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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Create RDD in Apache Spark using Pyspark

Gitesh Dhore
Gitesh Dhore Last Updated : 02 Sep, 2022
5 min read

 This article was published as a part of the Data Science Blogathon.

Introduction

In this tutorial, we will learn about the building blocks of PySpark called Resilient Distributed Dataset, which is popularly known as PySpark RDD. Before we do so, let’s understand its basic concept.

rdd

 

What are RDDs?

RDD stands for Resilient Distributed Dataset, which are elements that run and work on multiple nodes to perform parallel processing in a cluster. RDDs are immutable, meaning you cannot change them once you create an RDD. These are fault-tolerant, so they automatically recover in case of failure. You can apply multiple operations on these RDDs to achieve a particular task.
There are two ways to apply operations −
• Transformation
• Events
Let’s see what these methods are:
Transformation − These operations are applied to create a new RDD. Filter, group, and map are examples of transformations.
Events− These are operations that are applied to an RDD that instruct Spark to perform a calculation and send the result back to the controller.
To use any operation in PySpark, we need to create a PySpark RDD first. The following code block details the PySpark RDD − class
class pyspark.RDD (
   Judd,
   ctx
   jrdd_deserializer = AutoBatchedSerializer(PickleSerializer())
)
Let’s see how to run a few basic operations using PySpark. The following code in a Python file creates a words RDD that stores a set of said words.
words = sc.parallelize (
   ["scale",
   "Java",
   "Hadoop",
   "spark",
   "Akka",
   "spark vs Hadoop",
   "pyspark",
   "pyspark and spark"]
)
Now let’s do some word operations.

count()

An array of elements in an RDD will be returned.
---------------------------------------count.py------- --------------------------------
from pyspark import SparkContext
sc = SparkContext("local", "count app")
words = sc.parallelize (
   ["scale",
   "Java",
   "Hadoop",
   "spark",
   "Akka",
   "spark vs hadoop",
   "pyspark",
   "pyspark and spark"]
)
counts = words.count()
print "elements are -> %i" % (counts)
---------------------------------------count.py------- --------------------------------
Command − The command for count() is −
$SPARK_HOME/bin/spark-submit count.py
Output − output for the above code −
Number of elements in RDD → 8

collect()

All elements are returned.
---------------------------------------collect.py------- --------------------------------
from pyspark import SparkContext
sc = SparkContext("local", "Collect app")
words = sc.parallelize (
   ["scale",
   "Java",
   "Hadoop",
   "spark",
   "Akka",
   "spark vs hadoop",
   "pyspark",
   "pyspark and spark"]
)
coll = words.collect()
print "RDD elements -> %s" % (collection)
---------------------------------------collect.py------- --------------------------------
Command − The command for collect() is −
$SPARK_HOME/bin/spark-submit collect.py
Output − output for the above code −
Elements in RDD -> [
   'scale',
   'Java',
   'hadoop',
   'spark',
   'Akka',
   'spark vs Hadoop,
   'pyspark',
   'pyspark and spark'
]

foreach(f)

Returns only those elements that satisfy the condition of the function inside the foreach. In the following example, we call the print in foreach function, which prints all the elements in the RDD.
----------------------------------------foreach.py------- --------------------------------
from pyspark import SparkContext
sc = SparkContext("local", "ForEach app")
words = sc.parallelize (
   ["scale",
   "Java",
   "Hadoop",
   "spark",
   "Akka",
   "spark vs hadoop",
   "pyspark",
   "pyspark and spark"]
)
def f(x): print(x)
fore = words.foreach(f)
----------------------------------------foreach.py------- --------------------------------
Command − This is a command for foreach(f)  −
$SPARK_HOME/bin/spark-submit foreach.py
Output − output for the above code −
scala
Java
hadoop
spark
Akka
spark vs hadoop
pyspark
pyspark and spark

filter(f)

A new RDD is returned containing the elements that satisfy the function inside the filter. In the following example, we filter out strings containing “spark.”
----------------------------------------filter.py------- --------------------------------
from pyspark import SparkContext
sc = SparkContext("local", "Filter app")
words = sc.parallelize (
   ["scale",
   "Java",
   "Hadoop",
   "spark",
   "Akka",
   "spark vs hadoop",
   "pyspark",
   "pyspark and spark"]
)
word_filter = words.filter(lambda x: 'spark' in x)
filtered = word_filter.collect()
print "Filtered RDD -> %s" % (filtered)
----------------------------------------filter.py------- ---------------------------------
Command − filter(f) command is −
$SPARK_HOME/bin/spark-submit filter.py
Output − output for the above code −
Filtered RDD -> [
   'spark',
   'spark vs Hadoop,
   'pyspark',
   'pyspark and spark'
]

map(f, preservePartitioning = False)

A new RDD is returned by applying the function to each element in it. In the following example, we create a key-value pair and map each string to the value 1.
----------------------------------------map.py------- --------------------------------
from pyspark import SparkContext
sc = SparkContext("local", "Map app")
words = sc.parallelize (
   ["scale",
   "Java",
   "Hadoop",
   "spark",
   "Akka",
   "spark vs hadoop",
   "pyspark",
   "pyspark and spark"]
)
word_map = words.map(lambda x: (x, 1))
mapping = word_map.collect()
print "Key-value pair -> %s" % (mapping)
----------------------------------------map.py------- --------------------------------
Command − The command for map(f, protectsPartitioning=False) is −
$SPARK_HOME/bin/spark-submit map.py
Output − output of the above code −
Key-value pair -> [
   ('scale', 1),
   ('java', 1),
   ('Hadoop, 1),
   ('spark', 1),
   ('akka', 1),
   ('spark vs hHadoop 1),
   ('pyspark', 1),
   ('pyspark and spark', 1)
]

reduce (f)

After performing the specified associative and commutative binary operation, the element in the RDD is returned. In the following example, we import the add pack from the operator and apply it to “num” to perform a simple addition operation.
-----------------------------------------reduce.py------- --------------------------------
from pyspark import SparkContext
from the import add operator
sc = SparkContext("local", "Reduce app")
nums = sc.parallelize([1, 2, 3, 4, 5])
add = nums.reduce(add)
print "Adding elements -> %i" % (add)
-----------------------------------------reduce.py------- --------------------------------
Command − The command calling ce(f) is −
$SPARK_HOME/bin/spark-submit reduction.py
Output − output of the above code −
Adding all elements -> 15

join(other, numPartitions = None)

Returns an RDD with a pair of elements with the corresponding keys and all values ​​for that particular key. The following example shows pairs of elements in two different RDDs. After joining these two RDDs, we get an RDD with elements that have the corresponding keys and their values.
----------------------------------------join.py------- --------------------------------
from pyspark import SparkContext
sc = SparkContext("local", "Join app")
x = sc.parallelize([("spark", 1), ("hadoop", 4)])
y = sc.parallelize([("spark", 2), ("Hadoop", 5)])
joined = x.join(y)
final = join.collect()
print "connect RDD -> %s" % (final)
----------------------------------------join.py------- --------------------------------
Command − command for join(other, numPartitions = None) is −
$SPARK_HOME/bin/spark-submit join.py
Output − output for the above code −
Connect to RDD -> [
   ('spark', (1, 2)),
   ('Hadoop, (4, 5))
]

cache()

Keep this RDD with the default storage level (MEMORY_ONLY). You can also check whether it is cached or not.
---------------------------------------cache.py------- --------------------------------
from pyspark import SparkContext
sc = SparkContext("local", "Cache app")
words = sc.parallelize (
   ["scale",
   "Java",
   "Hadoop",
   "spark",
   "Akka",
   "spark vs hadoop",
   "pyspark",
   "pyspark and spark"]
)
words.cache()
caching = words.persist().is_cached
print "cached words are > %s" % (caching)
---------------------------------------cache.py------- --------------------------------
Command − The command for cache() is −
$SPARK_HOME/bin/spark-submit cache.py
Output − output for the above code −
Words have been cached -> True
These were some of the most important operations performed.

Conclusion

RDD stands for Resilient Distributed Dataset, which are elements that run and work on multiple nodes to perform parallel processing in a cluster. RDDs are immutable, meaning that you cannot change them once you create an RDD.

  • Transformation − These operations are applied to an RDD to create a new one. Filter, group, and map are examples of transformations.
  • Events− These are operations that are applied to an RDD that instruct Spark to perform a calculation and send the result back to the controller.
  • It returns only those elements that satisfy the condition of the function inside the foreach. In the following example, we call the print in foreach function, which prints all the elements in the RDD.

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Gitesh Dhore
Gitesh Dhore

I am a Machine Learning Enthusiast. Done some Industry level projects on Data Science and Machine Learning. Have Certifications in Python and ML from trusted sources like data camp and Skills vertex. My Goal in life is to perceive a career in Data Industry.

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