Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Table of contents
1.
Introduction
2.
MapReduce
2.1.
MapReduce Processing Flow
2.2.
Pros of MapReduce 
2.3.
Cons of MapReduce
2.4.
Applications of MapReduce
3.
Spark
3.1.
Spark Processing Flow 
3.2.
Pros of Spark
3.3.
Cons of Spark
3.4.
Applications of Spark
4.
MapReduce vs Spark
5.
Frequently Asked Questions
5.1.
What is MapReduce?
5.2.
What is Spark?
5.3.
What is the main difference between MapReduce and Spark?
6.
Conclusion
Last Updated: Mar 27, 2024

MapReduce vs Spark

Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

MapReduce and Spark are powerful data processing frameworks widely used in the industry. While both can handle large-space data processing, do you know there are some key differences between them? Do you know how we can efficiently process vast amounts of data in the applications with a parallel distributed algorithm on a cluster?

mapreduce vs spark

In this article, we will discuss MapReduce vs. Spark by discussing their applications, pros, and cons. In the end, we will also outline some key differences between the two in tabular format. Moving forward, let’s first understand about MapReduce.

MapReduce

MapReduce is a framework used in distributed data processing. This programming model was primarily designed for processing distributed data introduced by Google. This framework helps to write applications for processing large amounts of data on multiple interconnected computers that work together and form a cluster.

Cluster means collections of computers (nodes) that are networked together for performing parallel computations on large data sets.

MapReduce is a component of Hadoop. The large dataset is split and combined in MapReduce for parallel processing to give a final result. Its libraries are written in various programming languages with the needed optimizations. It is used for mapping each job and reducing it to the equivalent tasks. This provides less overhead over a cluster network and reduces the processing power. This means the large data sets are divided into smaller tasks known as maps. These are then combined to give results known as ‘reduce’. This process reduces the overhead/workload over the cluster network and helps to minimize the power used to process the information.

MapReduce Processing Flow

Below are the steps of processing flow (Data flow) in MapReduce.

flowchart
  • As an initial step, the input reader reads the upcoming data and splits it into required size data blocks where each data block is associated with a Map function. After the input reads the data, corresponding key-value pairs are generated, and the input files reside in HDFS.
     
  • The coping key-value pairs are processed by the map function, and the corresponding output key-value pairs are generated. Also, the map input and output type need not be the same each time.
     
  • As a next step, the output of each Map function is assigned to the appropriate reducer by the partition function. This function is provided by the available key and value pairs. The index of the reducers is returned.
     
  • The next step is shuffling and sorting, in which the data is shuffled between nodes to enable it to process for reduced function. On input data, the sorting operation is performed, where the data is compared through the comparison function and is arranged in a sorted manner.
     

Recommended article- Hadoop MapReduce 

Pros of MapReduce 

Below are some of the Pros of using MapReduce.

  • MapReduce is scalable due to its simple design.
     
  • At any step of execution, we have the control of the process.
     
  • MapReduce is parallel in nature. Therefore, it is efficient if we want to work with both structured and unstructured data.
     
  • MapReduce doesn't require very high memory compared to Hadoop’s other ecosystem components. Therefore it can work at high speed with minimal memory.
     
  • MapReduce is useful in computation and graph problems such as Geospatial query problems.
     
  • MapReduce is cost-effective as it enables users to store the data cost-effectively.
     
  • MapReduce supports parallel processing through which multiple tasks of the same dataset can be processed parallelly.

Cons of MapReduce

Some of the cons of MapReduce are mentioned below.

  • Using MapReduce, one might face a big challenge, which takes a sequential multi-process approach to run a job and writes the output back in HDFS (Hadoop Distributed File System). As each step requires reading and writing, Its jobs are usually slower because of the latency or delay of disk I/O as this affects the performance and execution time.
     
  • Even for common operations such as join, filter, sorting, etc., a lot of manual coding is required.
     
  • It is challenging to maintain as the semantics are hidden inside the reduced functions and map.
     
  • It is a rigid framework.
     
  • MapReduce is inefficient in real-time processing, such as OLAP and OLTAP.

Applications of MapReduce

Below are some of the applications of MapReduce.

  • MapReduce is used to distinguish the most loved items according to a client or customer's purchase history in multiple e-commerce suppliers like Amazon, Walmart, and eBay.
     
  • MapReduce is used in data warehouses for analyzing large data volumes in the data warehouse by implementing the required business logic and getting data insights.
     
  • MapReduce can handle failures without downtime.
     
  • MapReduce provides a scalable framework by allowing users to run an application from multiple nodes.
     

Moving forward, let’s understand about Spark.

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

Spark

Spark, also known as Apache Spark, is an open-source framework focusing on interactive queries, machine learning, and real-time workloads. It's a high-speed big data and machine learning engine, originally developed at UC Berkeley in 2009. It is one of the most famous big data distributed processing frameworks, with approximately 365,000 meetup members in 2017.

It used in-memory caching and an optimized query execution approach. Spark is used by organizations such as FInRA, Yelp, Zillow, DataXu, Urban Institute, and CrowdStrike.

Recommended article - Introduction To Spark ML 

Spark Processing Flow 

Below are the steps of processing flow in Spark.

Spark Processing Flow
  • Spark works with the system to distribute the data across the cluster and process the data parallelly.
     
  • It uses master/slave architecture(one central coordinator) and various distributed workers. The central coordinator here is a driver running its own Java process.
     
  • The drivers communicate with large distributed workers known as executors. Each executor is known as a separate Java process.
     
  • The spark application, being the combination of the driver and its executor, is launched on a set of machines with the help of a cluster manager. 
    Spark has a default standalone Cluster Manager. Apart from it, Spark also works with open-source cluster managers such as Hadoop Yarn or Apache Mesos, etc.

Pros of Spark

Some of the pros of Spark are mentioned below.

  • It overcomes the limitations of MapReduce by processing in-memory as it reduces the number of steps required in a job compared to MapReduce by reusing data across multiple parallel operations.
     
  • Spark runs fast enough using in-memory caches and optimized query execution.
     
  • Spark is developer friendly as it natively supports Scala, Java, R, and Python and provides us with various programming languages to build applications.
     
  • Spark allows running multiple workloads, such as interactive queries, machine learning, graph processing, and real-time analytics.

Cons of Spark

Below are some of the cons of Spark.

  • There is no file management system available within Spark. Therefore it has to rely on another caused-based platform such as HDFS.
     
  • Apache Spark offers higher latency and lower throughput as compared to Apache Flink, which has higher throughput and low latency.
     
  • In Spark, there is a need for manual optimization for the jobs to be optimized.
     
  • Spark is not suitable for a multi-user environment.

Applications of Spark

Some of the applications of Spark are mentioned below.

  • Spark is used in over 1000 organizations for production.
     
  • It is widely used in banking to predict a customer’s churn and recommend new financial products.
     
  • Spark is used for analyzing stock prices and predicting future investment baking trends.
     
  • Using Spark, we can provide customers with the necessary services to attract them to the product.
     
  • Spark is known for eliminating the downtime of internet-connected equipment, as it recommends whenever there is a need for preventive measures.

MapReduce vs Spark

Below are some of the differences between MapReduce and Spark.

Basis MapReduce Spark
Definition MapReduce is an open-source framework used for writing data in HDFS (Hadoop File System). Spark is mainly used for high-speed data processing. Spark, also known as Apache Spark, is an open-source framework focusing on interactive queries, machine learning, and real-time workloads.
Speed MapReduce is slow as compared to Spark. Spark is faster than MapReduce.
Real-time processing MapReduce cannot handle real-time processing. Spak can deal with real-time processing.
Security MapReduce supports more security projects as it can access elements in Hadoop security.

Spark is not as efficient in providing security as compared to MapReduce.

Spark can only use the ‘shared secret password’ approach for authentication.

Scalability In MapReduce, we can add different ‘n’ nodes. Therefore, it has good scalability. Spark’s scalability is low as compared to MapReduce.
Caching memory MapReduce is unable to cache memory for performing a task. Spark can cache memory data to process a task.
Processing Paradigm MapReduce is batch oriented. Spack has an interactive and real-time processing paradigm.
Ecosystem MapReduce is a component of Hadoop.  Spark is a widened standalone ecosystem.
Latency Due to its lower performance, MapReduce offers greater latency. Spark is a high-speed engine. Therefore, it has low-latency processing capabilities.
Tolerance to Failure MapReduce has a high tolerance to failure compared to Spark. If, in the execution, a process becomes corrupted, it can restart from the point where it stopped earlier. This happens because MapReduce uses hard drives instead of RAM. Spark has a low tolerance to failure compared to MapReduce. Therefore it has to start from scratch if a process becomes corrupted.

To decide which one is better depends on a particular problem statement that we want to solve, and we opt for one which fits best according to the given situation. Like, MapReduce saves and retrieves its results on each iteration. Therefore, it can be used for programs that do not require significant memory.

Frequently Asked Questions

What is MapReduce?

MapReduce is an open-source framework that is used for writing data in HDFS (Hadoop File System). MapRduce is a component of Hadoop. The data is split and combined in MapReduce to give a final result. Its libraries are written in various programming languages with the needed optimizations. It is used for mapping each job and reducing it to the equivalent tasks.

What is Spark?

Spark is an open-source framework that is mainly used for high-speed data processing. Spark, also known as Apache Spark, is an open-source framework focusing on interactive queries, machine learning, and real-time workloads.

What is the main difference between MapReduce and Spark?

Spark is an in-memory distributed computing framework that stores the intermediate results in memory; therefore, Spark is much more efficient for smaller workloads and large-scale data by offering high speed. While MapReduce is a programming model that uses disk storage. Due to its lower performance, MapReduce offers more significant latency.

Conclusion

In this article, we have discussed  MapReduce vs. Spark by discussing their uses, pros, and cons. In the end, we have also outlined some key differences between the two in tabular format. You can read more such articles on our platform, Coding Ninjas Studio. Also read below-mentioned articles.

You will find straightforward explanations of almost every topic on this platform. So take your coding journey to the next level using Coding Ninjas.

Happy coding! 

Previous article
What are the 5 Vs of Big Data?
Next article
Difference between Apache Spark and Hadoop
Live masterclass