Apache Spark vs Hadoop – What’s the Difference? (Pros and Cons)
Apache Spark vs Hadoop – What’s the Difference? (Pros and Cons). The Apache tech stack consists of a variety of tools designed to solve modern world problems such as big data analytics. Both, Apache Spark and Hadoop are among the top platforms that continue to deliver high end enterprise functionalities. Large organizations globally use these platforms mostly to manage big data.
On one side, Apache Spark provides large scale data analytics while Hadoop provides storage for both structured and unstructured data. Well, both tools are open source data management tools offered by the Apache Software foundation. Besides, each platform has a large ecosystem of open source technologies that help with data management. Both Apache Spark and Hadoop can be used together or as standalone tools depending on your needs.
This article discusses both platforms in depth, including their features, components, and the major differences between them.
So, shall we start with Apache Spark vs Hadoop – What’s the Difference? Read on!
What is Apache Spark?
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The first tool to introduce in this article blog Apache Spark vs Hadoop – What’s the Difference? is Apache Spark. This is an open source, multi language, unified, parallel processing engine for executing large-scale data analytics applications and machine learning on single nodes or clustered computers. This platform is designed based on Hadoop MapReduce and expands the MapReduce model to effectively use it for other computations, such as stream processing and interactive queries. Since, Apache Sparks’ key feature is its in-memory cluster computing which improves the running speeds of an application.
Spark covers workloads like iterative algorithms, batch applications, interactive queries, and streaming. Apache Spark also reduces the management burden for users who are maintaining separate tools.
Before Spark , there was MapReduce, a processing framework that allowed Google to index the ever growing bulk of content on the web over large cluster groups of commodity servers. Matei Zaharia created Spark as a project inside the AMPLap at The University of California, Berkeley.
How Does Apache Spark Work?
Interestingly, Apache Spark contains a code base, and the system has several layers. Every layer is in charge of a specific responsibility. The initial layer is the interpreter; Spark uses a Scala interpreter with some variations. As users enter their code in the Spark console (creating applying operators and RDDs), Spark generates an operator graph. Once the user executes an action such as collect, the Graph submits to a DAG Scheduler.
Then, the DAG scheduler sections the operator graph into MapReduce stages. The sections of the input data form the basis of each data stage. To optimize the Graph, the DAG Scheduler routes operators together. Users in a single stage can route multiple map operators. This optimization is a crucial component of Spark’s performance. The concluding result of the DAG scheduler is a collection of stages. The stages move on to the Task scheduler. The Task scheduler initiates tasks through the cluster manager.
Features of Apache Spark
All in all, Apache Spark has many features, making it a great big data processing engine option. The features that make Apache Spark one of the most popular Big Data platforms are:
Processing Speed
The world of Big Data processing primarily involves processing bulks of complex data. Organizations and enterprises are looking for systems that can process vast amounts of data in the least possible time. Therefore, Apache Spark applications can run 10x faster on disk and up to 100x faster in memory in Hadoop clusters.
Apache Spark uses Resilient Distributed Dataset (RDD), enabling Spark to store data transparently on memory and read or write it to disk only when required. This process minimizes the disk read and write time.
Ease of Use
Afterwards, Apache Spark enables users to write scalable applications in Scala, Java, R, and Python. So, developers build and run Spark applications in the programming language of their choice. Additionally, Spark has an in-built set of more than 80 high-level operators. Users can interactively use Spark to query data from SQL, R, Scala, and Python shells.
Support for Complex Analytics
Spark supports basic “map” and “reduce” operations and supports streaming data, SQL queries, and advanced analytics, such as graph algorithms and Machine Learning. Apache Spark has powerful libraries, including Spark Streaming, GraphX, Mllib, and SQL & DataFrames. In addition, Spark lets users combine the abilities of all these libraries into a single workflow or application.
Real-Time Stream Processing
Spark developers designed to handle real time data streaming. Although MapReduce handles and processes the data stored within Hadoop clusters, Spark can not only do that but also controls data in real time through Spark Streaming.
In contrast to other streaming solutions, Spark restores lost work and delivers the same semantics out-of-the-box without needing additional code or configuration. It also allows clients to reuse the exact code for stream and batch processing.
Dynamic Nature
Apache Spark runs individually in cluster mode, on Apache Mesos, Kubernetes, Hadoop YARN, and even in the cloud. Spark also accesses different types of data sources. For example, Spark runs on the YARN cluster manager and read existing Hadoop data. Apache Spark reads from any Hadoop data repository, such as HBase, Apache Cassandra, Apache Hive, and HDFS. This Spark feature makes it a suitable instrument for migrating pure Hadoop applications as long as its use case is Spark friendly.
An Active and Growing Community
Developers from more than 300 companies have played a part in designing and building Apache Spark. Spark is supported by an active community of developers constantly working to enhance its performance and features.
Components of Apache Spark
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Below are some components of Apache Spark architecture:
Spark Core
Apache Spark Core consists of a general execution engine specific to the Spark platform, created per the requirement. It offers reference datasets saved in external storage systems and in built memory computing.
Spark SQL
Spark SQL is a component that sits on top of Apache Spark Core. It initiates SchemaRDD, which is a new set of data abstraction. SchemaRDD offers support for both semi structured and structured data.
Spark Streaming
Spark Streaming is a component of Apache Spark that enables Spark to run real time streaming data. It has an API that manipulates data streams that compare with the RDD API. It allows programmers to understand the project and transition through the apps that control the data and provide the results in real time.
Mllib (Machine Learning Library)
Apache Spark comes equipped with a Machine Learning library known as Mllib. Mllib contains a variety of solid Machine Learning algorithms, clustering, collaboration filters, classification, and more. Mllib also comes with a few low level functions that work together with the other functionalities to enable Spark to scale out over a cluster.
GraphX
GraphX is a library in Sparks to perform computations and manipulate graphs. Like Spark SQL and Spark Streaming, GraphX also expands Spark RDD API, which generates a directed graph. It also contains several operators to manipulate the graphs and graph algorithms.
Pros of Apache Spark
- Has a simple and straightforward API that’s easy to learn and implement.
- Significantly faster than Hadoop due to its ability to perform in memory computations.
- Easily deployable on a cluster of machines.
- Fault tolerant and provides automatic recovery from errors.
- Cost effective, because it needs fewer machines and less storage space.
- Open source project that is freely available.
Cons of Apache Spark
- Compared to other Big Data frameworks, Apache Spark provides fewer machine learning libraries and algorithms.
- Spark is resource intensive and strains your cluster’s resources.
- It is not ideal for a multi-server environment.
- You can encounter problems when working with small files.
Up next with Apache Spark vs Hadoop – What’s the Difference? is to introduce Apache Hadoop.
What is Apache Hadoop?
The second solution of this blog article Apache Spark vs Hadoop – What’s the Difference? is Apache Hadoop. Second tool, also open source, java based software. Altogether, it enables developers to manage big data sets. It allows a network of computers to execute large and complex data problems. Hadoop is a cost effective, scalable solution that stores and runs unstructured, structured, and semi structured data. It scales from a single server to thousands of server machines, each offering users storage and local computation.
Instead of depending on hardware to deliver high availability, the library identifies and manages failures at the application layer.
How Does Apache Hadoop work?
Basically, Hadoop is primarily an environment of libraries, and each library has a specific task it is responsible for. HDFS writes data to the server and then reads and reuses it multiple times. HDFS has a faster continuous multiple read and write action when compared to other file systems.
Job Tracker, the master node, manages each Task Tracker slave node and runs the jobs. Each time a user requires Data, they send a request to NameNode, which is the smart node of the cluster of HDFS and is in charge of the DataNode slave nodes. The request passes to the DataNode, which serves the required data.
Enterprises use yarn or MapReduce for processing and scheduling. Hadoop MapReduce runs a sequence of jobs. Each of these jobs is a java application that executes the data. As an alternative to MapReduce querying tools such as Hive Hadoop, Pig Hadoop provides data hunters power and flexibility.
Features of Apache Hadoop
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Here are some key features of Apache Hadoop:
Open Source
Hadoop is open source software. This means that its source code is accessible free of charge for inspection, analysis, and adjustment. It enables organizations to adjust the code according to their requirements.
Scalability
Hadoop is capable of both horizontal and vertical scaling. It deposits and distributes large datasets across hundreds of server machines that run in parallel. Unlike conventional RDBMS (Relational Database Systems) that can’t scale to process large volumes of data, Hadoop allows businesses to run applications on multiple nodes involving thousands of terabytes.
Fault Tolerance
Fault tolerance is Hadoop’s most important aspect. HDFS in Hadoop contains a replication mechanism to include fault tolerance. Hadoop generates a copy of each block on different machines, which relies on the replication factor. So if any server machine within a cluster fails, users can access an exact copy of the data from other machines.
High Availability
Even in conditions that are not ideal, Hadoop ensures high availability of data. Because of its fault tolerance, if any DataNode fails, the data will still be accessible to users from the various DataNodes surrounding a copy of the exact data.
Additionally, the high availability Hadoop cluster consists of two or more running NameNodes which are either active or passive, in a hot standby configuration. The NameNode is the active node, while the passive node is the standby node that examines the edit log modification of the active name node and executes them to its namespace.
If an active node fails, the passive node steps up to manage the active node. So even if the NameNode crashes, files are still accessible to users.
Cost Effective
Apache Hadoop provides an economical storage solution for enterprises exploring datasets. The challenge that faces traditional relational database management systems is that it is costly for clients to scale to such a level to process large volumes of data.
Robust Ecosystem
Hadoop has an extensive ecosystem ideal for developers in large organizations. The Hadoop Ecosystem includes a collection of technologies and tools that work to deliver various data processing needs. The Hadoop ecosystem contains projects such as Yarn, MapReduce, Hive, HBase, Pig, Avro, Flume, Zookeeper, and many more.
Components of Apache Hadoop
Hadoop architecture comprises the following components:
Hadoop Distributed File System (HDFS)
HDFS has a master/slave architecture. This architecture comprises a single NameNode that functions as the master and several DataNodes that acts as a store. Both NameNode and DataNode can run on commodity machines. The creators of HDFS used the Java language to develop it. This means any Java friendly machine can run the NameNode and DataNode software.
NameNode
This is the master node that manages and maintains all data. It leads to DataNodes and fetches data from them. A NameNode stores the file system data.
Secondary NameNode
This is the master node and is in charge of maintaining the checkpoints of the metadata on the servers. It is the slave node that contains all the file data in the form of blocks.
HDFS stores the files and the application data separately on dedicated servers. The file’s contents are replicated by HDFS on multiple DataNodes depending on the replication factor. The NameNode and DataNode communicate with each other through TCP protocols.
MapReduce Layer
The MapReduce layer comes into being once the client application submits a MapReduce job to Job Tracker. The Job Tracker relies on the request to the proper Task Trackers. If the Task Tracker times out or fails that section if the system reschedules the job.
Pros of Hadoop
- Designed to scale from a single server to thousands of machines, each with its own computation and storage capabilities.
- Hadoop’s distributed computing ensures there is no single point of failure, so the system can continue to function if one of the nodes fails.
- The cost of hardware and software for a Hadoop cluster is significantly less than for a conventional system.
- Designed for high availability and to detect and recover from application-level failures.
- Capable of handling structured, semi-structured, and unstructured data, enabling businesses to utilize all data types.
- Performs storage and processing on the same node, allowing for faster processing speeds.
Cons of Hadoop
- Has a big learning curve for users unfamiliar with it.
- Typically slower than conventional systems, particularly when the data is structured and can be managed by relational databases.
- The data stored in Hadoop is not encrypted, despite the fact that it has some security features.
- Is intended for batch processing, so it cannot be used for real time processing.
- Insufficient documentation makes it difficult to comprehend.
Now it is the main part of our article Apache Spark vs Hadoop – What’s the Difference?
Main Differences Between Apache Spark and Apache Hadoop
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As evident above, Apache Spark and Apache Hadoop are designed to perform different tasks. Here are some of the main differences between Apache Spark and Hadoop:
Performance
Hadoop boosts its general performance by accessing the data stored locally on HDFS. However, this does not come close to Spark’s in memory processing. Apache Spark is 100x faster on RAM than Hadoop with MapReduce. The main reason why Spark performs better is that it uses RAM instead of reading and writing intermediate data to disk. Hadoop saves Data on many repositories and then runs the data in batches with MapReduce.
Cost
Both of these platforms are open source and free. However, development, maintenance, and infrastructure costs differ. The critical factor when it comes to cost is the underlying hardware that users require to run these tools. Since Hadoop is compatible with any kind of disk storage for a data process, the costs of running it are relatively low.
However, Spark relies on in memory computations to achieve real time data processing. Switching up with plenty of RAM adds to the cost of ownership.
Data Processing
Both of these structures use different approaches to handle data. Although both Spark with RDDs and Hadoop with MapReduce process data in a distributed environment, Hadoop is best suited for batch processing. In contrast, Spark excels in real time processing.
Spark operates with resilient distributed datasets (RDD). An RDD is a collection of elements saved in partitions on nodes over the cluster. A single RDD is typically too large for one node to manage. So, Spark partitions RDDs to the nearest nodes and runs the operations in parallel. The system tracks all actions executed on an RDD using a Directed Acyclic Graph (DAG).
By combining high level APIs with in memory computations, Spark effectively handles a live stream of unstructured data. Additionally, the system saves the data in a predetermined number of partitions. A single node contains as many sections as the user requires; however one partition can not extend to another node.
Fault Tolerance
Both Spark and Hadoop provide a respectable level of managing failures. However, they employ different approaches to fault tolerance.
Hadoop’s fault tolerance feature is the basis of its operation. Hadoop replicates data multiple times across the nodes. In the event of a failure, the system resumes operations by generating the missing blocks from different locations. The master is not responsible for tracking the status of each slave node. In case a slave nod fails to reply to pings from a master node. The master allocates the unfinished jobs to another slave node.
Spark implements RDD blocks to accomplish fault tolerance. The Spark system tracks the origin of the immutable dataset. It can then resume the process when there is an issue. Spark recreates data within a cluster using DAG tracking of workflows. This data framework allows Spark to handle failures efficiently.
Scalability
Hadoop employs HDFS to handle big data. When data rapidly grows, Hadoop swiftly scales to meet the demand. Since Spark lacks its file system, it has to depend on HDFS when the data grows too large to handle.
Spark clusters extend and improve computing power by joining more servers to the network. Because of this, the number of nodes in both structures can reach thousands.
Ease of Use and Support for Multiple Languages
Spark is the newer of these two frameworks and may not have as many experts as Hadoop, but it is still more user friendly. Spark offers support for multiple languages together with the native Scala. Hadoop is a java project, and users primarily use java and python to interact with MapReduce. Hadoop does not provide an interactive mode to assist users. However, it interoperates with Hive and Pig tools to enable developers to write advanced MapReduce programs.
Another advantage of Spark is that it allows programmers to reuse code where possible by doing so, developers can minimize the application development time.
Security
Between Hadoop and Spark, Hadoop is more secure. Mostly, Spark’s security feature is on by default. So unless a user actively tackles the problem, the setup remains exposed. In comparison, Hadoop operates with various access and authentication control methods. However, Spark achieves a sufficient level of security by integrating with Hadoop.
Resource Management and Scheduling
Last comparison of bothApache Spark vs Hadoop – What’s the Difference? is Resource Management and Scheduling. Hadoop lacks an in built scheduler. It utilizes external solutions to manage resources and schedule tasks. With NodeManager and ResourceManager, YARN is in charge of resource management within a Hadoop cluster. YARN does not handle the state management of single applications; it only apportions available processing power.
However, in Spark, all these functions are in built. The DAG scheduler is responsible for dividing operators into phases. Each stage has several tasks that DAB schedules and Apache Spark needs to execute.
Thank you for reading Apache Spark vs Hadoop – What’s the Difference? We shall conclude.
Apache Spark vs Hadoop – What’s the Difference? (Pros and Cons) Conclusion
Summing up, both Apache Spark and Hadoop are both effective big data tools, but they serve distinct purposes. Hadoop excels at batch processing, whereas Spark excels at iterative and interactive computation. Spark is the superior option for streaming applications due to its superior memory efficiency and sophisticated query engine.
To read more about Apache Spark, navigate to our blog over here.
