics and can run anywhere (standalone/cloud, etc.).
- With the rise of Big Data Analytics, the concept that arises is IoT (Internet of Things). IoT implants objects and devices with small sensors that interact with each other, and users are making use of it in a revolutionary way.
- It is a decentralized computing infrastructure where data, compute, storage, and applications are located, somewhere between the data source and the cloud. It brings the advantages of the cloud closer to where data is created and acted upon, more or less the way edge computing does it.
To summarize, Apache Spark helps calculate the processing of large amount of real-time or archived data, both structured and unstructured, without anything being held or attached. It’s linking appropriate complex possibilities similar to graph algorithms and Machine Learning. Spark brings processing of Big Data to a large quantity.
In real time, Apache Spark is used in many notable business industries such as Uber, Pinterest, etc. These companies gather terabytes of event data from users and engage them in real-time interactions such as video streaming and many other user interfaces, thus, maintaining constant smooth and high-quality customer experience.