MapReduce Vs Spark: Amongst multiple large data frameworks available on the market, choosing the right is a difficult challenge.
A classic approach to compare the advantages and disadvantages of each platform is unlikely to help, as businesses should consider each and every framework from the perspective of their particular needs.
First of all, what is Spark and Hadoop MapReduce?
Spark: This is an open source big data framework, providing a faster and more general-purpose data processing engine.
Spark is originally designed for fast calculations. It also includes a wide range of workloads – for example, batch, interactive, repeater, and streaming.
Hadoop MapReduce: It’s an open-source framework for writing applications. It also processes structured and unorganized data stored in HDFS. Hadoop MapReduce is designed in a way to process a large amount of data on a group of commodity hardware.
MapReduce can process data in batch mode.
A quick glance at the market situation
Hadoop and Spark, both are open source projects by Apache Software Foundation and the two are major products in big data analytics.
Hadoop is leading a big data market for more than 5 years. As per a recent market research, installed base of Hadoop extends to 50,000+ customers, while Spark has only 10,000+ installations.
However, the popularity of Spark jumped in recent times to overcome Hadoop in only one year.
A past installation growth rate (2016/2017) shows that the trend is still going on. Spark is performing better than Hadoop with 47% vs 14% respectively.
The key difference between Hadoop MapReduce and Spark
To make comparisons fair and square, here we will contrast Spark with Hadoop MapReduce since both of them are responsible for data processing.
In fact, the important difference between them lies in the processing approach: Spark does it in-memory, while Hadoop MapReduce has to read from and write to a disk.
Consequently, the speed of processing varies greatly – Spark maybe 100 times faster. However, the quantity of processed data is also different: Hadoop MapReduce is capable of working with far larger data sets than Spark.
Now, let’s take a look at the tasks that are good for each framework.
Tasks Hadoop MapReduce is good for:
Linear processing of huge datasets
Hadoop MapReduce allows parallel processing of large amounts of data.
It breaks a large part into small sizes to separately process on different data nodes and automatically gathers the results across several nodes to return a single result.
The resulting dataset is larger than the available RAM? O that occasion, Hadoop MapReduce may beat Spark.
Economic solutions, if no immediate results are expected
A study considers MapReduce a good solution on the condition, speed of processing is not important.
It was observed that data processing done during night hours makes sense to consider using Hadoop MapReduce.
Tasks Spark is good for:
Fast data processing
- Spark is faster in terms of In-memory processing than Hadoop MapReduce – up to 100 times for data in RAM and up to 10 times for data in storage.
- MapReduce is native batch processing engine of Hadoop. Several components or layers (like YARN, HDFS, etc.) in latest versions of Hadoop allows easy processing of batch data.
- Seeing that MapReduce is about permanent storage, it stores data on disk, which means it can handle large datasets.
- With the condition that task is to process data repeatedly – Spark defeats Hadoop MapReduce.
- Hadoop: Apache Hadoop offers batch processing. Hadoop develops a great deal in creating new algorithms and component stack to improve access to batch processing on a large scale.
- Sparc’s Resilient Distributed Datasets (RDDs) enable several map operations in memory, while the Hadoop MapReduce has to write temporary results to a disk.
Near real-time processing
- If a business needs instant insights, then they should choose Spark and its in-memory processing.
- Spark: It processes real-time data, i.e. data approaching from real-time event streams at the rate of millions of events per second, such as Twitter and Facebook data. The strength of Spark is to process live streams efficiently.
- Hadoop MapReduce: MapReduce has its failures when it comes to real-time data processing, as it was designed to perform batch processing on vast amounts of data.
Spark’s computational/estimation model is good for iterative or repetitive computations that are typical in graph processing. And Apache Spark consists of GraphX – an API for graph computation.
- Spark has MLlib – a built-in machine learning library, whereas talking about Hadoop, it needs a third-party to provide it.
- MLlib has unthinkable algorithms that also run in memory. But if required, the Spark specialists will tune and adjust them to work as per your needs.
Spark can create all combinations faster, as speed is the reason behind it. Although Hadoop may rank better when it comes to joining of very large datasets that require a lot of shuffling and sorting is needed.
Ease of Use in Spark
- When talking of user-friendly, Spark is easier to use than Hadoop. As Spark has user-friendly APIs for Scala (its native language), Java, Python, and Spark SQL.
- An interactive REPL (Read-Eval-Print-Loop) allows Spark users to get immediate feedback for commands.
- Hadoop: Hadoop, in contrast, is written in Java, is difficult to program, and requires abstractions.
- Although there is no interactive mode available with Hadoop MapReduce, tools like Pig and Hive, making it easier for users to work with it.
Security in Spark
- Spark’s security is currently in its immaturity stage, offering only authentication support through shared secret (password authentication).
- Hadoop MapReduce: Hadoop MapReduce possesses better security features than Spark. Hadoop supports Kerberos authentication, a good security feature but difficult to manage.
Examples of practical applications
We examined several examples of practical applications and concluded that Spark is likely to perform far better than MapReduce in all applications below, thanks to fast, or even closer to real-time processing. Let’s look at examples.
1. Customer Segmentation
Analyze customer behavior and identify customers’ segments that display the same behavior patterns, help businesses understand customer preferences and create a unique customer experience.
2. Risk management
Predicting various potential scenarios can help managers make the right decisions by choosing a non-risky option.
3. Detect Real-Time Fraud
After the system is skilled on historical data with the help of machine-learning algorithms, it can use these findings to identify or predict an anomaly in real time that may indicate the possible fraud.
4. Industrial Big Data Analysis
This is also about the detection and prediction of anomalies, but in this case, these anomalies relate to the machinery breakdown. A well-configured system collects data from the sensor to detect pre-failure conditions.
Hadoop and Spark, both come in the category of open-source projects, therefore they are for free.
When it comes to cost, organizations need to see their requirements.
If it’s about processing large amounts of large data, Hadoop will be cheaper because the hard disk space comes at a very low rate than memory space.
The compatibility of both Hadoop and Spark is good with each other. The compatibility of Spark is with all data sources and file formats supported by Hadoop.
Therefore, it’s not wrong to say that the compatibility of Spark with data types and data sources is similar to that of Hadoop MapReduce.
Which framework to choose?
It’s your particular business requirements which should determine the choice of a framework. Linear processing of huge datasets has the advantage of Hadoop MapReduce, while Spark provides faster performance, iterative processing, real-time analytics, graph processing, machine learning, and more.
In many cases, Spark is far better and much more advanced than Hadoop MapReduce. The good news is, Spark is fully compatible with Hadoop Eco-System and works comfortably with Hadoop Distributed File System, Apache Hive etc.
Spark can handle any type of requirement you ask for, whether it’s batch, interactive, iterative, streaming or graph, whereas MapReduce limits to batch processing.