Spark vs Hadoop MapReduce – Comparing Two Big Data Giants

Big data analytics emerged as a requisite for the success of business  and technology. That is why we now have various big data frameworks in  the market to choose from. Apache Spark and Hadoop are two of such big  data frameworks, popular due to their efficiency and applications. While  we do have a choice, picking up the right one has become quite  difficult. Perhaps, performing a downright comparison of the pros and  cons of these tools would be no good as well, since this will not  highlight the particular usefulness of a tool. Instead, this article  performs a detailed Apache Spark vs Hadoop MapReduce comparison,  highlighting their performance, architecture, and use cases.

Before delving into a detailed comparison, we will briefly explore what Spark and Hadoop are.

Let’s start exploring the frameworks.

What is Apache Spark

Apache Spark is a real-time data analytics framework that mainly  executes in-memory computations in a distributed environment. It offers  incredible processing speed, making it desirable for everyone interested  in big data analytics. Spark can either work as a stand-alone tool or can be associated with Hadoop  YARN. Since it flaunts faster data processing, it is suitable for  repeated processing of data sets. Nonetheless, it requires more power.

What is MapReduce, or Hadoop MapReduce

MapReduce is what constitutes the core of Apache Hadoop,  which is an open source framework. The MapReduce programming model lets  Hadoop first store and then process big data in a distributed computing  environment. This makes it capable of processing large data sets,  particularly when RAM is less than data. Hadoop does not have the speed  of Spark, so it works best for economical operations not requiring  immediate results.

Hadoop MapReduce vs Spark – Detailed Comparison

Both Spark and Hadoop serve as big data frameworks, seemingly  fulfilling the same purposes. However, they have several differences in  the way they approach data processing. Here, we draw a comparison of the  two from various viewpoints.

Spark vs MapReduce Performance

At a glance, anyone can randomly label Spark a winner considering the  data processing speed. Nonetheless, delving into the details of the  performance of Hadoop and Spark reveals more facts.

Spark vs Hadoop big data analytics visualization

Apache Spark Performance

As said above, Spark is faster than Hadoop. This is because of its  in-memory processing of the data, which makes it suitable for real-time  analysis. Nonetheless, it requires a lot of memory since it involves  caching until the completion of a process. So, if the data fits well  into memory, Spark can become the right option.

MapReduce Performance

Hadoop processes data by first storing it across a distributed  environment, and then processing it in parallel. The software is  basically designed to process data gathered from various sources. Thus,  it takes quite some time to show results. In other words, Hadoop should  not be considered for data processing where faster results are needed.

However, if speed and time are not critical, then Hadoop outperforms  Spark Apache as it conveniently handles large data sets, is capable of  handling hardware failures, and does not require a lot of processing  memory. Besides, unlike Spark, it does not require caching, thus saving  up memory.

Hadoop vs Apache Spark Language

Hadoop MapReduce and Spark not only differ in performance but are  also written in different languages. Hadoop is usually written in Java  that supports MapReduce functionalities. Nonetheless, Python may also be  used if required.

On the other hand, Apache Spark is mainly written in Scala. But‎, it  also comes with APIs for ‎Java‎, ‎Python‎, R, and SQL. Hence, it offers  more options to the developers.

Spark and MapReduce Architecture

Spark and MapReduce both perform data analytics in a distributed  computing environment. However, both the software process a given  dataset differently. Below we explain and compare the architecture when  it comes to Spark vs MapReduce.

Apache Spark Architecture

Spark is a clustered computing system that has RDD (Resilient  Distributed Dataset) as its fundamental data structure. This data  structure makes Spark resilient to faults and failure when processing  data distributed over multiple nodes and managing partitioned datasets  of values. It is RDD’s capability to exploit the power of multiple nodes  in a cluster that makes it faster and tolerant to faults.

While executing data analysis, Spark Apache manages the distribution  of datasets over various nodes in a cluster, creating RDDs. RDD is an  immutable collection of objects that may be lazily transformed via  Directed Acyclic Graph (DAG). Each dataset in an RDD is divided into  multiple logical partitions facilitating in parallel computation across  different nodes in the cluster.

Inside the cluster, the master node holds the driver program  where the Spark Context is created for smooth execution of all Spark  functionalities. The Spark Context collaborates with the Cluster Manager to manage a task. Besides, the Spark architecture also consists of Worker Nodes that hold data cache, perform task execution, and return the results to  the Spark Context. The number of Worker Nodes may be increased as  desired for faster processing. However, it will require more space.

Apache Spark Architecture Diagram

The following diagram illustrates Spark architecture. Whenever an RDD  is created in the Spark Context, it is then further distributed to the Worker Nodes for task execution alongside caching. The Worker Nodes, after task execution, send the results back to the Spark Context.

Apache Spark architecture

Hadoop MapReduce Architecture

Hadoop comprises of two core components – HDFS (Hadoop Distributed  File System) and YARN (Yet Another Resource Negotiator). As the name  implies, HDFS manages big data storage across multiple nodes; while YARN  manages processing tasks by resource allocation and job scheduling.

The HDFS architecture is based on two main nodes – a NameNode, and multiple DataNodes.  These nodes exhibit a master/slave architecture where a NameNode serves  as the master managing storage and logging changes to the metadata of  all files in a cluster. While the DataNodes are the slave nodes that  perform NameNode’s commands.

YARN also comprises of two major components that manage the core  functions of resource management and task scheduling. These are the Resource Manager and the Node Managers.  Resource Manager is a cluster-level component (there can only be one  per cluster), whereas Node Managers exist at the node level, making up  several NodeManagers in a cluster.

Hadoop MapReduce Diagram

The following diagram shows the architecture of Hadoop HDFS. The  NameNode saves the metadata of all stored files as well as logs any  changes to the metadata. Whereas, the DataNodes store the actual data,  attend read/write requests and performs NameNode’s instructions  regarding creation, deletion, or replication of blocks.

Hadoop HDFS architecture

In YARN architecture, the Resource Manager allocates resources for running apps in a cluster via Scheduler. Besides, its ApplicationManager component accepts job submissions and negotiates for app execution with the first container.

Node Managers manage containers and track resource utilization. NodeManagers also communicate with ResourceManager for updates.

Hadoop YARN architecture

Hadoop vs Spark Cost

In general, both Hadoop and Spark are free open-source software.  However, developing the associated infrastructure may entail software  development costs. From the viewpoint of Hadoop vs Apache Spark budget,  Hadoop seems a cost-effective means for data analytics. It requires less  RAM and can even work on commodity hardware.

Spark, on the other hand, requires more RAM since it works faster and  does not consume disk I/O. So, it may require pricey systems. However,  as it boasts advanced technology, it requires less computation units,  and this may lower the costs.

Spark vs MapReduce Compatibility

Spark and Hadoop MapReduce are identical in terms of compatibility.  While both can work as stand-alone applications, one can also run Spark  on top of Hadoop YARN. Spark also supports Hadoop InputFormat data  sources, thus showing compatibility with almost all Hadoop-supported  file formats.

Likewise, Hadoop can also be integrated with various tools like Sqoop and Flume.

Apache Spark And Hadoop Security

Spark supports authentication via shared secret. Its security features also include event logging, and it uses javax servlet filters for securing web user interface. Nevertheless, if it runs on  YARN and integrates with HDFS, it may also leverage the potential of  HDFS file permissions, Kerberos, and inter-node encryption.

On the other hand, Hadoop surpasses Apache Spark in terms of  security, as it supports Kerberos authentication. While Kerberos may be  difficult to handle, Hadoop also supports third-party authentication,  such as Lightweight Directory Access Protocol (LDAP). It also offers  conventional file permissions, encryption, and access control lists  (ACLs). Besides, it ensures providing appropriate user permissions for a  job with Service Level Authorization (SLA). Here, Hadoop surpasses  Spark in terms of security features.

MapReduce Vs Spark Use Cases

While Apache Spark and Hadoop process big data in different ways,  both the frameworks provide different benefits, and thus, have different  use cases.

Apache Spark Use Cases

Spark, being the faster, is suitable for processes where quick results are needed. Hence, Spark fits best for:

1. Real-time analysis of big data
2. Fast data processing with immediate results
3. Iterative operations
4. Machine Learning algorithms
5. Graph processing

Hadoop MapReduce Use Cases

As noted above, comparing processing speeds of Apache Spark vs  MapReduce gives Spark an edge over Hadoop. However, Hadoop exhibits  tremendous capability of processing large data sets. Hence, it works  best for:

1. Analysis of archive data
2. Operations involving commodity hardware
3. Data analysis where time factor is not essential
4. Linear data processing of large datasets

Spark And Hadoop Examples

By comparing MapReduce vs Spark practical examples, one can easily  get an idea of how these two giant frameworks are supporting big data  analysis on large scale.

Apache Spark Examples

1. Risk management and forecasting
2. Industrial analysis of big data gathered from sensors for predictive maintenance of equipment
3. Fraud detection and prevention with real-time analysis
4. Delivering more tailored customer experiences by analyzing data related to customer behavioral patterns
5. Predicting stock market trends with real-time predictive analysis of stock portfolio movements

MapReduce Examples

1. Social networking platforms such as Facebook, Twitter, and LinkedIn use MapReduce for data analyses
2. Law enforcement and security agencies use Hadoop for processing huge  datasets of criminal activities gathered over a period of time for  crime prevention
3. Finance, telecom, and health sectors rely on Hadoop for periodic  analysis of big data to fuel future operations based on the gather  customer reviews
4. Improvement of science research with data analysis
5. Data analysis by city and state governments for improving overall  infrastructure, such as by analyzing data related to traffic situations

Hadoop MapReduce Or Apache Spark – Which One Is Better?

Considering the overall Apache Spark benefits, many see the framework  as a replacement for Hadoop. Perhaps, that’s the reason why we see an  exponential increase in the popularity of Spark during the past few  years.

Nonetheless, the outlined comparison clarifies that both Apache Spark  and Hadoop have advantages and disadvantages. Though both frameworks  are used for data processing, they have significant differences with  regards to their approach to data analytics. Both of them are designed  in different languages and have distinct use cases. Therefore, it purely  depends on the users which one to choose based on their preferences and  project requirements.