When the world is revolving around Big Data, Apache Spark has shown a rapid adoption in enterprise applications in a significant manner across a wide range of industries. Developed by UC Berkeley in 2009, this data processing engine was later donated to Apache and now has become one of the most powerful open source data processing engines in the Big data field.
With its high tech analytics and massive speed, it can handle multiple petabytes of clustered data of more than 8000 nodes at a time. But what is the most significant in Apache Spark that is even powerful to replace Hadoop’s MapReduce?
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The answer is Speed. Yes, Spark can be 100 times faster than Hadoop when it comes to large-scale data processing. But how? Let’s look into the technical aspects of Spark that make it faster in the data processing.
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Bottom-up Structure of Apache Spark
The main two parts of Apache Spark are –
Spark Core – A distributed execution engine. Java, Scala, and Python APIs provide the platform which is required for any ETL application development.
Set of Libraries – It helps in streaming, SQL processing, and machine learning specific algorithm tasks.
The entire structure is designed for bottom-up performance. Most of the data science specific machines learning algorithms are iterative. When a dataset is cached in memory the data processing speeds up automatically. Also, Apache Spark has this in-memory cache property that makes it faster.
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Factors that Make Apache Spark Faster
There are several factors that make Apache Spark so fast, these are mentioned below:
1. In-memory Computation
Spark is meant to be for 64-bit computers that can handle Terabytes of data in RAM. Spark is designed in a way that it transforms data in-memory and not in disk I/O. Hence, it cut off the processing time of read/write cycle to disk and storing intermediate data in-memory. This reduces processing time and the cost of memory at a time. Moreover, Spark supports parallel distributed processing of data, hence almost 100 times faster in memory and 10 times faster on disk.
2. Resilient Distributed Datasets (RDD)
The main abstraction of Apache Spark is Resilient Distributed Datasets (RDD). It is a fundamental data structure of Spark. Spark RDD can be viewed as an immutable distributed collection of objects. These objects can be cached using two methods, either by a cache() or persist().
The beauty of storing RDD in memory using the cache() method is – while storing the value in-memory if the data doesn’t fit it sends the excess data to disk or recalculates it. Basically, it is a logical partitioning of each dataset in RDD which can be computed on different nodes of a cluster. As it is stored in memory, RDD can be extracted whenever required without using the disks. It makes processing faster.
3. Ease of Use
Spark follows a general programming model. This model does not constrain programmers to design their applications into a bunch of maps and reduce operations. The parallel programs of Spark look very similar to sequential programs, which is easy to develop.
Finally, Spark works on a combination of batch, interactive, and streaming jobs in the same application. As a result, a Spark job can be up to 100 times faster and only need 2 to 10 times less code writing.
4. Ability for On-disk Data Sorting
Apache Spark is the largest open-source data processing project. It is fast when stores a large scale of data on disk. Spark has the world record of on-disk data sorting.
5. DAG Execution Engine
DAG or Direct Acrylic Graph allows the user to explore each stage of data processing by expanding the detail of any stage. Through a DAG user can get a stage view which clearly shows the detailed view of RDDs.
Spark has GraphX, which is a graph computation library and provides inbuilt graph support to improve the performance of the machine learning algorithm. Spark uses DAG to perform all required optimization and computation in a single stage rather than going for multiple stages.
6. SCALA in the backend
The core of Apache Spark is developed using SCALA programming language which is faster than JAVA. SCALA provides immutable collections rather than Threads in Java that helps in inbuilt concurrent execution. This is an expressive development APIs for faster performance.
7. Faster System Performance
Due to its cache property, Spark can store data in memory for further iterations. As a result, it enhances the system performance significantly. Spark utilizes Mesos which is a distributed system kernel for caching the intermediate dataset once each iteration is finished.
Furthermore, Spark runs multiple iterations on the cached dataset and since this is in-memory caching, it reduces the I/O. Hence, the algorithms work faster and in a fault-tolerant way.
8. Spark MLlib
Spark provides a built-in library named MLlib which contains machine learning algorithms. This helps in executing the programs in-memory at a faster rate.
9. Pipeline Operation
Following Microsoft’s Dryad paper methodology, Spark utilizes its pipeline technology more innovatively. Unlike Hadoop’s MapReduce, Spark doesn’t store the output fed of data in persistent storage, rather just directly passes the output of an operation as an input of another operation. This significantly reduces the I/O operations time and cost making the overall process faster.
10. JVM Approach
Spark can launch tasks faster using its executor JVM on each data processing node. This makes launching a task just a single millisecond rather than seconds. It just needs making an RPC and adding the Runnable to the thread pool. No jar loading, XML parsing, etc are associated with it. Hence, the overall process is much faster.
11. Lazy Evaluation
Spark utilizes memory holistically. Unless an action method like sum or count is called, Spark will not execute the processing.
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Conclusion
Due to its high performance, there is a surge in the adoption of Spark in Big data industries. Spark is running with Cassandra, with Hadoop, and on Apache Mesos. Although Spark adoption has increased significantly and it may reduce the use of MapReduce due to speed factor, but it’s not about replacing MapReduce completely.
Rather, it is predicted that Spark would facilitate the powerful growth of another stack in Big data arena. Till now Spark doesn’t have any file management system. Hence, until the time a new Spark specific file management system comes into the picture it has to rely on Hadoop’s HDFS (Distributed file system) for data processing. Databrick certification is one of the top Spark certifications, so if you are aspiring to become a Certified Big Data Professional, get ready to achieve one.
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Whizlabs Big Data Certification courses – Spark Developer Certification (HDPCD) and HDP Certified Administrator (HDPCA) are based on the Hortonworks Data Platform, a market giant of Big Data platforms. Whizlabs recognizes that interacting with data and increasing its comprehensibility is the need of the hour and hence, we are proud to launch our Big Data Certifications. We have created state-of-the-art content that should aid data developers and administrators to gain a competitive edge over others.
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I appreciate your work on Spark. It’s such a wonderful read on Spark tutorial. Keep sharing stuffs like this.
Altough some points that you mentioned represents an advantage over MR, not all of them are related with its enhanced speed. MR also has a cache method, YARN executing MR can reuse JVMs to avoid the overload of create a new JVM and lazy evaluation by itself doesnt provide better speed. Spark can run on YARN and Mesos, like MR. I appreciate the post but I think that what you mention are some advantages, in fact, over MR.