Although not immediately obvious, C++ is used in Big Data development along with Java, MapReduce, Python, and Scala. For example, if you’re using a Hadoop framework, it will be implemented in Java, but MapReduce applications can be written in C++, Python, or R.
C++ keeps popping up in the data science space as it’s a relatively simple, but powerful language. When you need to compute large data sets quickly and your algorithm isn’t predefined, C++ can help. But whenever C++ is used, pointers need to be used correctly and header files need to be complete.
On a single server, you also can use it to duplicate for reliability, but without investing in backfilling, replicas, and replaying persistent message queues (until it’s in seven digits of active users for a four digit QPS requirement).
For example, for dynamic load balancing or a highly efficient adaptive caching layer, C++ will be the best language to use.
C++ Enhances Processing Speed
When complex machine learning algorithms are involved, large terabyte or petabyte data sets need to be processed reasonably quickly. Further, more often than not cluster computing or parallel processing techniques will need to be used.
It’s the only language where 1GB+ data can be processed in a second. Further, you can retrain and apply predictive analytics in real-time, maintain consistency of the system of record, and serve four digit QPS of a production RESTful API.
Data scientists often use C++ to write big data frameworks and libraries. These are then used by other languages as well.
C++ Enables System Programming
Before it was rewritten in Java, Google’s MapReduce was written in C++. Further, MongoDB was also written in C++. When it comes to deep learning and deep neural networks, C++ is one of a handful of languages that can be used to write deep learning algorithms.
This is mainly because scientists are quite fond of C++ libraries. The other language libraries that they are fond of are Python libraries. Most of the deep learning algorithms require implementations in C++. For example, Caffe is a popular deep learning algorithm repository and machine learning software.
Resource Consumption and Cost
Typically C and C++ applications need less capacity and electric power than virtual machine languages. This helps to reduce CapEx and OpEx as well as server farm cost. Speaking of costs, C++ also reduces the total cost of development.
When it comes to resource management, C++ provides features that other languages lack. Further, you also have access to extensive templates to write generic code.
Although the libraries available for C++ aren’t as good as the ones available for Java, several third-party developers have built their own (for example Boost and Dlib). The lack of standard threading libraries also forces the programmer to figure out how to best utilize the system resources (like MapR-FS and RIBS2).
The Emergence of Python
Having said all this about C++ in Big Data, we must also acknowledge the emergence of Python development. In recent years, Python has grown in popularity as a flexible, easy to use, comprehensive language. The primary reason for this is because the core language of Python is designed to make programming easier, faster, and makes maintenance simple.
Further, libraries like the NumPy and SciPy have a solid foundation for statistical, numerical, and matrix computations. In comparison, there isn’t a library for C++ that can come even close. It’s also a general purpose language, so there are loads of standard libraries for typical programming tasks (although C++ is also a general purpose language, as mentioned before, it lacks standard libraries).
Python generators can also connect several data processing functions in a row and push the data between them. It can do this without materializing all the intermediate data sets in memory. So when you want to process large quantities of data, it can be very useful.
Further, Python (like Matlab and R) comes with an interactive interpreter. So you can easily experiment with data sets while keeping all the structures in memory permanently. This means that you won’t have to restart and enter the code again with every modification.
The interactive interpreter can also go a long way to help with visualizations. But although Python is a high-level interpretive language, it’s much slower than C++. So it provides several opportunities to optimize critical parts of code by rewriting it in C++ or C. For example, the standard libraries like NumPy are optimized this way. Additionally, PyPy or Cython can also be used to optimize custom application code.
Although other programming languages may have overtaken C++ in the data science space, it’s still going to be lurking in the shadows as it has a critical role to play (at least for the time being).
And what do you think are the pitfalls of using C++ on Big Data projects?