Debaleena GhoshPySpark is an open-source distributed computing framework for large-scale data processing. This framework is built on top of Apache Spark.
Apache Spark represents a fast and general-purpose cluster computing system. PySpark provides an easy-to-use programming interface for data processing with Python.
PySpark allows users to write applications in Python and run them on Spark. In this blog, we will discuss PySpark in backend development, its features, and how to use it for scalable data processing.
Before we deep dive into understanding PySpark, let’s understand Apache Spark.
Introduction to Apache Spark
- Apache Spark is a distributed computing framework engineered for processing large-scale data sets.
- The framework was designed at UC Berkeley’s AMPLab in 2009. Later, in 2013, it was donated to the Apache Software Foundation.
- Apache Spark is renowned for its fast processing speed, fault tolerance, and ease of use.
- Apache Spark is built on top of Hadoop and is designed to provide faster and more flexible data processing capabilities than Hadoop MapReduce.
- It achieves this by processing data in memory using Resilient Distributed Datasets (RDDs), which are immutable and partitioned datasets that can be cached in memory.
What Is PySpark?
PySpark is the Python API for Apache Spark. It allows developers to write Spark applications in Python, a language widely used in data science and machine learning. PySpark provides a simple and easy-to-use interface to interact with Apache Spark, making it a popular choice for data processing and analytics tasks.
Why Is Pyspark Needed?
PySpark is needed for several reasons:
- Python is an incredibly popular programming language in the data science and machine learning community, and PySpark allows developers to leverage the power of Apache Spark while using their preferred programming language.
- PySpark simplifies the process of interacting with Apache Spark by providing a simple and easy-to-use interface. This makes it simpler for developers to write Spark applications, reducing the learning curve and increasing productivity.
- PySpark provides several APIs such as RDDs, DataFrames, and Datasets for data processing, making it easier for developers to manipulate data. PySpark also integrates with several data sources and data formats, allowing developers to work with a wide range of data types.
- PySpark is designed for distributed computing, allowing developers to process large-scale data sets efficiently. PySpark achieves this by distributing data processing tasks across multiple nodes in a cluster, making it a highly scalable solution for big data processing.
Key Features of PySpark
- Distributed Computing: PySpark is designed for distributed computing. It can process large-scale data using a distributed processing model that allows for parallel processing across multiple machines.
- Fault Tolerance: PySpark provides fault tolerance. It can handle the failure of individual nodes without affecting the overall performance of the system. This is attained by replicating data across multiple nodes and rerunning the failed tasks on other nodes.
- Integration: PySpark integrates with various data sources, such as Hadoop Distributed File System (HDFS), Cassandra, and HBase. It also supports multiple data formats such as JSON, CSV, and Parquet.
- High-Level APIs: PySpark provides high-level APIs for data processing, making it easy to operate for developers. The APIs are based on the RDD (Resilient Distributed Dataset) model, which is a fault-tolerant elements collection that can be processed in parallel.
- Machine Learning: PySpark provides a powerful machine learning library called MLlib. It provides various algorithms for classification, regression, clustering, and collaborative filtering.
- Support for Several Languages: PySpark supports several programming languages such as Python, Java, Scala, and R. This allows developers to choose the language they are most comfortable with for their data processing tasks.
- Consistency of Discs and Caching: PySpark ensures consistency of data stored on disks and caching in memory. This is achieved through the use of RDDs, which are immutable and partitioned datasets that can be cached in memory.
- Rapid Processing: PySpark is known for its rapid processing speed. It achieves this by processing data in memory using RDDs and optimizing data processing operations.
Difference Between Scala and PySpark
Scala and PySpark are two programming languages used for Apache Spark programming. Scala is the primary language used for Apache Spark development, but PySpark is also popular due to its ease of use for Python programmers.
Here are the key differences between Scala and PySpark:
| Scala | PySpark | |
| Syntax | Scala is a statically typed language with a concise syntax, similar to Java. | PySpark, is a dynamically typed language with a more expressive syntax, making it easier to write code quickly. |
| Performance | Scala code can be slightly faster than PySpark code due to its static typing and JIT compilation. | PySpark is comparatively slower. However, the difference in performance is usually small and depends on the specific use case. |
| Community Support | Scala has a larger community and a more mature ecosystem. Scala has more resources, libraries, and tools available for Spark development. | PySpark is relatively new and is currently developing a thriving community. |
| Learning Curve | Scala has a more complex learning curve than PySpark, especially for developers who need to become more familiar with functional programming concepts. | PySpark, on the other hand, has a lower learning curve, especially for developers already familiar with Python. |
| Interoperability | Scala is more interoperable with Java and other JVM-based languages, which means that Spark applications written in Scala can easily integrate with other JVM-based applications. | PySpark, on the other hand, can easily integrate with Python-based applications. |
What Are the Benefits of Using Pyspark?
PySpark offers several benefits for developers and data analysts, including:
- Easy to understand and use: PySpark is built on top of Python, a popular and easy-to-learn programming language. This makes PySpark easier to learn and use for developers who are already familiar with Python.
- Swift Processing: PySpark can process large amounts of data quickly due to its distributed processing capabilities and in-memory computation. This allows for faster data processing and analysis than traditional batch processing systems.
- In-Memory Computation: PySpark’s use of in-memory computation allows for faster data processing and analysis than traditional disk-based systems. This means that PySpark can perform complex analytical tasks in real-time, providing instant insights into data.
- Libraries: PySpark provides several libraries for machine learning (PySpark MLlib), graph processing (PySpark GraphX), and real-time streaming processing (PySpark Streaming). These libraries provide a high-level API for common data processing tasks and can be used to build end-to-end data pipelines.
- Simple to write: PySpark is designed to be easy to write and maintain, with concise and readable code that is easy to debug. This makes it easier for data analysts and developers to work with large datasets and complex analytical tasks.
Who uses PySpark?
PySpark is used by a variety of organizations across industries for big data processing and analytics. Some examples of companies and organizations that use PySpark include:
- Airbnb: PySpark is used at Airbnb for data processing and analytics, including analyzing user behaviour and improving the search ranking algorithm.
- Netflix: Netflix uses PySpark for processing large-scale data sets related to user activity, video metadata, and content recommendations.
- Uber: PySpark is used at Uber for processing real-time data streams related to ride requests, driver locations, and payment transactions.
- Adobe: Adobe uses PySpark for processing data related to user behaviour and engagement across its suite of products, including Photoshop, InDesign, and Acrobat.
- IBM: IBM uses PySpark for processing data related to cybersecurity and threat detection, as well as for machine learning and natural language processing applications.
- Databricks: Databricks, the company that created PySpark, provides a cloud-based platform for big data processing and analytics using PySpark, as well as other Apache Spark APIs.
- NASA: PySpark is used at NASA for processing data related to space exploration, including analyzing data from the Hubble Space Telescope and other space missions.
- Comcast: Comcast uses PySpark for processing large-scale data sets related to customer behaviour and preferences, as well as for analyzing network performance data.
- Rakuten: Rakuten, a Japanese e-commerce company, uses PySpark for processing data related to user behaviour, product recommendations, and advertising targeting.
- Yelp: Yelp uses PySpark for processing data related to user reviews, ratings, and engagement, as well as for analyzing advertising performance.
- Capital One: Capital One uses PySpark for processing data related to credit card transactions, customer behaviour, and fraud detection.
- Walmart: Walmart uses PySpark for processing data related to supply chain management, customer behaviour, and inventory optimization.
- Twitter: Twitter uses PySpark for processing real-time data streams related to user activity, trending topics, and advertising targeting.
How to Use PySpark for Backend Development?
| Set up a PySpark cluster. |
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| Define your data sources. |
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| Load your data into PySpark. |
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| Process your data |
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| Store your results |
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Understanding PySpark Architecture
PySpark Architecture
PySpark is built on top of Apache Spark, which is a distributed processing framework designed to handle large-scale data processing and analysis. PySpark provides a Python API for interacting with Spark and allows developers to write Spark applications in Python.
The PySpark architecture consists of four main components:
| Driver program | Cluster manager | Worker nodes | Executors |
| This is the main entry point for a PySpark application. It runs the PySpark application and manages the overall execution flow. | Responsible for resource management of the Spark cluster, including allocating resources to worker nodes, scheduling tasks, and monitoring the health of the cluster. | Worker nodes are responsible for executing tasks on the Spark cluster. They receive tasks from the driver program and execute them in parallel across the cluster. | Executors are responsible for executing tasks on worker nodes. Each worker node can have multiple executors, and each executor can execute multiple tasks in parallel. |
Cluster Manager Types:
PySpark supports several cluster managers, including:
| Standalone mode: | Apache Mesos: | Hadoop YARN: |
| This is the default cluster manager for Spark. It is a simple cluster manager that comes bundled with Spark and can be used to run Spark applications on a single machine or a cluster of machines. | Mesos is a popular cluster manager that provides a unified API for managing resources across multiple data centres and cloud providers. | YARN is the resource manager for Hadoop and provides a scalable and reliable cluster management solution for running Spark applications on Hadoop clusters. |
Modules and Packages:
PySpark provides several built-in modules and packages that can be used to build Spark applications. Some of the key modules and packages include:
| pyspark.sql: | pyspark.ml: | pyspark.streaming: | pyspark.graphx: |
| This module provides support for structured data processing and analysis using Spark SQL. | This module provides support for machine learning tasks, including classification, regression, and clustering. | This module provides support for real-time streaming data processing and analysis. | This module provides support for graph processing and analysis using Spark GraphX. |
In addition to the built-in modules and packages, PySpark also supports third-party libraries and packages, which can be installed using pip or conda. Some popular third-party packages for PySpark include NumPy, Pandas, and TensorFlow.
What external Libraries can be used with PySpark?
PySpark is a powerful framework that provides a range of data processing and analysis capabilities.
However, there are several external libraries that can be used in conjunction with PySpark to extend its functionality and provide additional capabilities. Some popular external libraries compatible with PySpark are:
- NumPy: NumPy is a powerful Python library for scientific computing that offers support for large, multi-dimensional arrays and matrices and a comprehensive range of mathematical functions. It can be used with PySpark to provide additional capabilities for data manipulation and analysis.
- Pandas: Pandas is a highly popular Python library for data manipulation and analysis that provides support for data structures like data frames and series. It can be used with PySpark to provide additional capabilities for data processing and analysis, such as data cleaning, filtering, and transformation.
- Matplotlib: Matplotlib represents a Python library for data visualization that provides support for creating a wide range of charts and graphs, including line charts, scatter plots, and histograms. It can be used with PySpark to provide additional capabilities for data visualization and exploration.
- TensorFlow: TensorFlow is a popular machine learning open-source library providing support for building and training neural networks. It can be used with PySpark to provide additional capabilities for machine learning tasks like classification, regression, and clustering.
- Keras: Keras is a high-level neural network Python-based API that provides support for building and training neural networks. It can be used with PySpark to provide additional capabilities for deep learning tasks like image and text classification.
PySpark RDD
PySpark RDD (Resilient Distributed Dataset) is a fundamental data structure in PySpark. RDD represents an immutable distributed collection of objects, which allows the processing of data in parallel across a cluster.
RDD Creation:
There are several ways to create RDDs in PySpark:
- Creating RDDs from an external data source such as Hadoop Distributed File System (HDFS), local file system, or other data sources such as Hive, Cassandra, etc.
Example of creating a data frame from a CSV file:
| from pyspark.sql import SparkSession
Spark = SparkSession.builder.appName(“DataFrame example”).getOrCreate() df = spark.read.csv(“file.csv”, header=True, inferSchema=True) df.show() |
- Creating RDDs by parallelizing an existing collection in your Python program, such as a list or a tuple.
Example of creating an RDD from parallelizing an existing Python list:
| from pyspark import SparkContext
sc = SparkContext(“local”, “RDD example”) data = [1, 2, 3, 4, 5] rdd = sc.parallelize(data) |
RDD Operations:
Once RDDs are created, there are two types of operations you can perform on them: transformations and actions.
- Transformations
Transformations represent operations that create a new RDD from an existing one. They are lazily evaluated, which means that they are not executed immediately. Instead, they are stored as a set of instructions that will be executed when an action is called.
Here’s an example of some common transformations:
| # map transformation
rdd = rdd.map(lambda x: x * 2) # filter transformation rdd = rdd.filter(lambda x: x > 5) # flatMap transformation rdd = rdd.flatMap(lambda x: range(1, x)) |
- Actions
Actions are operations that trigger the execution of transformations and return a result to the driver program or store the result in an external storage system. Actions are eagerly evaluated, which means that they are executed immediately when they are called.
Here’s an example of some common actions:
| # reduce action
result = rdd.reduce(lambda x, y: x + y) # count action result = rdd.count() # take action result = rdd.take(10) |
These are just a few illustrations of the transformations and actions available in PySpark. PySpark provides a wide range of other transformations and actions, including groupByKey, sortByKey, join, and more, which can be used to process large datasets efficiently in a distributed manner.
PySpark SQL:
PySpark SQL is a module in PySpark that provides a programming interface to work with structured data using SQL queries.
It provides a way to query data stored in various external data sources such as Hadoop Distributed File System (HDFS), local file system, or other data sources such as Hive, Cassandra, etc. PySpark SQL also supports many SQL operations like SELECT, WHERE, GROUP BY, JOIN, and more.
Here’s an example of using PySpark SQL to query a DataFrame:
| from pyspark.sql import SparkSession
Spark = SparkSession.builder.appName(“DataFrame example”).getOrCreate() df = spark.read.csv(“file.csv”, header=True, inferSchema=True) df.createOrReplaceTempView(“table”) result = spark.sql(“SELECT * FROM table WHERE age > 30”) result |
The above code generates a DataFrame from a CSV file and registers it as a temporary view named “table”. The spark.sql() method is then utilised to execute a SQL query on the DataFrame and return the result.
PySpark Streaming
PySpark Streaming is a scalable, fault-tolerant, and real-time processing engine for live data streaming. It is built on top of the Spark core and provides high-level APIs for processing live data streams.
With PySpark Streaming, you can ingest, process, and analyze live data streams in real-time using a similar programming model as batch processing.
Here’s an example of how to use PySpark Streaming to process a live data stream:
| from pyspark.streaming import StreamingContext
# Create a local StreamingContext with two working threads and batch interval of 1 second ssc = StreamingContext(sparkContext, 1) # Create a DStream that will connect to a live data source, such as a socket or Kafka stream lines = ssc.socketTextStream(“localhost”, 9999) # Split each line into words words = lines.flatMap(lambda line: line.split(” “)) # Count each word in each batch wordCounts = words.map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b) # Print the first ten elements of each RDD generated in this DStream to the console wordCounts.pprint() # Start the computation ssc.start()
# Wait for the computation to terminate ssc.awaitTermination() |
In the above example, a PySpark StreamingContext is created with a batch interval of 1 second. The code then creates a DStream by connecting to a live data source (in this case, a socketTextStream).
The DStream is then processed using PySpark’s transformation and action APIs to count the number of occurrences of each word in the stream. Finally, you can print the result on the console using the pprint() method.
The computation is started using ssc.start() and waits for the termination signal using ssc.awaitTermination().
PySpark GraphFrames
PySpark GraphFrames is a library that extends PySpark’s DataFrame API with a GraphFrame abstraction for processing graph data. It allows us to represent graph data as vertices and edges, which can be manipulated and analyzed using a wide range of graph algorithms.
Here’s an example of how to use PySpark GraphFrames to create and analyze a graph:
| from pyspark.sql.functions import *
from graphframes import *
# Create vertices DataFrame v = sqlContext.createDataFrame([(1, “A”), (2, “B”), (3, “C”)], [“id”, “name”]) # Create edges DataFrame e = sqlContext.createDataFrame([(1, 2), (2, 3)], [“src”, “dst”]) # Create GraphFrame g = GraphFrame(v, e) # Query the graph results = g.find(“(a)-[]->(b)”).show() # Run PageRank algorithm results = g.pageRank(resetProbability=0.15, tol=0.01).vertices.show() |
In the above example, a graph is created using two DataFrames, one for vertices and one for edges. The graph is then queried using GraphFrames’ find() method to find all paths between vertices’ a’ and ‘b’. The PageRank algorithm is then run on the graph to find the importance of each vertex in the graph.
Difference between PySpark and Python
PySpark and Python are both related to the programming language Python, but they serve different purposes and have different capabilities. Here are some key differences between PySpark and Python:
| PySpark | Python | |
| Distributed computing | PySpark is designed for distributed computing and processing large datasets. While there is a certain overlap between the two, PySpark’s capabilities for distributed computing and scalability set it apart from Python. | Python is a general-purpose programming language used for a variety of tasks, including data processing and machine learning. |
| Scalability: | PySpark is highly scalable and can handle large datasets with ease. | Python, on the other hand, can struggle with large datasets and may require additional resources or optimization techniques to handle them. |
| Parallel processing: | PySpark allows you to perform parallel processing on large datasets, which can speed up processing times significantly. | Python, while it supports multi-threading and multiprocessing, can struggle to achieve the same level of parallel processing as PySpark. |
| Machine learning: | PySpark comes with the MLlib library, which provides machine-learning capabilities such as classification, regression, clustering, and more. | Python has several machine learning libraries as well, but they may not be as optimized for distributed computing as PySpark’s MLlib. |
| Real-time processing: | PySpark has support for real-time processing using PySpark Streaming, which allows you to process data in real time as soon as it is generated. | Python does not have built-in support for real-time processing, although there are libraries that can be used to achieve real-time processing. |
PySpark Installation on Windows
Installing PySpark on Windows can be a bit more involved than installing it on a Unix-based operating system like Linux or macOS. Here’re the steps you can follow to install PySpark on Windows:
Install Java
PySpark requires Java to be installed on your system. You can download the latest version of Java from the official Java website (https://www.java.com/en/download/).
Download Apache Spark
Download the latest version of Apache Spark from the official website (https://spark.apache.org/downloads.html). Choose the package type as “Pre-built for Apache Hadoop 2.7 and later.”
Extract the Spark Archive
Once you have downloaded the Apache Spark package, extract it to a folder on your local machine.
Install Anaconda
Anaconda is a popular Python distribution that comes with many scientific packages and pre-installed tools. Use the official website to download and install the updated version of Anaconda (https://www.anaconda.com/products/individual).
Create a new Conda Environment
You need to open the Anaconda prompt and develop a new conda environment for PySpark by running the following command:
conda create –name pyspark python=3.7
This will create a new conda environment defined as “pyspark” with Python version 3.7.
Install PySpark
Activate the conda environment by running the following command:
conda activate pyspark
Next, install PySpark by running the following command:
pip install pyspark
Set Environment Variables
To use PySpark, you require to set two environment variables. These include JAVA_HOME and SPARK_HOME.
JAVA_HOME must point to the root directory of your Java installation, and SPARK_HOME should point to the directory where you extracted Apache Spark in Step 3.
To set these variables, right-click on “This PC” and select “Properties.” Click on “Advanced system settings” and then click on the “Environment Variables” button. In the “System variables” section, select the “New” button and enter JAVA_HOME and the path to your Java installation. Repeat the process for SPARK_HOME.
Finally, add the %SPARK_HOME%\bin directory to your system’s PATH variable.
That’s it! PySpark should now be installed and ready to use on your Windows machine. You can launch a PySpark shell by running the command Pyspark in the Anaconda prompt while in the Pyspark environment.
FAQ on PySpark
Is PySpark a programming language or framework?
PySpark is a framework built on top of the Python programming language, designed specifically for distributed computing and processing large datasets.
Is PySpark the same as SQL?
No, PySpark is not the same as SQL. PySpark is a distributed computing framework for processing large datasets, while SQL is a language used to manage and manipulate relational databases.
Does PySpark need coding?
Yes, PySpark requires programming skills in Python as well as knowledge of distributed computing concepts to effectively use and apply its capabilities.
Are PySpark and Hadoop the same?
No, PySpark and Hadoop are not the same. Hadoop is an open-source framework known fir for distributed storage and processing of huge datasets, while PySpark is a distributed computing framework built on top of Apache Spark.
Is PySpark a good skill?
Yes, PySpark is a highly sought-after skill in the industry as it allows for the processing of large datasets in a distributed computing environment, making it an important tool for data engineering and machine learning.
Is PySpark faster than SQL?
PySpark and SQL are designed for different purposes, so their performance cannot be directly compared. PySpark is optimized for distributed computing and processing large datasets, while SQL is optimized for querying and manipulating relational databases.
What is the max salary in PySpark?
The salary for a PySpark developer can vary depending on location, experience, and industry. However, in general, PySpark developers can earn a high salary as it is a specialized and in-demand skill.
Do data engineers use PySpark?
Yes, data engineers often use PySpark as it is a potential tool for processing and analyzing huge datasets in a distributed computing environment.
What skills are required to learn PySpark?
To learn PySpark, one should know programming concepts in Python, distributed computing concepts, and knowledge of data processing and analysis. Familiarity with SQL and machine learning concepts can also be helpful.
Conclusion
PySpark, is an amazing tool for distributed computing and processing of large datasets. Whether you’re a data engineer, data scientist, or machine learning enthusiast, PySpark can help you analyze data at lightning-fast speeds.
So if you’re looking to enhance your big data skills and boost your career, learning PySpark could be the key to unlocking a whole new world of possibilities.
Sumit Malviya
Vadita
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