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Spark vs Hadoop - What Are The Key Differences?
What are the key differences between Spark and Hadoop?
Spark and Hadoop are big data frameworks that are extensively used. In this article, the key variations of the technologies have been discussed at length.
When it comes to data processing frameworks for big data architecture Spark and Hadoop are among the most well-known. Both of them are at the crux of an opulent ecosystem comprising open source technologies to analyze, manage, and process big data sets. Therefore, businesses typically review both for possibly using in apps.
Debating comparisons of Hadoop and Spark from a user perspective, the key point invariably is the optimization of processing in real-time or batch processing in the big data background. However, that is an oversimplification of the variations among the Apache Spark and Hadoop frameworks.
Although Hadoop at the outset was specifically or exclusively for batch applications, these days, though, if not entirely minimally some components can be useful when it comes to analytics workloads in real-time, and interactive querying. Spark, in the meantime, was initially developed for faster batch processing compared to Hadoop.
Furthermore, the choice necessarily isn’t between using Hadoop or Spark in the big data processing environments. Most organizations use both for varied use cases for big data. Both Hadoop and Spark are useable simultaneously as well: Applications based on Spark are oftentimes developed as the top layer of Hadoop Distributed File System (HDFS) and YARN resource management technology, integral to Hadoop. For Spark, HDFS is the key option for data storage, as it is not equipped with a repository or file system.
Hadoop and Spark’s capabilities, features, and components have been discussed in detail along with their variants beginning with a few primary details regarding every open-source framework.
Hadoop the brainchild of Mike Cafarella and Doug Cutting who are software engineers was released essentially for processing big data by using MapReduce, a processing engine and programming model brainchild of and promoted by Google through a technical paper of 2004 along with HDFS. Hadoop is equipped to smartly slice and dice huge data processing issues across varied computers, locally execute calculations and thereafter collate or compile the outcomes. The distributed architecture for processing of the framework is a boon as its seamless development of big data applications aimed at clusters consisting of massive commodity servers, known as nodes.
The key aspects are inclusive of the technologies as follows:
- Hadoop Common - This essentially is preset libraries and utilities that the other components of Hadoop use
- Hadoop MapReduce - Although the role of Hadoop MapReduce has been minimized due to YARN, nonetheless MapReduce is to date the inbuilt core processing unit in operation in most Hadoop clusters for running voluminous batch applications. It plays a key role in the procedure of slicing and dicing humungous calculations and spreading out throughout varied cluster nodes and thereafter executing varied processing tasks.
- YARN - Yet Another Resource Negotiator (YARN) is the cluster resource manager of Hadoop processing assigned workloads. It lines up processing tasks allocating calculate resources inclusive of memory and CPU to varied applications. YARN became an integral component of Hadoop 2.0 replacing the tasks by Hadoop’s implementation of MapReduce.
- HDFS - At first emulating Google’s file system, HDFS handles the procedure of accessing, storing, allocating, and accessing data throughout distinct servers. It's capable of handling both unstructured and structured data, therefore it’s a top pick for developing a data lake.
Matei Zaharia innovated the back-end technology of Spark at the time enhancing the way data are arranged for increased memory processing and efficiency throughout distributed nodes of a cluster. Similar to Hadoop, Spark processes voluminous data by slicing and dicing workloads on varied nodes, however, it usually does comparatively faster. Therefore, it is equipped to manage huge amounts of data with MapReduce that Hadoop isn’t equipped to process. Spark, therefore, is decidedly a generic processing engine.
Technologies forming the key components of Spark are
- MLlib - An inbuilt machine learning library, included in MLlib are preset algorithms for machine learning along with feature selection tools and creating pipelines for machine learning.
- Structured Streaming and Spark Streaming - Stream processing abilities are added through these modules. Spark Streaming extracts data from varied sources including of Kinesis, Kafka, and HDFS, and splits the data into smaller batches representing an unending stream. Structured Streaming created on Spark SQL is a new strategy designed for reducing latency and simplifying programming.
- Spark SQL – With this module users can optimize structured data processing by running SQL queries directly or executing the Dataset API of Spark for accessing the engine for running SQL.
- Spark Core - This is the execution engine that underlies providing scheduling of jobs and coordinating underlying I/O functions, by executing the basic API of Spark.
How are Spark and Hadoop Disparate?
The fact that Hadoop uses MapReduce is in itself a noteworthy framework disparity. HDFS was aligned to Hadoop in its initial versions, meanwhile, Spark’s development was intended as a replacement for MapReduce. Although when it comes to data processing Hadoop isn’t dependent on MapReduce anymore, nonetheless the two are inseparable regardless. Many still believe that Hadoop and Hadoop MapReduce are synonymous.
The fully integrated MapReduce with Hadoop has its benefits in terms of restricting spiraling costs related to big data processing tasks with tolerance for certain delays. Spark, conversely and comparatively clearly at an advantage against MapReduce in presenting insightful analytics, on-time data processing is executed in real-time.
Hadoop revolutionized the thought process of increasing volumes of data. It enabled environments of voluminous big data with variable data forms practicable in organizations, for storing and aggregating preset data for supporting analytical applications. Therefore, it's a platform that is oftentimes used as a data lake, storing both processed preset as well as raw data for analytical use cases.
Although Hadoop transcends batch processing, it is ideal for historical data analysis. The design of Spark was from scratch for optimized and intensive data processing tasks. Therefore, it is useful in varied ways. Spark typically is useful for interactive data analysis, online applications, extract, transform and load (ETL) functions as well as similar batch processing. It auto-executes as an integral part of data analysis.
Spark in a Hadoop cluster is the top layer mimicking a staging tier, for exploring and analyzing data and ETL. That is yet another key distinction among the frameworks: Spark lacks an inbuilt file system HDFS for example, which essentially indicates that it requires pairing with similar platforms or Hadoop for management and storage of data for the long term.
Here's a comparative analysis of Spark and Hadoop on certain key aspects.
The basic distinction in architecture between Spark and Hadoop is in regards to the arrangement of data for data processing purposes. Data in Hadoop resides in disk drives in blocks and are replicated throughout the disk drives on the varied cluster servers, as HDFS provides fault and redundancy tolerance. Hadoop-based applications would then be equipped to execute as though a directed acyclic graph (DAG) or a job containing several jobs.
Hadoop 1.0 has a central Job Tracker service allocating MapReduce jobs throughout nodes executing as a standalone, as well as individual nodes executing jobs managed by the localized TaskTracker service. In Hadoop 2.0, however, Task Tracker and JobTracker have been scrapped to replace with the following YARN components:
- From a resource repository, abstractly, the system or program files are allocated to distinct applications and nodes.
- Application Master is essentially a daemon developed for every application, negotiating ResourceManager essential resources and working with Node Managers for the execution of tasks
- Node Manager is installed on every cluster node for monitoring resource consumption
- a ResourceManager daemon operates like a resource arbitrator and global job scheduler
Data residing in Spark is accessible from external repositories, like HDFS, Amazon Simple Storage Service; a cloud object store, or varied databases as well as other kinds of data stores. Although the bulk of the processing is executed in RAM, however, this platform can transfer and process data in ROM, in case of huge data presets that RAM may not have the capacity to process. Spark is essentially executable YARN-managed clusters, Kubernetes and Mesos, or in an independent mode.
Spark and Hadoop are alike in the sense that the architecture of both platforms has altered substantially in comparison with how it was designed originally. In previous version releases, Spark Core streamlined data as an in-memory data repository also known as a resilient distributed dataset (RDD), distributed throughout the varied cluster nodes. It created DAGs as well for helping in the efficient processing of scheduled jobs.
As of now, the RDD API has backing, however, on Spark 2.0, it got replaced by the programming interface that the API of the Dataset recommended. Datasets similar to RDDs are distributed data repositories with robust typing attributes, however, they are inclusive of far more enhanced optimizations enabled by Spark SQL for optimal performance. The latest architecture is inclusive of Datasets known as DataFrames, with names of columns, which are conceptually similar to frames of data in Python applications and R or relational database tables. MLlib and structured streaming both use the DataFrame/Dataset strategy.
The capability of Data processing
Both Spark and Hadoop are distributed big data frameworks equipped for processing voluminous data. Regardless of the YARN-enabled processing of huge workloads, Hadoop is to date complements MapReduce, for batch processing that typically runs for a protracted period without stringent service-level agreements (SLAs).
Spark, conversely, can execute batch processing optionally to MapReduce and provides complex APIs as well for other use cases for processing. Added to stream processing, modules for machine learning, and SQL is PySpark interfaces and is in Python programming languages, and Graphics API for SparkR for graph processing
MapReduce-enabled Hadoop processing typically is sluggish and managing is challenging. Spark conversely, oftentimes is much faster for most types of batch processing: Those advocating Spark believe that it is capable of performing much faster compared to Hadoop with the same workload in the case of batch job processing in RAM, though performance efficiency is considerably lower in real-time applications.
The fact that batch processing on Spark is faster than on Hadoop is an advantage due to Spark’s ability of processing data without needing data to be written back on ROM as a temporary step. However, Spark applications too, that are executed on ROM perform much faster compared to Hadoop with MapReduce workloads as developers of Spark believe.
However, Hadoop is perhaps at an advantage, in regards to the management of the execution of workloads for a protracted period on one cluster concurrently. Executing too many Spark applications simultaneously may at times result in issues with insufficient memory slowing all application performance.
In principle, systems on the Hadoop platform are scalable to make room for accessing bigger data sets periodically as the information can be processed and stored in a far more cost-effective manner on ROM rather than RAM. An operation of YARN was included in Hadoop 3.0, enabling clusters to provide support to numerous nodes and beyond through the connection of several subclusters, with their resource handlers.
The disadvantage is the fact that investments in additional labor would be necessary from big data and IT teams for on-site implementations for provisioning newer nodes and adding to a single cluster. Furthermore, storage in Hadoop is localized equipping computing resources to the nodes of a cluster, which users and applications alike who may not belong to a cluster may find difficult when it comes to accessing the information. However, certain scalability drawbacks are feasibly managed automatically with Hadoop’s cloud version.
One of the core advantages of Spark is that computing and storage functions are distinct. Therefore, users and applications alike seamlessly can access the information regardless of their physical location. Included in Spark are tools for helping users freely extend nodes according to the requirements of the workload; it's seamless as well to reallocate nodes automatically after Spark’s processing cycle. Scaling nodes are challenging in Spark with distributed workloads throughout nodes independently for reducing leakage of memory.
Hadoop’s foolproof security with little overhead externally would be ideal for retaining data in the long term. HDFS provides transparency, foolproof encryption of data with distinct encryption area directories as well as an inbuilt service to manage encryption keys; included is a model based on permission as well for enforcing directories and file access controls, along with the capability of creating lists of access control used for implementing security based on the role as well as other rules for varied groups or users.
Moreover, Hadoop is equipped for operation in a safe mode along with Kerberos verification for users and services. It can avail access to related tools including Apache Knox, a gateway known as a REST API providing proxy and verification services for enforcing security protocols in clusters of Hadoop, as well as Apache Ranger, a central framework for security management for environments of Hadoop. These combined features provide robust security initially in comparison with Spark.
Spark’s security ecosystem comparatively is far more complicated supporting varied security layers according to the nature of the deployment. It typically uses a verification strategy that is shared secretly for example, for procedure calls remotely among Spark procedures, with mechanisms that are specific to any deployment for generating the unique passwords. There are use cases where protections are constrained due to daemons and applications sharing the secret, while it is not applicable when Spark executes on Kubernetes or YARN.
Enforcing security over and above streams of data is Spark’s main goal. However, they are not as permanent as data storage for longer periods. What is worrying is this may lead to enabling and indeed inviting cybercrimes at a far wider scale and scope through security loopholes. Additionally, Spark security and validation checks by default are disabled. Having said that, adequate protections are achievable with the combination of the suitable architecture for encryption along with key policies of the management.
SPARK VS HADOOP | WHAT IS THE DIFFERENCE?
This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.
© 2022 Avik Chakravorty