A Beginner’s Guide to Learning PySpark for Big Data Processing
Here’s your complete beginner-friendly guide to getting started with learning PySpark for big data.
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Did you know that, according to Linkedin, over 24,000 Big Data jobs in the US list Apache Spark as a required skill? Learning Spark has become more of a necessity to enter the Big Data industry. One of the most in-demand technical skills these days is analyzing large data sets, and Apache Spark and Python are two of the most widely used technologies to do this. Python is one of the most extensively used programming languages for Data Analysis, Machine Learning, and data science tasks. Apache Spark is one of the most popular frameworks for managing and dealing with Big Data. What if you could use both these technologies together? This is where Apache Spark PySpark comes in.
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Here’s What You Need to Know About PySpark
This blog will take you through the basics of PySpark, the PySpark architecture, and a few popular PySpark libraries, among other things. Finally, you'll find a list of PySpark projects to help you gain hands-on experience and land an ideal job in Data Science or Big Data. So, let’s get started with the PySpark journey!
What is PySpark?
PySpark is a Python API for Apache Spark that was released to support the collaboration of Apache Spark with Python. Furthermore, PySpark allows you to interact with Resilient Distributed Datasets (RDDs) in Apache Spark and Python. You can accomplish this using the Py4j library. PySpark Py4J is a popular package built into PySpark that allows Python to interact with JVM objects dynamically. PySpark comes with several libraries that can help you write effective programs. Before we delve into further details, let’s look at what the PySpark logo looks like; it is formed by merging the Python and Apache Spark logos.
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View All ProjectsWhy use PySpark?
PySpark is a handy tool for data scientists since it makes the process of converting prototype models into production-ready model workflows much more effortless. PySpark allows us to create model workflows in cluster environments for model training and serving. Exploratory data analysis (EDA) is crucial in determining data collection structure in a data science workflow, and PySpark can be used for exploratory data analysis and building machine learning pipelines. Another reason to use PySpark is that it has the benefit of being able to scale to far more giant data sets compared to the Python Pandas library.
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PySpark Applications-How are Businesses leveraging PySpark?
Some of the industry giants like Yahoo, Netflix, etc., are leveraging various functionalities of Spark PySpark. Yahoo utilizes Apache Spark's Machine Learning capabilities to customize its news, web pages, and advertising. They use Pypark to determine what kind of news readers are interested in reading. Also, PySpark helps them categorize the news stories to determine who would be interested in reading each news category.
Netflix amazes its users with fantastic recommendations every time they watch they are using the platform. But how does this happen? Netflix uses the collaborative filtering feature offered by PySpark. Apart from that, Runtastic also uses Spark PySpark for Big Data sanity checks. Their team uses Python's unittest package to keep things simple and manageable and creates a task for each entity type (e.g., sports activities).
But what makes Python PySpark so valuable to all of these businesses? Let us find out!
Features of PySpark
Features that contribute to PySpark's immense popularity in the industry-
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Real-Time Computations
PySpark emphasizes in-memory processing, which allows it to perform real-time computations on huge volumes of data. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency.
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Multi-Language Support
PySpark platform is compatible with various programming languages, including Scala, Java, Python, and R. Because of its interoperability, it is the best framework for processing large datasets.
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Caching and Disc consistency-
The PySpark API has excellent caching and disc consistency mainly due to the easy programming layers.
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Easy Processing-
PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage.
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Compatible with RDD-
PySpark is a Python API, and Python is a dynamically typed programming language that is beneficial for working with RDD.
When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS, AWS S3, and various other file systems. Different machine learning and graph libraries are available in PySpark, and it is compatible with multiple cluster managers (Spark, Yarn, Mesos, etc.).
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The PySpark Architecture
The PySpark architecture consists of various parts such as Spark Conf, RDDs, Spark Context, Dataframes, etc.
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Spark Conf
SparkConf helps establish a few setups and parameters to execute a Spark application on the local cluster or a dataset. It contains the settings needed to launch a Spark application.
There are plenty of SparkConf features you can use, such as-
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To set a configuration property, use set(key, value).
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To set the master URL, use setMaster(value).
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To name an application, use setAppName(value).
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To access the configuration value, use get(key, defaultValue=None).
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To set the Spark installation path on worker nodes, use setSparkHome(value).
Any Spark program begins by generating a SparkContext object, instructing the application to connect to a cluster. You must implement SparkConf to store the application's configuration data in the SparkContext instance to finish the job. Let's have a look at what SparkContext can do.
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Spark Context
The Apache Spark functionality is accessed through the SparkContext portal. In any Spark operator program, the production of SparkContext is a significantly more crucial phase. The Resource Manager (YARN/Mesos) allows the Spark Application to interact with the Spark Cluster. SparkContext can only be generated once SparkConf is formed. The Spark driver program can transmit a configuration parameter to the SparkContext via SparkConf.
Source: Apache Spark
When you execute a Spark application, a driver program with the primary function runs, and your SparkContext is created here. The driver program then performs the operations inside the executors on worker nodes.
The parameters of a SparkContext are listed below-
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Master- It refers to the URL of the cluster which connects to Spark.
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appName- It’s the name of your task.
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sparkHome- This is the Spark installation directory.
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pyFiles- The.zip or.py files to transmit to the cluster and include in the PYTHONPATH.
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Environment- Represents variables in the context of worker nodes.
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batchSize- A single Java object (batchSize) represents the number of Python objects. Set 1 to deactivate batching, 0 to use an infinite batch size, or -1 to use an automated batch size based on object sizes.
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Serializer- It is an RDD serializer.
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Conf- It configures all the Spark properties with an L{SparkConf} object.
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JSC- Represents the JavaSparkContext instance.
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profiler_cls- This class belongs to the custom Profiler and is used to do the profiling. The default profiler is pyspark.profiler.BasicProfiler.
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Resilient Distributed Databases - RDDs
The components that run and operate on numerous nodes to execute parallel processing on a cluster are RDDs (Resilient Distributed Datasets). RDDs are irrevocable entities, which means you can't change them after creation. RDDs are also fault-tolerant; thus, they will automatically recover in the event of a failure.
RDD is an acronym for-
Resilient- It is fault-tolerant and capable of regenerating data in the event of a failure.
Distributed- The data in a cluster is distributed among the various nodes.
Dataset- It refers to a collection of segregated data that contains values.
RDD uses a key to partition data into smaller chunks. The advantage of breaking data into manageable blocks is that if one executor node crashes, another node can still process the data. Because the same data blocks are duplicated over numerous executor nodes, these can quickly recover from any failures. RDD allows you to efficiently conduct functional calculations against a dataset by linking together multiple nodes.
To generate RDDs, PySpark offers two choices: loading an external dataset or distributing a set of objects. The most straightforward approach to building RDDs is the parallelize() function, which takes an existing program collection and passes it to the Spark Context.
There are mainly two types of operations you can perform with RDDs-
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Transformations: These operations are used to generate a new RDD. Map, flatMap, filter, distinct, reduceByKey, mapPartitions, and sortByKey are some of the transformation operations used on RDDs.
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Actions: These operations are used on an RDD to enable Apache Spark to perform computations and then provide the result to the driver. Collect, collectAsMap, reduce, countByKey/countByValue are some of the action operations used on RDDs.
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PySpark SQL and Dataframes
A dataframe is a shared collection of organized or semi-structured data in PySpark. This collection of data is kept in Dataframe in rows with named columns, similar to relational database tables. It also has several characteristics in common with RDD, such as being immutable, distributed, and following lazy evaluations. It accepts various file types, including JSON, CSV, TXT, and others. You can load it from existing RDDs or by setting the schema dynamically.
PySpark SQL is a Spark module for performing SQL-style analysis on large amounts of structured or semi-structured data. PySpark SQL combines relational processing with the functional programming API of Spark. With PySparkSQL, we can also use SQL queries to perform data extraction. PySparkSQL introduced the DataFrame, a tabular representation of structured data that looks like a table in a relational database management system. PySpark SQL supports a variety of data sources, allowing SQL queries to be combined with code modifications, resulting in a powerful big data tool. PySpark SQL provides all existing and new users with consistent shared access to various data sources such as Parquet, JSON, and many others. PySpark SQL also supports a diverse set of Big Data (machine learning) algorithms such as Classification, Clustering, Linear Regression, etc.
Some of the essential elements of PySpark SQL include UDFs, Filter, Joins, etc.
PySpark UDF
The PySpark UDF (User Defined Function) in Spark SQL is used to define a new Column-based function. It expands the Spark SQL vocabulary for manipulating datasets. UDFs in PySpark is similar to UDFs in traditional databases. You must build a Python function and wrap it in PySpark SQL UDF() or register it as a UDF and use it on DataFrame and SQL. UDFs expand PySpark's operations and reuse them across various DataFrames. Once developed, you can reuse UDFs across several DataFrames and SQL expressions.
PySpark Filter
PySpark Filter is a PySpark function that deals with filtered data in a Spark Data Frame when it's needed. Data cleaning is a crucial stage when working with data in PySpark, and PySpark Filter provides the functionality to accomplish this. PySpark Filter is used in conjunction with the Data Frame to filter data so that just the necessary data is used for processing, and the rest can be scarded. This allows for faster data processing since undesirable data is cleansed using the filter operation in a Data Frame.
PySpark Joins
The PYSPARK JOIN Operation is a means to merge Data Frames in a spark application. PySpark JOIN is essential when dealing with massive datasets or nested data that emerges from two Data Frames in Spark. In a PySpark application, a join operation provides the ability to join multiple data frames or work on multiple rows of a Data Frame.
It supports all fundamental SQL join types, including INNER, LEFT OUTER, RIGHT OUTER, LEFT ANTI, LEFT SEMI, CROSS, and SELF JOIN.
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Spark Files
PySpark allows you to use sc.addFile (your default SparkContext) to upload your files. SparkFiles.get() can be used to obtain the path of the working directory. In addition, the following class methods are available in SparkFiles to resolve the path to the files added by SparkContext.addFile()-
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get(filename): It defines the path of the file uploaded using SparkContext.addFile().
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getrootdirectory(): The path to the root directory is defined by this class method. It includes the entire file uploaded via the SparkContext.addFile method ().
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PySpark Streaming
PySpark Streaming is a fault-tolerant, scalable system based on the RDD batch model. Spark Streaming accepts a continuous data stream as input from Apache Flume, Kinesis, Kafka, TCP sockets, and others. The streamed data is then split into smaller batches based on the batch interval and sent to the Spark Engine. Complex algorithms described with high-level functions like map, reduce, join, and window are used by Spark Engine to process these data batches. The processed sets are then sent over to databases, filesystems, and live dashboards as the process ends.
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Storage Level
The PySpark StorageLevel determines how RDD should be stored in memory. It also determines whether or not to serialize and replicate RDD partitions. It is responsible for deciding whether RDD should be preserved in memory, on disc, or both in Apache Spark. It includes PySpark StorageLevels and static constants such as MEMORY ONLY.
There are several PySpark StorageLevels to choose from when storing RDDs, such as:
DISK_ONLY: StorageLevel(True, False, False, False, 1)
DISK_ONLY_2: StorageLevel(True, False, False, False, 2)
MEMORY_AND_DISK: StorageLevel(True, True, False, False, 1)
MEMORY_AND_DISK_2: StorageLevel(True, True, False, False, 2)
MEMORY_AND_DISK_SER: StorageLevel(True, True, False, False, 1)
MEMORY_AND_DISK_SER_2: StorageLevel(True, True, False, False, 2)
MEMORY_ONLY: StorageLevel(False, True, False, False, 1)
MEMORY_ONLY_2: StorageLevel(False, True, False, False, 2)
MEMORY_ONLY_SER: StorageLevel(False, True, False, False, 1)
MEMORY_ONLY_SER_2: StorageLevel(False, True, False, False, 2)
OFF_HEAP: StorageLevel(True, True, True, False, 1)
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Broadcast and Accumulator
Apache Spark employs shared variables to perform parallel processing. When the driver delivers a task to the cluster's executor, a copy of the shared variable is sent to each node, allowing it to be used for functions.
Apache Spark can support two different types of shared variables- Broadcast and Accumulator.
Broadcast variables are employed to save the copy of data throughout all nodes. Instead of sending a replica of a read-only variable with tasks, you can keep them stored on each system. The value attribute of a Broadcast variable holds the data and returns a broadcasted result.
Accumulator variables are used to aggregate data using associative and commutative processes. You can utilize an accumulator to perform a sum operation ( like in MapReduce). The value attribute of an Accumulator variable is equivalent to the value attribute of a broadcast variable. It saves data and returns the accumulator's value; however, you can only use it in a driver program.
You can check out the official PySpark documentation to have an in-depth understanding of the PySpark architecture.
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Popular PySpark Libraries
MLlib
PySpark MLlib is a machine learning library written in Python. It's a wrapper for PySpark Core that allows you to perform data analysis using machine learning algorithms like clustering, classification, etc. PySpark MLlib enables you to implement various machine-learning methods such as classification, clustering, linear regression, and many others. One of the most notable features of machine learning using PySpark is that it is very scalable and works on distributed systems. MLlib offers extraction, transformation, selection, hashing, and many other features. Also, statistical procedures such as chi-square testing, descriptive statistics, linear algebra, and model evaluation methods are offered as utilities in the PySpark library MLlib.
Some of the packages used in PySpark MLlib are-
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mllib.classification- Spark MLlib offers various binary classification, multiclass classification, and regression analysis methods. These classification algorithms include decision trees, logistic regression, Naive Bayes, etc.
Using both continuous and categorical variables, the spark.mllib implementation supports decision trees for binary and multiclass classification as well as regression. The implementation splits data into rows, allowing millions or even billions of instances to be trained simultaneously.
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mllib.clustering- Unsupervised learning is a technique for extracting references from databases using simply input data and no pre-defined actions. Clustering, an unsupervised learning problem, enables you to group subsets of items based on some measure of similarity.
For instance, the k-means clustering technique separates data points into a predetermined number of groupings. The parallelized version of the k-means++ algorithm, kmeans||, is included in the MLlib implementation.
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mllib.regression- The goal of regression analysis is to identify correlations and dependencies among variables. When working with linear regression models, the interface is identical to when dealing with logistic regression models. There are a variety of regression algorithms available, including Lasso, ridge regression, decision trees, random forests, and gradient-boosted trees. In spark.mllib, logistic regression may be used to predict a binary or multiclass result using binomial or multinomial logistic regression.
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mllib.fpm- Frequent Pattern Matching has been an important topic in data mining research for years now. Frequent pattern matching is often among the initial steps in analyzing a large-scale dataset, mining recurring items, itemsets, subsequences, or other components. The following (hyper-)parameters are used in spark.mllib's FP-growth implementation:
minSupport: the minimal amount of support required for an item set to be labeled as frequent.
minConfidence: the level of confidence required to generate an Association Rule.
numPartition: The number of partitions used to share the labor is referred to as the partition count.
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mllib.stat- The colStats() method in this MLlib package gives summary statistics for RDD. colStats() calculates the minimum, maximum, mean, difference, number of non-zero values, and the total count for each column.
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mllib.recommendation- This package supports model-based collaborative filtering, in which people and products are defined by a small number of latent characteristics that can be used to forecast missing items. The collaborative filtering method fills in the missing items/entries in a user-item association matrix.
The Alternating Least Squares (ALS) approach is used by spark.mllib to identify these latent features.
Graphframes
GraphFrames is a graph processing toolkit that uses the PySpark core and PySparkSQL to provide a set of APIs for performing graph analysis quickly. It is designed for high-performance distributed computing. GraphFrames, like Apache Spark's GraphX package, offer general graph processing. GraphFrames are supported by Spark DataFrames and offer the following benefits:
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GraphFrames provides consistent APIs for Python, Java, Scala languages. All GraphX algorithms are accessible from Python and Java.
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GraphFrames allow users to phrase queries using the familiar and powerful Spark SQL and DataFrames APIs.
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GraphFrames fully supports DataFrame data sources, allowing you to write and read graphs in various formats, including Parquet, JSON, and CSV.
PySpark Projects to Practice in 2022
Mastering PySpark is one of the key skills required to land a Big Data or a Data Science job. Explore some of these fantastic PySpark projects to master PySpark for big data processing -
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PySpark Tutorial - Learn to use Apache Spark with Python
This PySpark tutorial project will teach you about core Spark architecture and lifecycle. It will walk you through the various PySpark operations and teach you how to implement Spark Transformations and Actions. It will help you gain a solid understanding of RDDs and different PySpark optimization techniques. This project will allow you to explore the Spark UI and Spark Submit.
Source Code: PySpark Tutorial - Learn to use Apache Spark with Python
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Create A Data Pipeline based on Messaging Using PySpark Hive
Simulating a complex real-world data pipeline based on messaging is the goal of this PySpark project. PySpark, Hive, HDFS, Kafka, NiFi, Airflow, Tableau, and AWS QuickSight are the technologies used in this project. It requires using NiFi to ingest real-time streaming data from an external API. Furthermore, the complex JSON data is parsed into CSVand stored in HDFS using NiFi.
The project shows you how to use PySpark to transmit parsed data to Kafka for processing and write the data to an output Kafka topic.
Source Code: Create A Data Pipeline based on Messaging Using PySpark Hive
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Deploying auto-reply Twitter handle with Kafka, Spark, and LSTM
This PySpark project shows how to set up an Auto-Reply Twitter Handle that responds to query-related tweets with a trackable ticket ID. Working on this project will give you a better understanding of PySpark UDFs and the implementation of Spark Streaming. This Twitter Support project aims to listen to live tweets with needed tags and publish them to a Kafka topic. Then, the tweets will be ingested by Spark Stream and fed through an NLP pipeline for further processing. The tweets are classified using deep learning models like LSTM based on sentiment and query category. Finally, they are responded to with a custom selected ID using Flask and the tweepy API.
Source Code: Deploying auto-reply Twitter handle with Kafka, Spark, and LSTM
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Event Data Analysis using AWS ELK Stack
This Elasticsearch project uses the AWS ELK stack to analyze streaming event data. For visualization, this project utilizes Nifi, PySpark, Elasticsearch, Logstash, and Kibana. It begins with complex Json real-time streaming data extraction and CSV parsing, followed by data storage in HDFS through NiFi. This project will teach you to use PySpark to retrieve data from HDFS for further analysis with PySparkSQL. It requires executing data exploration and analysis through a variety of SQL queries in PySpark. Finally, it ingests the data into Elasticsearch after returning the processed data to HDFS.
Source Code: Event Data Analysis using AWS ELK Stack
Wrapping Up
By now, you know that PySpark gives the ability to implement all of Python's core capabilities in the Spark framework and use Spark's building blocks and operations in Python utilizing Apache Spark's Python API. If this blog makes you feel confident enough in your understanding of PySpark, you should begin focusing on some of the most commonly asked PySpark interview questions and answers to prepare for your next big data job interview. You can also check out some fantastic solved end-to-end Data Science and Big Data projects in the ProjectPro repository that will help you upskill your data skills.
FAQs
Is PySpark easy to learn?
If you have a basic understanding of Python, SQL and a working knowledge of the Apache Spark framework, PySpark is easy to master. Additionally, working on some good PySpark projects will help you deepen your understanding of PySpark architecture.
How long does it take to learn PySpark?
It takes around five to six weeks to learn PySpark. You'll need to commit one or two weeks to learn Apache Spark from the ground up. You must study Python programming in the next two to three weeks. It may take longer to learn Python if you have no prior programming experience (C/C++). Finally, you need to allocate at least two weeks to work on PySpark projects.
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