Apache Spark and Hadoop are two revolutionary products that have made distributed processing of large data sets across clusters of computers a cakewalk. This article discusses the differences between spark vs Hadoop MapReduce. It also discusses the performance and resource management in Spark and Hadoop to compare them in a better manner.
Spark vs Hadoop MapReduce Summary
The following table summarizes the differences between Spark and Hadoop MapReduce.
|Apache Hadoop offers an open-source implementation of MapReduce.||Spark uses its own implementation of MapReduce.|
|Hadoop doesn’t use in-memory caching.||Spark uses in-memory computation as well as in-memory caching.|
|Hadoop saves the intermediate outputs in disks.||Spark Saves intermediate outputs in the RAM itself.|
|Hadoop doesn’t inspect or optimize the job.||Spark optimizes each task using DAGs and RDDs.|
|Hadoop uses the YARN resource manager to execute tasks.||Spark can use YARN as well as its standalone resource manager.|
|Hadoop provides a reliable and scalable storage solution(HDFS) for big data.||Spark has no data solution. It only focuses on performance optimization.|
If you want to learn more about these differences, keep reading this article.
What is Spark?
Apache Spark is an open-source, distributed computing system that allows for fast and efficient processing of large-scale data sets. It was first introduced in 2014 by the Apache Software Foundation. Since then, it has become one of the most widely used frameworks for big data processing.
- Spark has been designed to handle batch processing, streaming data, machine learning, and graph processing all in the same framework. For this, it uses a distributed processing model, where large data sets are broken down into smaller pieces and processed in parallel across multiple nodes or machines in a cluster.
- Spark also provides a high-level API for programming. This makes it easy for us to work with Spark in different programming languages. Right now, Spark APIs are available in Scala, Java, SQL, Python, R, C#, and F#.
- Resilient Distributed Datasets (RDDs) power the performance of the Spark framework. It enables spark to keep track of all the operations and transformations on the dataset. Without RDDs, the Spark execution engine will have no information about the operations and transformations.
What is Hadoop?
Apache Hadoop is also an open-source distributed computing framework designed to handle large-scale data processing and storage. It was also developed by Apache Software Foundation. Right now, it is now widely used by organizations to store, process and analyze big data.
Hadoop consists of two main components: Hadoop Distributed File System (HDFS) and MapReduce.
- Hadoop Distributed File System (HDFS) is a distributed file system that stores data across a cluster of machines. It provides reliable and fault-tolerant storage for large datasets by replicating data across multiple nodes in the cluster. HDFS is optimized for handling large files and supports streaming access to data.
- MapReduce is a programming model and processing engine for distributed data processing. It allows users to write programs that process large volumes of data in parallel across a cluster of machines. MapReduce breaks down a task into smaller sub-tasks that can be processed in parallel across multiple nodes in the cluster.
Hadoop is highly scalable and can handle large amounts of data. It can also be easily extended with additional components such as Hive, Pig, and Spark to provide additional functionality for data processing and analysis. We use Hadoop widely in industries such as banking, healthcare, and e-commerce for various data-related tasks such as data warehousing, data mining, and predictive analysis.
Spark vs Hadoop MapReduce
MapReduce is a programming model developed by Google to facilitate distributed computation of large datasets. Apache Hadoop offers an open-source implementation of MapReduce.
Talking about Spark vs Hadoop MapReduce, you will often hear people saying that Spark doesn’t use MapReduce. However, this isn’t true. Both Spark and Hadoop use the MapReduce programming model.
Spark uses its own implementation of MapReduce with a different Map, Reduce, and Shuffle operation compared to Hadoop. Spark aims to replace the Hadoop MapReduce’s implementation with its own faster and more efficient implementation.
Hadoop vs Spark Performance
Generally speaking, Spark is faster and more efficient than Hadoop. Spark has an advanced directed acyclic graph (DAG) execution engine that supports acyclic data flow and in-memory computation. Due to this, Apache Spark runs programs up to 100 times faster than Hadoop MapReduce in memory and 10 times faster on disk.
All the computation is done in the memory (RAM) itself. Then what is special about Spark is that it runs programs 100 times faster than Hadoop. Here are the reasons.
- Spark uses in-memory computation as well as in-memory caching. Also, it stores all the intermediate outputs in RAM instead of the disk. This is the reason why Spark is so fast. Due to caching, Spark runs iterative machine-learning programs 100 times faster than Hadoop. On the other hand, it can give 10 times better performance in interactive data mining tasks.
- When we run any task in Spark, the code is split into sub-tasks that are connected using a DAG. Spark keeps track of each sub-task using RDDs and optimizes the execution. In Hadoop, we just write and execute a MapReduce job. Hadoop doesn’t inspect or optimize the job.
Spark vs Hadoop: Resource Management
Hadoop uses the YARN resource manager to execute tasks. Applications in Hadoop negotiate with YARN to get the resources needed for the execution. When you run Spark in local mode, it can use its own resource manager. However, when we run Spark in distributed mode, it also needs a resource manager like YARN.
Hadoop provides a reliable and scalable storage solution(HDFS) for big data. On the other hand, Spark only focuses on performance optimization. You can use any storage solution such as HDFS, S3, or HBase with Spark.
Spark vs Hadoop: Advantages of Spark over Hadoop
By now we have discussed the basic differences between Hadoop and Spark. Let us now discuss some of the advantages of Spark over Hadoop.
- Faster processing: As discussed above, Spark processes data in-memory, while Hadoop MapReduce reads and writes intermediate data to disk. This means that Spark can process data much faster than MapReduce, especially for iterative algorithms and interactive data analysis.
- Easy to use: Spark provides a higher-level programming interface. It supports a wider range of programming languages, including Python, R, and SQL. This makes it easier for developers to write and test Spark applications.
- Real-time processing: Spark provides built-in support for real-time processing and streaming. This makes it easier to process data as it is generated, rather than waiting for the entire data set to be processed.
- Advanced analytics: With Spark, we get a wide range of built-in libraries for advanced analytics, including machine learning, graph processing, and SQL-like queries. This makes it easier to perform complex data analysis without writing custom code.
- Compatibility with Hadoop: Spark can run on Hadoop clusters and can read data from Hadoop Distributed File System (HDFS). This makes it easy to integrate Spark with existing Hadoop-based data processing pipelines.
- Cost-effective: Spark can be run on commodity hardware. This makes it more cost-effective than Hadoop MapReduce. Spark’s ability to process data faster also means that fewer resources are required to process large datasets.
Spark vs Hadoop: Advantages of Hadoop over Spark
While Spark has many advantages over Hadoop, Hadoop also has some unique advantages. Let us discuss some of them.
- Storage: Hadoop Distributed File System (HDFS) is better suited for storing and managing large amounts of data. HDFS is designed to handle large files and provides a fault-tolerant and scalable way to store data. Spark provides no storage solution.
- Batch processing: Hadoop MapReduce is better suited for batch processing of large data sets. It provides a simple and efficient way to process large data sets in parallel across multiple nodes in a cluster.
- Stability: Hadoop has been around for longer than Spark and has a more mature ecosystem. It has a more stable and reliable platform and is generally better suited for mission-critical applications.
- Security: Hadoop has built-in security features that make it easier to secure data and control access to sensitive data. This includes Kerberos-based authentication and authorization, encryption, and auditing.
- Hadoop Ecosystem: Hadoop has a large and mature ecosystem with many additional tools and technologies that are built on top of it, such as Apache Pig, Apache Hive, and Apache HBase. This makes it easier to integrate Hadoop with other data processing tools and platforms.
Hadoop or Spark: What should you use?
By now, we have discussed all the aspects of Spark vs Hadoop. Let us now discuss the use cases where we should use Hadoop and Spark. Here, I would like to mention that Spark is not designed to replace Hadoop. It just provides a faster way of processing big data.
You should use Spark if you need real-time processing capabilities with fast processing of data.
- If you are working on iterative tasks such as predictive analytics or classification algorithms using machine learning, Spark is a better choice.
- Spark is a user-friendly and easy-to-use platform. You can use it with different programming languages. So, if your team consists of people knowing different programming languages and want to work on a single big data project, Spark should be your go-to choice.
- If you have an existing Hadoop setup, you can integrate Spark with your existing Hadoop-based data processing pipelines to increase performance.
Hadoop can also be a better alternative to Spark in many scenarios.
- If you need to store and manage large amounts of data and perform batch processing on the data, you can choose Hadoop instead of Spark. Spark is best suited for interactive data mining.
- Hadoop comes with its own ecosystem. Hence, if you need a more stable and reliable platform for mission-critical applications, you can use Hadoop instead of Spark.
In this article, we discussed Spark vs Hadoop MapReduce to identify the advantages and disadvantages of using both frameworks. I hope you enjoyed reading this article.
To learn more about programming, you can read this article on Pyston vs PyPy. You might also like this article on python vs R for data science.
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