Difference Between Hadoop vs Spark
Hadoop is an open-source framework that allows to store and process big data, in a distributed environment across clusters of computers. Hadoop is designed to scale up from a single server to thousands of machines, where every machine is offering local computation and storage. Spark is an open-source cluster computing designed for fast computation. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. The main feature of Spark is in-memory cluster computing that increases the speed of an application.
Hadoop
- Hadoop is a registered trademark of the Apache software foundation. It utilizes a simple programming model to perform the required operation among clusters. All modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be dealt with by the framework.
- It runs the application using the MapReduce algorithm, where data is processed in parallel on different CPU nodes. In other words, the Hadoop framework is capable enough to develop applications, which are further capable of running on clusters of computers and they could perform a complete statistical analysis for a huge amount of data.
- The core of Hadoop consists of a storage part, which is known as Hadoop Distributed File System and a processing part called the MapReduce programming model. Hadoop basically split files into the large blocks and distribute them across the clusters, transfer package code into nodes to process data in parallel.
- This approach dataset to be processed faster and more efficiently. Other Hadoop modules are Hadoop common, which is a bunch of Java libraries and utilities returned by Hadoop modules. These libraries provide a file system and operating system level abstraction, also contain required Java files and scripts to start Hadoop. Hadoop Yarn is also a module, which is being used for job scheduling and cluster resource management.
Spark
- Spark was built on the top of Hadoop MapReduce module and it extends the MapReduce model to efficiently use more type of computations which include Interactive Queries and Stream Processing. Spark was introduced by the Apache software foundation, to speed up the Hadoop computational computing software process.
- Spark has its own cluster management and is not a modified version of Hadoop. Spark utilizes Hadoop in two ways – one is storage and second is processing. Since cluster management is arriving from Spark itself, it uses Hadoop for storage purposes only.
- Spark is one of the Hadoop’s subprojects which was developed in 2009, and later it became open source under a BSD license. It has lots of wonderful features, by modifying certain modules and incorporating new modules. It helps run an application in a Hadoop cluster, multiple times faster in memory.
- This is made possible by reducing the number of read/write operations to disk. It stores the intermediate processing data in memory, saving read/write operations. Spark also provides built-in APIs in Java, Python or Scala. Thus, one can write applications in multiple ways. Spark not only provides a Map and Reduce strategy but also support SQL queries, Streaming data, Machine learning and Graph Algorithms.
Head to Head Comparison Between Hadoop vs Spark (Infographics)
Below is the top 8 difference between Hadoop and Spark:
Key Differences between Hadoop and Spark
Both Hadoop vs Spark are popular choices in the market; let us discuss some of the major difference between Hadoop and Spark:
- Hadoop is an open source framework which uses a MapReduce algorithm whereas Spark is lightning fast cluster computing technology, which extends the MapReduce model to efficiently use with more type of computations.
- Hadoop’s MapReduce model reads and writes from a disk, thus slow down the processing speed whereas Spark reduces the number of read/write cycles to disk and store intermediate data in-memory, hence faster-processing speed.
- Hadoop requires developers to hand code each and every operation whereas Spark is easy to program with RDD – Resilient Distributed Dataset.
- Hadoop MapReduce model provides a batch engine, hence dependent on different engines for other requirements whereas Spark performs batch, interactive, Machine Learning and Streaming all in the same cluster.
- Hadoop is designed to handle batch processing efficiently whereas Spark is designed to handle real-time data efficiently.
- Hadoop is a high latency computing framework, which does not have an interactive mode whereas Spark is a low latency computing and can process data interactively.
- With Hadoop MapReduce, a developer can only process data in batch mode only whereas Spark can process real-time data through Spark Streaming.
- Hadoop is designed to handle faults and failures, it is naturally resilient toward faults, hence a highly fault-tolerant system whereas, with Spark, RDD allows recovery of partitions on failed nodes.
- Hadoop needs an external job scheduler for example – Oozie to schedule complex flows whereas Spark has in-memory computation, so it has its own flow scheduler.
- Hadoop is a cheaper option available while comparing it in terms of cost whereas Spark requires a lot of RAM to run in-memory, thus increasing the cluster and hence cost.
Hadoop and Spark Comparison Table
The primary Comparison between Hadoop and Spark are discussed below
| Basis Of Comparison Between Hadoop vs Spark |
Hadoop |
Spark |
| Category | Basic Data processing engine | Data analytics engine |
| Usage | Batch processing with a huge volume of data | Process real-time data, from real-time events like Twitter, Facebook |
| Latency | High latency computing | Low latency computing |
| Data | Process data in batch mode | Can process interactively |
| Ease of Use | Hadoop’s MapReduce model is complex, need to handle low-level APIs | Easier to use, abstraction enables a user to process data using high-level operators |
| Scheduler | External job scheduler is required | In-memory computation, no external scheduler required |
| Security | Highly secure | Less secure as compare to Hadoop |
| Cost | Less costly since MapReduce model provide a cheaper strategy | Costlier than Hadoop since it has an in-memory solution |
Conclusion
Hadoop MapReduce allows parallel processing of massive amounts of data. It breaks a large chunk into smaller ones to be processed separately on different data nodes and automatically gathers the results across the multiple nodes to return a single result. In case the resulting dataset is larger than available RAM, Hadoop MapReduce may outperform Spark.
Spark, on the other hand, is easier to use than Hadoop, as it comes with user-friendly APIs for Scala (its native language), Java, Python, and Spark SQL. Since Spark provides a way to perform streaming, batch processing, and machine learning in the same cluster, users find it easy to simplify their infrastructure for data processing.
Final decision to choose between Hadoop vs Spark depends on the basic parameter – requirement. Apache Spark is much more advanced cluster computing engine than Hadoop’s MapReduce, since it can handle any type of requirement i.e. batch, interactive, iterative, streaming etc. while Hadoop limits to batch processing only. At the same time, Spark is costlier than Hadoop with its in-memory feature, which eventually requires a lot of RAM. At the end of the day, it all depends on a business’s budget and functional requirement. I hope now you must have got a fairer idea of both Hadoop vs Spark.
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