What is the difference between Spark and MapReduce?

Big data requires very specific tools. Without them, your ability to work with large amounts of data will be greatly hampered. Given that all companies around the world depend on data to remain competitive, it is essential that your company knows (and uses) the right tools for the job.

You might think that such a decision would be limited to choosing the right database for the task. While this is one of the most important choices you will need to make, it will not be the last. In fact, several tools are required to successfully venture into the big data domain.

Two such tools are Spark and MapReduce . What are these tools and what is the difference between them? These are important questions that you must ask and answer. Fortunately, we’re here to help make it easier to answer the looming question: “What’s the difference between Spark and MapReduce?” Both tools are frameworks that have become absolutely crucial for many companies that rely on Big Data, but they are fundamentally different.

Let’s dive deeper and see what the difference is between these two frameworks. We will look at this through the lens of 5 different categories: Data Processing, Crash Recovery, Operability, Performance, and Security. Before we address these questions, let's first find out what these two tools are.

What is spark?

Spark is an open-source, general-purpose, unified analytics engine used to process large amounts of data. Spark's core database processing engine works with libraries for SQL machine learning, computer graphics, and stream processing.

Spark supports Java, Python, Scala, and R and is used by application developers and data scientists to quickly query, analyze, and transform data at scale. Spark is often used for ETL and SQL batch jobs on massive datasets, processing streaming data from IoT devices, various types of sensors and financial systems, as well as machine learning.

What is MapReduce?

MapReduce is a programming model/pattern within the Apache Hadoop framework used to access massive data stores in the Hadoop File System (HDFS), making it a core function of Hadoop.

MapReduce enables concurrent processing by dividing massive sets of data into smaller pieces and processing them in parallel on Hadoop servers to aggregate data from a cluster and return the output to an application.

Data processing

Both Spark and MapReduce excel at processing different types of data. The biggest difference between the two, however, is that Spark includes almost everything you need for your data processing needs, while MapReduce really only excels at batch processing (where it's the best on the market).

So if you're looking for a Swiss army knife of data processing, Spark is what you want. If, on the other hand, you want serious batch processing power, MapReduce is your tool.

Crash recovery

This is an area where the two are quite different. Spark does all data processing in RAM, which makes it very fast, but not very capable of recovering from failures. Should Spark experience a failure, data recovery will be considerably more challenging because the data will be processed in volatile memory.

MapReduce, on the other hand, handles data processing in a more standardized way (on local storage). This means that if MapReduce encounters a failure, it can pick up where it left off when it's back online.

In other words, if you are recovering from a failure (like a power loss), MapReduce is the best option.

Operability

Simply put, Spark is much easier to program than MapReduce. Spark is not only interactive (so developers can run commands and get immediate feedback), but it also includes building blocks to simplify the development process. You'll also find built-in APIs for Python , Java , and Scala.

MapReduce, on the other hand, is considerably more challenging to develop. There is no interactive mode or integrated APIs. To make the most of MapReduce, your developers may need to rely on third-party tools to help with the process.

Performance

If performance is at the top of your list, Spark is the right choice. Because it processes data in memory (RAM) rather than slower local storage, the difference between the two is considerable (with Spark being up to 100 times faster than MapReduce ).

The only caveat is that due to the nature of in-memory processing, if you lose power to a server you will lose data. However, if you need to squeeze out as much speed as possible, you can't go wrong with Spark.

Security

This one is quite simple. When working with Spark, you will find very fewer security tools and features, which can make your data vulnerable. And while there are methods to better secure Spark (like Kerberos authentication), it's not exactly an easy process.

On the other hand, both Knox Gateway and Apache Sentry are readily available for MapReduce to help make the platform considerably more secure. While it takes effort to secure Spark and MapReduce, you will find the latter more secure out of the box.

Conclusion

To make the choice simple: If you want speed, you want the Spark. If you want reliability, you want MapReduce. It really can be seen through such a basic lens. Either way, you'll want to consider one of these tools if you're serious about Big Data.