Pyspark Vs Apache Spark
Apache Spark has become so popular in the world of Big Data. Basically, a computational framework that was designed to work with Big Data sets, it has gone a long way since its launch on 2012. It has taken up the limitations of MapReduce programming and has worked upon them to provide better speed compared to Hadoop. Spark is written in Scala. It supports other programming languages such as Java, R, Python.
MapReduce is the programming methodology of handling data in two steps: Map and Reduce. In the first step, the data sets are mapped by applying a certain method like sorting, filtering. In the second step, the data sets are reduced to a single/a few numbered datasets. This type of programming model is typically used in huge data sets.
Imagine if we have a huge set of data flowing from a lot of other social media pages. Our goal is to find the popular restaurant from the reviews of social media users. We might need to process a very large number of data chunks.
Firstly, we will need to filter the messages for words like ‘foodie’,’restaurant’,’dinner’,’hangout’,’night party’,’best brunch’,’biryani’,’team dinner’. These data are siphoned into multiple channels, where each channel is capable of processing these information. Here each channel is a parallel processing unit. Hence, a large chunk of data is split into a number of processing units that work simultaneously. This divide and conquer strategy basically saves a lot of time. Here, the messages containing these keywords are filtered. Each filtered message is mapped to its appropriate type. Here, the type could be different types of cuisines, like Arabian, Italian, Indian, Brazilian and so on. Each message is again mapped to its kind accordingly. This is how Mapping works. Again, type can include places like cities, famous destinations.
Next step is to count the reviews of each type and map the best and popular restaurant based on the cuisine type and place of the restaurant. This is how Reducing applies.
Features of Spark
- Spark makes use of real-time data and has a better engine that does the fast computation.
- Very faster than Hadoop.
- It uses an RPC server to expose API to other languages, so It can support a lot of other programming languages. PySpark is one such API to support Python while working in Spark.
PySpark is an API developed and released by the Apache Spark foundation. The intent is to facilitate Python programmers to work in Spark. The Python programmers who want to work with Spark can make the best use of this tool. This is achieved by the library called Py4j.
Like Spark, PySpark helps data scientists to work with (RDDs) Resilient Distributed Datasets. It is also used to work on Data frames. PySpark can be used to work with machine learning algorithms as well.
PySpark vs Spark
|A tool to support Python with Spark||A data computational framework that handles Big data|
|Supported by a library called Py4j, which is written in Python||Written in Scala. Apache Core is the main component.|
|Developed to support Python in Spark||Works well with other languages such as Java, Python, R.|
|Pre-requisites are Programming knowledge in Python. Understanding of Big data and Spark||Pre-requisites are programming knowledge in Scala and database|