PySpark Programming

PySpark is a Python API for Spark released by the Apache Spark community to support Python with Spark. Using PySpark, one can easily integrate and work with RDDs in Python programming language too. There are numerous features that make PySpark such an amazing framework when it comes to working with huge datasets. Whether it is to perform computations on large datasets or to just analyze them, Data Engineers are switching to this tool.

Key Features of PySpark

• Real-time computations: Because of the in-memory processing in the PySpark framework, it shows low latency.
• Polyglot: The PySpark framework is compatible with various languages such as Scala, Java, Python, and R, which makes it one of the most preferable frameworks for processing huge datasets.
• Caching and disk persistence: This framework provides powerful caching and great disk persistence.
• Fast processing: The PySpark framework is way faster than other traditional frameworks for Big Data processing.
• Works well with RDDs: Python programming language is dynamically typed, which helps when working with RDDs.

Why PySpark? The Need of PySpark

More solutions to deal with big data, better. But then, if you have to switch between tools to perform different types of operations on big data, then having a lot of tools to perform a lot of different tasks does not sound very appealing, does it?

It just sounds like a lot of hassle one has to go through to deal with huge datasets. Here came some scalable and flexible tools to crack big data and gain benefits from it. One of those amazing tools that help handle big data is Apache Spark.

Now, it’s no secret that Python is one of the most widely used programming languages among Data Scientists, Data Analysts, and many other IT experts. The reason for this could be that it is simple and has an interactive interface or it is a general-purpose language. Therefore, it is trusted by Data Science folks to perform data analysis, Machine Learning, and many more tasks on big data. So, it’s pretty obvious that combining Spark and Python would rock the world of big data, isn’t it?

That is exactly what the Apache Spark community did when they came up with a tool called PySpark, which is basically a Python API for Apache Spark.

Spark with Python vs Spark with Scala

As it is already discussed, Python is not the only programming language that can be used with Apache Spark. Data Scientists already prefer Spark because of the several benefits it has over other Big Data tools, but choosing which language to use with Spark is a dilemma that they face.

Being one of the most popular frameworks when it comes to Big Data Analytics, Python has gained so much popularity that you wouldn’t be shocked if it became the de-facto framework for evaluating and dealing with large datasets and Machine Learning in the coming years.

The most used programming languages with Spark are Python and Scala. Now if you are going to learn PySpark (Spark with Python), then it is important that you know why and when to use Spark with Python, instead of Spark with Scala. In this section, the basic criteria, one should keep in mind while making the choice between Python and Scala to work on Apache Spark, are explained.

Now, see the comparison between Python and Scala in detail:

Advantages of using PySpark: 

• Python is very easy to learn and implement. 
• It provides a simple and comprehensive API. 
• With Python, the readability of code, maintenance, and familiarity is far better. 
• It features various options for data visualization, which is difficult using Scala or Java.

What are the Benefits of Using PySpark?

Following are the benefits of using PySpark. Let’s talk about them in detail

In-Memory Computation in Spark: With in-memory processing, it helps you increase the speed of processing. And the best part is that the data is being cached, allowing you not to fetch data from the disk every time thus the time is saved. For those who don’t know, PySpark has DAG execution engine that helps facilitate in-memory computation and acyclic data flow that would ultimately result in high speed.

Swift Processing: When you use PySpark, you will likely to get high data processing speed of about 10x faster on the disk and 100x faster in memory. By reducing the number of read-write to disk, this would be possible.

Dynamic in Nature: Being dynamic in nature, it helps you to develop a parallel application, as Spark provides 80 high-level operators.

Fault Tolerance in Spark: Through Spark abstraction-RDD, PySpark provides fault tolerance. The programming language is specifically designed to handle the malfunction of any worker node in the cluster, ensuring that the loss of data is reduced to zero.

Real-Time Stream Processing: PySpark is renowned and much better than other languages when it comes to real-time stream processing. Earlier the problem with Hadoop MapReduce was that it can manage the data which is already present, but not the real-time data. However, with PySpark Streaming, this problem is reduced significantly.

When it is Best to use PySpark?

Data scientists and other Data Analyst professionals will benefit from the distributed processing power of PySpark. And with PySpark, the best part is that the workflow for accomplishing this becomes incredibly simple like never before. By using PySpark, data scientists can build an analytical application in Python and can aggregate and transform the data, then bring the consolidated data back. There is no arguing with the fact that PySpark would be used for the creation and evaluation stages. However, things get tangled a bit when it comes to drawing a heat map to show how well the model predicted people’s preferences.

Running with PySpark

PySpark can significantly accelerate analysis by making it easy to combine local and distributed data transformation operations while keeping control of computing costs. In addition, the language helps data scientists to avoid always having to downsample large sets of data. For tasks such as building a recommendation system or training a machine-learning system, using PySpark is something to consider. It is important for you to take advantage of distributed processing can also make it easier to augment existing data sets with other types of data and the example it includes like combining share-price data with weather data.