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Hadoop vs. Spark: Which cluster computing framework can take care of your data analytics needs better?
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Hadoop vs. Spark comparisons still spark debates on the web, and there are solid arguments to be made regarding the utility of both platforms.
For about a decade now, Apache Hadoop, the first prominent distributed computing platform, has been known to provide a robust resource negotiator, a distributed file system, and a scalable programming environment, MapReduce. The platform has helped abstract away the hardships of fault tolerance and computation distribution and thus has drastically lowered the entry barrier into the Big Data space.
Soon after it had gone big, Apache Spark came along – improving on the design and usability of its predecessor – and rapidly became the de-facto standard tool for large-scale data analytics.
In this article, we go over the properties of Hadoop’s MapReduce and Spark and explain, in general terms, how distributed data processing platforms for cloud computing and their various machine learning models and learning algorithms have evolved.
As the name suggests, MapReduce was born conceptually out of the Map and Reduce functions in functional programming languages. The map part creates value pairs or intermediate keys for incoming inputs, while reduce accepts these intermediate data keys and merges them to build potentially smaller sets of values.
This is how it works
The inputs are divided into chunks by the splitter function. Then, map (filtering and sorting inputs values by queues) and reduce (a summary operation such as determining the overall number of elements in each queue) jobs are created. There’s also the master’s role in the memory processing engine, ensuring that jobs are appropriately scheduled, and all failed tasks are re-executed. After the map and reduced workers are done, stream processing is all finished, and the master will call the user.
In Hadoop, a dedicated module, YARN is responsible for job allocation data management; Java is the core programming language, and Hadoop Distributed File System [HDFS] is where the data is kept data stored.
Since the inputs are split into chunks, independent of one another, graph processing and map jobs are performed in paralle. The framework helps write applications that operate on massive, large volumes of data.
Hadoop MapReduce, before any other tool, has made available parallel processing of large datasets on clusters of commodity hardware, while also ensuring reliability and fault tolerance for distributed processing.
Hadoop can’t be used as a database substitute. At the same time, structured data can be stored, and the platform doesn’t automatically index data objects. To find something, a programmer must generate the indices and analyze data first.
MapReduce, which forces each step in the data processing engine to workflow into a map and reduce phase, could be more efficient for multi-pass computations. That each use case must be converted into the MapReduce pattern in-memory data processing itself limits the platform’s throughput to a large extent.
Another drawback is the necessity to store data – a processing step’s output – into HDFS before the next step archived to process data, can take place. This creates disk, data storage, and replication issues which might result in overheads.
The system’s sequential nature of streaming data and processing means programmers must create many Map and Reduce jobs, which the platform can only run in order.
Debugging is also challenging in Hadoop as the platform’s code tends to be verbose. It’s been stated by multiple researchers that expert knowledge is required to implement MapReduce effectively in real-world applications.
Hadoop vs. Spark
Apache Spark is a fast, easy-to-use, powerful, and general engine for big data processing tasks.
Consisting of six components – Core, SQL, Streaming, MLlib, GraphX, and Scheduler – it is less cumbersome than Hadoop modules. It also provides 80 high-level operators that enable users to write code for applications faster. Additionally, data engineers often cite wide optimization opportunities in terms of arbitrary operator graph execution and the ability to streamline processing processes data flows via lazy evaluation of data queues as Spark’s most notable benefits for data scientists.
Spark lends itself well to integration with the Hadoop ecosystem and various databases; it has APIs in Java, Python, and Scala, and provides interactive shells from Python and Scala. It utilizes directed acyclic graph architectures and supports in-memory data sharing across the graphs which allows several jobs to operate on the same data simultaneously.
Unlike Hadoop, which has disk-based processing, Spark is an in-memory engine. It only utilizes disk space when the batch-processing data can no longer fit into memory.
The system’s distinctive property is its unique programming abstraction RDD (resilient distributed datasets), a fault-tolerant collection of objects that can be computed in parallel. After the initial dataset has been turned into a series of RDDs, two types of operations can be performed next:
- Transformations such as map, join, filter, etc. These create new RDDs containing the results of said operations, and they are only executed when a specific action – such as a data volume writing intermediate data as storage into HDFS – needs to consume the data.
- Actions such as counting, reducing, and returning specific values.
- Due to RDD transformations only being run when actions are called, the platform can operate more efficiently.
While in MadReduce, a Java class is always the driver, in Spark, a driver could be any program utilizing the core API and potentially other, more specialized APIs. An interpreter, too, can be the driver, which is immensely useful in the exploration or debugging context.
Though the high-level APIs might make Spark applications seem quite intuitive, the system internals, such as when data shuffling takes real-time data processing into place real time processing, must be understood well when trying to build scalable processing software.
To achieve fault-tolerance, Spark utilizes a streaming extension to its core API, which divides (micro-batches) live data streams into RDD sequences called discretized streams (DStreams). The core API, additional Spark streaming APIs such as GraphX, and operations (window, etc.) can easily access and run computations on DStreams. Micro-batching has contributed a lot to the platform’s overall resilience.
Apache Spark excels over Hadoop MapReduce in several key areas, including performance, ease of use, and versatility. Spark’s in-memory engine minimizes disk space usage and offers a rich set of high-level operators, allowing more efficient data sharing and processing. On the other hand, Hadoop’s MapReduce is more disk-reliant and less flexible, requiring data to be stored in its HDFS system before proceeding to the next computational step.
Despite their differences, both platforms offer unique advantages and can complement each other in big data processing. While Spark lacks a native distributed file system, Hadoop excels in handling large data sets that require asynchronous, fine-grained updates. When used together, Spark and Hadoop can unlock significant efficiencies in big data computing, making each a viable option depending on the specific needs of a project.
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