If you need to process big data, you are likely weighing the pros and cons of Spark vs MapReduce. It can be difficult to determine which of these data processing tools is superior since both of them have features that the other doesn’t. The question shouldn’t be which data framework, Hadoop MapReduce or Apache Spark, is best, but rather which framework best fits your data analytics needs.
Hadoop MapReduce has been a staple in the big data processing field with very little competition until Apache Spark was introduced in 2014. While Spark may be the newer technology, MapReduce still has advantages over Spark in certain areas. Let’s take a closer look at MapReduce and Spark, explore the differences between these two data frameworks, and determine which one is best in certain situations.
What are the Differences Between MapReduce and Spark?
The main differences between these two data frameworks can be boiled down to five major categories. They are as follows:
- Data Processing
- Failure Recovery
Let’s dive into these five categories and explore the differences between MapReduce and Spark in greater detail. Along the way, I’ll highlight some key aspects of these differences that make one or the other a better choice for certain data processing tasks.
Spark was designed to be faster than MapReduce, and by all accounts, it is; in some cases, Spark can be up to 100 times faster than MapReduce. Spark uses RAM (random access memory) to process data and stores intermediate data in-memory which reduces the amount of read and write cycles on the disk. This makes it faster than MapReduce. MapReduce reads and writes data directly from the disk. However, speed is not the only performance factor that you should take into consideration.
For Spark to run effectively it requires a lot of memory. Spark caches processes into the memory and holds them until told otherwise. If Spark is being used with other resource-demanding services, it could suffer major performance deterioration. Additionally, Spark’s performance capabilities can also suffer if the data sources are too large to fit entirely into memory.
MapReduce doesn’t support data caching, but it can run alongside other services with minimal to no performance downturn since MapReduce kills its processes immediately after completing them.
When it comes to performance, MapReduce and Spark both have their advantages. If your data fits into the amount of memory space you have, or you have a dedicated cluster, Spark is likely the best choice for your data framework. On the other hand, if you have a large amount of data that will not fit neatly into memory, and you need your data framework to run effectively with other services, MapReduce is the better choice for you.
Spark is easier to program than MapReduce. Spark is far more interactive and has simple building blocks that make creating user-defined functions easy. It also comes loaded with pre-built APIs for Python, Java, and Scala. In addition, Spark also includes the powerful Spark SQL and a resilient distributed dataset that makes processing data easy.
MapReduce is often said to be very difficult to program. This data framework is written in Java, and it does not have an interactive mode for users to run commands and get immediate feedback. Despite the perceived difficulty of programming in MapReduce, there are many tools that can run MapReduce without needing to be programmed. Hadoop has a number of these tools available. Additional tools like Apache Pig make it easier to program MapReduce. However, developers say that it can take some time to learn the syntax.
Spark is a lot easier to program than MapReduce. Still, there are several tools available to make programming MapReduce easier. If you are only making your decision based on how easy it is to use, Spark is likely to be your best choice. Spark also includes interactive tools that MapReduce cannot offer users.
MapReduce and Spark are both great at different types of data processing tasks. Spark comes loaded with a Machine Learning library which helps it do more than just process plain data. This powerful tool allows Spark to excel at graph processing and real-time data processing. Spark is also very capable of handling batch processing.
MapReduce excels at batch processing but if you want graph processing or real-time data processing features, you will need to use an additional platform. On the downside, MapReduce doesn’t have a Machine Learning feature. MapReduce formerly had Apache Mahout for Machine Learning, but Mahout has since been abandoned in favor of Spark.
Spark is great because it allows you to have one data framework for all of your data processing needs. There is no data processing task that Spark cannot handle. MapReduce is not as agile as Spark when it comes to being able to perform a wide variety of data processing tasks. However, MapReduce could be the best batch processing tool ever created.
Since MapReduce relies on hard drives instead of RAM, it is better suited for recovery after failure than Spark. If Spark crashes during the middle of a data processing task, it will need to start over when it comes back online. This could cost you a lot of lost time if Spark crashes in the middle of a large data processing task.
On the other hand, if MapReduce experiences a failure while working on a task, it can pick up where it left off when it comes back on. Since MapReduce relies on hard drives it is able to hold its place if it crashes in the middle of a task.
You can never be too secure. This is why security should always be considered when choosing any tech solution for your business. Bottom line is that Spark is less secure than MapReduce. The default security setting for Spark seems to be off. There are fewer security tools to work with in Spark, and this could potentially leave you and your data vulnerable to attacks.
There are ways to make Spark more secure like Kerberos authentication and encryption between nodes, but powerful security tools like Knox Gateway and Apache Sentry are only available with MapReduce. Although there has been talk about adding Apache Sentry support to Spark.
The positive news is that Spark is open-source which means Spark developers can create and add their own security features to the project. MapReduce is more developed than Spark when it comes to security. Over time I expect this paradigm to shift as Spark continues to gain popularity.
Apache Spark is the newer, faster technology. The capabilities Spark provides data scientists are very exciting, but Spark still has a lot of room for improvement and growth. While MapReduce may be older and slower than Spark, it is still the better tool for batch processing. Additionally, MapReduce is better suited to handle big data that doesn’t fit in memory.
As time passes, I fully expect Apache Spark to be the go-to data processing tool, but at this point in time, the Spark vs MapReduce question is still a toss-up. As we’ve discussed, MapReduce still has its advantages over Spark. You will need to choose a data framework that best meets your needs.
If you need to do a little bit of everything, Spark is likely the choice for you, but if you need a batch processing tool that can handle big data, MapReduce is the better choice. The importance of data processing cannot be overstated whether you are working on app development or building a website. At some point in time, you will likely need to process data. I hope this piece will be helpful to you when deciding which data processing tool to use.