Python Regex complete tutorial with usecases of email search inside whole dbms or code search inside a code repository

July 9, 2024

Regular expressions (regex) are a powerful tool for matching patterns in text. Python’s re module provides functions and tools for working with regular expressions. Here’s a complete tutorial on using regex in Python.

1. Importing the `re` Module

To use regular expressions in Python, you need to import the re module:

import re

2. Basic Functions

`re.search()`

Searches for a pattern within a string and returns a match object if found.

pattern = r"d+"  # Matches one or more digits
text = "The year is 2024"
match = re.search(pattern, text)
if match:
    print(f"Match found: {match.group()}")  # Output: Match found: 2024

`re.match()`

Matches a pattern at the beginning of a string.

pattern = r"d+"  # Matches one or more digits
text = "2024 is the year"
match = re.match(pattern, text)
if match:
    print(f"Match found: {match.group()}")  # Output: Match found: 2024

`re.findall()`

Finds all non-overlapping matches of a pattern in a string.

pattern = r"d+"  # Matches one or more digits
text = "The years are 2024, 2025, and 2026"
matches = re.findall(pattern, text)
print(f"Matches found: {matches}")  # Output: Matches found: ['2024', '2025', '2026']

`re.finditer()`

Returns an iterator yielding match objects for all non-overlapping matches.

pattern = r"d+"  # Matches one or more digits
text = "The years are 2024, 2025, and 2026"
matches = re.finditer(pattern, text)
for match in matches:
    print(f"Match found: {match.group()}")  # Output: 2024, 2025, 2026

`re.sub()`

Replaces occurrences of a pattern with a specified string.

pattern = r"d+"  # Matches one or more digits
text = "The years are 2024, 2025, and 2026"
new_text = re.sub(pattern, "YEAR", text)
print(new_text)  # Output: The years are YEAR, YEAR, and YEAR

3. Special Characters

.: Matches any character except a newline.
^: Matches the start of the string.
$: Matches the end of the string.
*: Matches 0 or more repetitions of the preceding element.
+: Matches 1 or more repetitions of the preceding element.
?: Matches 0 or 1 repetition of the preceding element.
{m,n}: Matches from m to n repetitions of the preceding element.
[]: Matches any one of the characters inside the brackets.
|: Matches either the pattern before or the pattern after the |.
() : Groups patterns and captures matches.

4. Escaping Special Characters

To match a literal special character, escape it with a backslash ().

pattern = r"$100"  # Matches the string "$100"
text = "The price is $100"
match = re.search(pattern, text)
if match:
    print(f"Match found: {match.group()}")  # Output: Match found: $100

5. Character Classes

d: Matches any digit (equivalent to [0-9]).
D: Matches any non-digit.
w: Matches any alphanumeric character (equivalent to [a-zA-Z0-9_]).
W: Matches any non-alphanumeric character.
s: Matches any whitespace character (spaces, tabs, newlines).
S: Matches any non-whitespace character.

6. Groups and Capturing

You can use parentheses to create groups and capture parts of the match.

pattern = r"(d{4})-(d{2})-(d{2})"  # Matches dates in the format YYYY-MM-DD
text = "Today's date is 2024-06-27"
match = re.search(pattern, text)
if match:
    year, month, day = match.groups()
    print(f"Year: {year}, Month: {month}, Day: {day}")  # Output: Year: 2024, Month: 06, Day: 27

7. Named Groups

Named groups allow you to assign a name to a capturing group for easier access.

pattern = r"(?P<year>d{4})-(?P<month>d{2})-(?P<day>d{2})"  # Matches dates in the format YYYY-MM-DD
text = "Today's date is 2024-06-27"
match = re.search(pattern, text)
if match:
    print(f"Year: {match.group('year')}, Month: {match.group('month')}, Day: {match.group('day')}")  # Output: Year: 2024, Month: 06, Day: 27

8. Lookahead and Lookbehind

Lookaheads and lookbehinds are assertions that allow you to match a pattern only if it is (or isn’t) followed or preceded by another pattern.

Positive Lookahead

pattern = r"d+(?= dollars)"  # Matches digits followed by "dollars"
text = "The price is 100 dollars"
match = re.search(pattern, text)
if match:
    print(f"Match found: {match.group()}")  # Output: Match found: 100

Negative Lookahead

pattern = r"d+(?! dollars)"  # Matches digits not followed by "dollars"
text = "The price is 100 dollars or 200 euros"
matches = re.findall(pattern, text)
print(f"Matches found: {matches}")  # Output: Matches found: ['200']

Positive Lookbehind

pattern = r"(?<=$)d+"  # Matches digits preceded by a dollar sign
text = "The price is $100"
match = re.search(pattern, text)
if match:
    print(f"Match found: {match.group()}")  # Output: Match found: 100

Negative Lookbehind

pattern = r"(?<!$)d+"  # Matches digits not preceded by a dollar sign
text = "The price is 100 dollars or $200"
matches = re.findall(pattern, text)
print(f"Matches found: {matches}")  # Output: Matches found: ['100']

9. Compiling Regular Expressions

For efficiency, especially when using the same regex multiple times, you can compile a regular expression.

pattern = re.compile(r"d{4}-d{2}-d{2}")  # Compile the regex pattern
text = "The dates are 2024-06-27 and 2025-07-28"

# Use the compiled pattern
matches = pattern.findall(text)
print(f"Matches found: {matches}")  # Output: Matches found: ['2024-06-27', '2025-07-28']

10. Flags

You can use flags to modify the behavior of regex functions. Common flags include:

re.IGNORECASE (re.I): Ignore case.
re.MULTILINE (re.M): Multi-line matching, affects ^ and $.
re.DOTALL (re.S): Dot matches all characters, including newline.

pattern = r"^hello"
text = "Hellonhello"

# Without flag
matches = re.findall(pattern, text)
print(f"Matches without flag: {matches}")  # Output: Matches without flag: ['hello']

# With IGNORECASE and MULTILINE flags
matches = re.findall(pattern, text, re.IGNORECASE | re.MULTILINE)
print(f"Matches with flags: {matches}")  # Output: Matches with flags: ['Hello', 'hello']

Example: Searching for Emails in a Database

Suppose you have a database with user data, and you want to extract all email addresses.

import re

# Sample data representing rows in a database
data = [
    "Alice, alice@example.com, 123-456-7890",
    "Bob, bob123@gmail.com, 987-654-3210",
    "Charlie, charlie123@company.org, 555-555-5555",
    "Invalid data, no email here, 000-000-0000"
]

# Regex pattern for matching email addresses
email_pattern = r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}'

emails = []

for entry in data:
    found_emails = re.findall(email_pattern, entry)
    emails.extend(found_emails)

print("Extracted Emails:")
for email in emails:
    print(email)

Example: Searching for Code Snippets in a Code Repository

Suppose you have a repository with Python files, and you want to find all instances of function definitions.

import re
import os

# Directory containing Python files
code_directory = 'path_to_code_repository'

# Regex pattern for matching function definitions in Python
function_pattern = r'defs+([a-zA-Z_][a-zA-Z0-9_]*)s*(.*)s*:'

functions = []

# Walk through the directory and process each Python file
for root, dirs, files in os.walk(code_directory):
    for file in files:
        if file.endswith('.py'):
            file_path = os.path.join(root, file)
            with open(file_path, 'r') as f:
                file_content = f.read()
                found_functions = re.findall(function_pattern, file_content)
                for func in found_functions:
                    functions.append((file, func))

print("Found Functions:")
for file, func in functions:
    print(f"Function '{func}' found in file '{file}'")

PySpark Projects:- Scenario Based Complex ETL projects Part1

July 7, 2024

1.Exploratory Data Analysis (EDA) with Pandas in Banking – Converted in Pyspark

While searching for A free Pandas Project on Google Found this link –Exploratory Data Analysis (EDA) with Pandas in Banking . I have tried to convert this Pyscript in Pyspark one.

First, let’s handle the initial steps of downloading and extracting the data:

# These are shell commands to download and unzip the data
!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip
!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/EDA_Pandas_Banking_L1/bank-additional.zip
!unzip -o -q bank-additional.zip

In a PySpark context, we’ll assume the data is already available locally after the above steps.

Importing Libraries

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline
plt.rcParams["figure.figsize"] = (8, 6)

import warnings
warnings.filterwarnings('ignore')

Equivalent imports in PySpark:

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
import matplotlib.pyplot as plt
import numpy as np

# Initialize Spark session
spark = SparkSession.builder.appName("EDA_PySpark_Banking").getOrCreate()

Loading Data

Pandas:

df = pd.read_csv('bank-additional/bank-additional-full.csv', sep=';')
df.head(5)

PySpark:

df = spark.read.csv('bank-additional/bank-additional-full.csv', sep=';', header=True, inferSchema=True)
df.show(5)

Initial Exploration

Columns

Pandas:

df.columns

PySpark:

df.columns

Info

Pandas:

print(df.info())

PySpark:

df.printSchema()

Describe

Pandas:

df.describe()
df.describe(include=["object"])

PySpark:

df.describe().show()
df.select([countDistinct(c).alias(c) for c in df.columns]).show()

Value Counts

Pandas:

df["y"].value_counts()
df["marital"].value_counts(normalize=True)

PySpark:

df.groupBy("y").count().show()
df.groupBy("marital").count().withColumn("normalize", col("count") / df.count()).show()

Sorting

Pandas:

df.sort_values(by="duration", ascending=False).head()
df.sort_values(by=["age", "duration"], ascending=[True, False]).head()

PySpark:

df.orderBy(col("duration").desc()).show(5)
df.orderBy(col("age").asc(), col("duration").desc()).show(5)

Applying Functions

Pandas:

df.apply(np.max)
d = {"no": 0, "yes": 1}
df["y"] = df["y"].map(d)

PySpark:

# PySpark does not have an apply method; use select with max for each column
df.select([max(c).alias(c) for c in df.columns]).show()

# Mapping values
df = df.withColumn("y", when(col("y") == "yes", 1).otherwise(0))

Further Analysis

Pandas:

print("Share of attracted clients =", '{:.1%}'.format(df["y"].mean()))
df[df["y"] == 1].mean()
acd = round(df[df["y"] == 1]["duration"].mean(), 2)
acd_in_min = acd // 60
print("Average call duration for attracted clients =", acd_in_min, "min", int(acd) % 60, "sec")
print("Average age of attracted clients =", int(df[(df["y"] == 1) & (df["marital"] == "single")]["age"].mean()), "years")
df[-1:]

PySpark:

df.groupBy().agg(mean("y")).show()
df.filter(df["y"] == 1).agg(*[mean(c).alias(c) for c in df.columns]).show()
acd = df.filter(df["y"] == 1).agg(round(mean("duration"), 2)).collect()[0][0]
acd_in_min = acd // 60
print("Average call duration for attracted clients =", acd_in_min, "min", int(acd) % 60, "sec")
avg_age = df.filter((df["y"] == 1) & (df["marital"] == "single")).agg(mean("age")).collect()[0][0]
print("Average age of attracted clients =", int(avg_age), "years")
df.orderBy(desc("age")).limit(1).show()

Crosstab and Pivot Table

Pandas:

pd.crosstab(df["y"], df["marital"])
pd.crosstab(df["y"], df["marital"], normalize='index')
df.pivot_table(["age", "duration"], ["job"], aggfunc="mean").head(10)

PySpark:

df.groupBy("y", "marital").count().show()
df.groupBy("y", "marital").count().withColumn("normalize", col("count") / sum("count").over(Window.partitionBy("y"))).show()
df.groupBy("job").agg(mean("age"), mean("duration")).show()

Plots

Pandas:

pd.plotting.scatter_matrix(df[["age", "duration", "campaign"]], figsize=(15, 15), diagonal="kde")
plt.show()

df["age"].hist()
df.hist(color="k", bins=30, figsize=(15, 10))
plt.show()

df.boxplot(column="age", by="marital")
plt.show()

df.boxplot(column="age", by=["marital", "housing"], figsize=(20, 20))
plt.show()

PySpark:

# PySpark does not support direct plotting; use matplotlib for plotting
# Convert to Pandas for plotting
pandas_df = df.select("age", "duration", "campaign").toPandas()
pd.plotting.scatter_matrix(pandas_df, figsize=(15, 15), diagonal="kde")
plt.show()

df.select("age").toPandas().hist()
df.toPandas().hist(color="k", bins=30, figsize=(15, 10))
plt.show()

df.select("age", "marital").toPandas().boxplot(by="marital")
plt.show()

df.select("age", "marital", "housing").toPandas().boxplot(by=["marital", "housing"], figsize=(20, 20))
plt.show()

Combining Code

Here is the combined code:

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.window import Window
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Initialize Spark session
spark = SparkSession.builder.appName("EDA_PySpark_Banking").getOrCreate()

# Load the data
df = spark.read.csv('bank-additional/bank-additional-full.csv', sep=';', header=True, inferSchema=True)

# Initial exploration
df.printSchema()
df.describe().show()

df.groupBy("y").count().show()
df.groupBy("marital").count().withColumn("normalize", col("count") / df.count()).show()

df.orderBy(col("duration").desc()).show(5)
df.orderBy(col("age").asc(), col("duration").desc()).show(5)

df.select([max(c).alias(c) for c in df.columns]).show()
df = df.withColumn("y", when(col("y") == "yes", 1).otherwise(0))

df.groupBy().agg(mean("y")).show()
df.filter(df["y"] == 1).agg(*[mean(c).alias(c) for c in df.columns]).show()

acd = df.filter(df["y"] == 1).agg(round(mean("duration"), 2)).collect()[0][0]
acd_in_min = acd // 60
print("Average call duration for attracted clients =", acd_in_min, "min", int(acd) % 60, "sec")

avg_age = df.filter((df["y"] == 1) & (df["marital"] == "single")).agg(mean("age")).collect()[0][0]
print("Average age of attracted clients =", int(avg_age), "years")
df.orderBy(desc("age")).limit(1).show()

df.groupBy("y", "marital").count().show()
df.groupBy("y", "marital").count().withColumn("normalize", col("count") / sum("count").over(Window.partitionBy("y"))).show()
df.groupBy("job").agg(mean("age"), mean("duration")).show()

# Plots
pandas_df = df.select("age", "duration", "campaign").toPandas()
pd.plotting.scatter_matrix(pandas_df, figsize=(15, 15), diagonal="kde")
plt.show()

df.select("age").toPandas().hist()
df.toPandas().hist(color="k", bins=30, figsize=(15, 10))
plt.show()

df.select("age", "marital").toPandas().boxplot(by="marital")
plt.show()

df.select("age", "marital", "housing").toPandas().boxplot(by=["marital", "housing"], figsize=(20, 20))
plt.show()

2.Dynamic list of variables Creation for ETL Jobs

Let us create One or Multiple dynamic lists of variables and save it in dictionary or Array or other datastructure for further repeating use in Pyspark projects specially for ETL jobs. Variable names are in form of dynamic names for example Month_202401 to Month_202312 for 24 months ( Take these 24 month backdated or as per current month’s Progression). Later we will use this dictionary to create 24 csv files by filtering on year and month. we will also use to create arrays based on this dynamic dictionary. We will create custom excel files for 24 year months combination where column names are also in form of above 24 { year and months combination such as xyz_2404_04 to xyz_2404_12 , abc_xyz_2404_0405 to abc_xyz_2405_1201}. This requirement is specifically for Pyspark based ETL projects.

Creating dynamic lists of variables and saving them in a dictionary or array for further use in PySpark ETL jobs is an excellent idea to streamline and automate the process. Here’s a step-by-step guide to achieve this in a PySpark-based project.

Step 1: Generate Dynamic Variable Names

First, we need to generate dynamic variable names for the past 24 months from the current month. We’ll store these variable names in a dictionary for easy access.

from datetime import datetime, timedelta
import pandas as pd
import pyspark.sql.functions as F
from pyspark.sql import SparkSession
import calendar

# Initialize SparkSession
spark = SparkSession.builder
    .appName("DynamicVariablesETL")
    .getOrCreate()

# Get current date
current_date = datetime.now()

# Create a dictionary to hold variable names
dynamic_vars = {}

# Generate variable names for the past 24 months
for i in range(24):
    date = current_date - timedelta(days=i*30)
    year_month = date.strftime('%Y%m')
    dynamic_vars[f'Month_{year_month}'] = None

# Print the generated variable names
print(dynamic_vars)

Step 2: Create CSV Files by Filtering on Year and Month

Next, we’ll use the dynamic variable names to filter data and create CSV files. For simplicity, let’s assume we have a sample DataFrame to filter.

# Sample DataFrame
data = {
    'date': [datetime.now() - timedelta(days=i*15) for i in range(50)],
    'value': [i for i in range(50)]
}

df = pd.DataFrame(data)
spark_df = spark.createDataFrame(df)

# Function to create CSV files based on dynamic variables
def create_csv_files(spark_df, dynamic_vars):
    for var in dynamic_vars.keys():
        year_month = var.split('_')[1]
        year = int(year_month[:4])
        month = int(year_month[4:])

        filtered_df = spark_df.filter((F.year("date") == year) & (F.month("date") == month))

        if filtered_df.count() > 0:
            filtered_df.write.csv(f"/path/to/output/{var}.csv")

create_csv_files(spark_df, dynamic_vars)

Step 3: Create Arrays Based on Dynamic Dictionary

We can also create arrays based on the dynamic dictionary.

# Create arrays from dynamic dictionary
dynamic_arrays = [f'Month_{date.strftime("%Y%m")}' for date in [current_date - timedelta(days=i*30) for i in range(24)]]

print(dynamic_arrays)

Step 4: Create Custom Excel Files

Lastly, we can create custom Excel files with columns named based on the dynamic dictionary.

import openpyxl
from openpyxl.utils.dataframe import dataframe_to_rows

# Function to create custom Excel files
def create_excel_files(dynamic_vars):
    for var in dynamic_vars.keys():
        year_month = var.split('_')[1]
        year = year_month[:4]
        month = year_month[4:]

        # Create a DataFrame with custom columns
        data = {
            f'xyz_{year}_{month}': [i for i in range(10)],
            f'abc_xyz_{year}_{month}{month}': [i for i in range(10)]
        }

        df = pd.DataFrame(data)

        # Create an Excel file
        file_name = f'/path/to/output/{var}.xlsx'
        writer = pd.ExcelWriter(file_name, engine='openpyxl')

        df.to_excel(writer, sheet_name='Sheet1', index=False)

        writer.save()

create_excel_files(dynamic_vars)

Combining It All Together

Here is the complete script that combines all the steps above.

from datetime import datetime, timedelta
import pandas as pd
import pyspark.sql.functions as F
from pyspark.sql import SparkSession
import calendar

# Initialize SparkSession
spark = SparkSession.builder
    .appName("DynamicVariablesETL")
    .getOrCreate()

# Get current date
current_date = datetime.now()

# Create a dictionary to hold variable names
dynamic_vars = {}

# Generate variable names for the past 24 months
for i in range(24):
    date = current_date - timedelta(days=i*30)
    year_month = date.strftime('%Y%m')
    dynamic_vars[f'Month_{year_month}'] = None

# Sample DataFrame
data = {
    'date': [datetime.now() - timedelta(days=i*15) for i in range(50)],
    'value': [i for i in range(50)]
}

df = pd.DataFrame(data)
spark_df = spark.createDataFrame(df)

# Function to create CSV files based on dynamic variables
def create_csv_files(spark_df, dynamic_vars):
    for var in dynamic_vars.keys():
        year_month = var.split('_')[1]
        year = int(year_month[:4])
        month = int(year_month[4:])

        filtered_df = spark_df.filter((F.year("date") == year) & (F.month("date") == month))

        if filtered_df.count() > 0:
            filtered_df.write.csv(f"/path/to/output/{var}.csv")

create_csv_files(spark_df, dynamic_vars)

# Create arrays from dynamic dictionary
dynamic_arrays = [f'Month_{date.strftime("%Y%m")}' for date in [current_date - timedelta(days=i*30) for i in range(24)]]

# Function to create custom Excel files
def create_excel_files(dynamic_vars):
    for var in dynamic_vars.keys():
        year_month = var.split('_')[1]
        year = year_month[:4]
        month = year_month[4:]

        # Create a DataFrame with custom columns
        data = {
            f'xyz_{year}_{month}': [i for i in range(10)],
            f'abc_xyz_{year}_{month}{month}': [i for i in range(10)]
        }

        df = pd.DataFrame(data)

        # Create an Excel file
        file_name = f'/path/to/output/{var}.xlsx'
        writer = pd.ExcelWriter(file_name, engine='openpyxl')

        df.to_excel(writer, sheet_name='Sheet1', index=False)

        writer.save()

create_excel_files(dynamic_vars)

This script demonstrates how to generate dynamic variable names, filter data to create CSV files, and create custom Excel files with dynamically generated columns. You can customize the paths and logic as needed for your specific ETL job in PySpark.

2.SAS project to Pyspark Migration- merging, joining, transposing , lead/rank functions, data validation with PROC FREQ, error handling, macro variables, and macros

Let us create a comprehensive SAS project that involves merging, joining, transposing large tables, applying PROC SQL lead/rank functions, performing data validation with PROC FREQ, and incorporating error handling, macro variables, and macros for various functional tasks.

Step 1: Set Up Macro Variables for Date Values

%let start_date = '01JAN2023'd;
%let end_date = '31DEC2023'd;

Step 2: Define Macros for Various Functional Tasks

%macro import_data(libname=, dataset=, filepath=);
    proc import datafile="&filepath" out=&libname..&dataset dbms=csv replace;
    getnames=yes;
    run;
%mend import_data;

%macro join_tables(libname=, table1=, table2=, out_table=, join_condition=);
    proc sql;
        create table &libname..&out_table as
        select a.*, b.*
        from &libname..&table1 as a
        left join &libname..&table2 as b
        on &join_condition;
    quit;
%mend join_tables;

%macro transpose_table(libname=, in_table=, out_table=, id_var=, var=);
    proc transpose data=&libname..&in_table out=&libname..&out_table;
    by &id_var;
    var &var;
    run;
%mend transpose_table;

%macro validate_data(libname=, table=, var=);
    proc freq data=&libname..&table;
    tables &var / missing;
    run;
%mend validate_data;

%macro apply_rank(libname=, table=, var=, out_table=);
    proc sql;
        create table &libname..&out_table as
        select *, rank() over (order by &var desc) as rank
        from &libname..&table;
    quit;
%mend apply_rank;

Step 3: Import Large Tables

%import_data(libname=work, dataset=table1, filepath='/path/to/table1.csv');
%import_data(libname=work, dataset=table2, filepath='/path/to/table2.csv');

Step 4: Merge and Join Tables

%join_tables(libname=work, table1=table1, table2=table2, out_table=merged_table, join_condition=a.id=b.id);

Step 5: Transpose the Data

%transpose_table(libname=work, in_table=merged_table, out_table=transposed_table, id_var=id, var=some_var);

Step 6: Apply PROC SQL Lead/Rank Functions

%apply_rank(libname=work, table=transposed_table, var=some_var, out_table=ranked_table);

Step 7: Validate Data with PROC FREQ

%validate_data(libname=work, table=ranked_table, var=some_var);

Step 8: Error Handling

Ensure error handling by checking the existence of datasets and proper execution of steps.

%macro check_dataset(libname=, dataset=);
    %if %sysfunc(exist(&libname..&dataset)) %then %do;
        %put NOTE: Dataset &libname..&dataset exists.;
    %end;
    %else %do;
        %put ERROR: Dataset &libname..&dataset does not exist.;
        %abort cancel;
    %end;
%mend check_dataset;

%check_dataset(libname=work, dataset=ranked_table);

Full Example in One Go

Putting it all together:

%let start_date = '01JAN2023'd;
%let end_date = '31DEC2023'd;

%macro import_data(libname=, dataset=, filepath=);
    proc import datafile="&filepath" out=&libname..&dataset dbms=csv replace;
    getnames=yes;
    run;
%mend import_data;

%macro join_tables(libname=, table1=, table2=, out_table=, join_condition=);
    proc sql;
        create table &libname..&out_table as
        select a.*, b.*
        from &libname..&table1 as a
        left join &libname..&table2 as b
        on &join_condition;
    quit;
%mend join_tables;

%macro transpose_table(libname=, in_table=, out_table=, id_var=, var=);
    proc transpose data=&libname..&in_table out=&libname..&out_table;
    by &id_var;
    var &var;
    run;
%mend transpose_table;

%macro validate_data(libname=, table=, var=);
    proc freq data=&libname..&table;
    tables &var / missing;
    run;
%mend validate_data;

%macro apply_rank(libname=, table=, var=, out_table=);
    proc sql;
        create table &libname..&out_table as
        select *, rank() over (order by &var desc) as rank
        from &libname..&table;
    quit;
%mend apply_rank;

%macro check_dataset(libname=, dataset=);
    %if %sysfunc(exist(&libname..&dataset)) %then %do;
        %put NOTE: Dataset &libname..&dataset exists.;
    %end;
    %else %do;
        %put ERROR: Dataset &libname..&dataset does not exist.;
        %abort cancel;
    %end;
%mend check_dataset;

/* Import large tables */
%import_data(libname=work, dataset=table1, filepath='/path/to/table1.csv');
%import_data(libname=work, dataset=table2, filepath='/path/to/table2.csv');

/* Merge and join tables */
%join_tables(libname=work, table1=table1, table2=table2, out_table=merged_table, join_condition=a.id=b.id);

/* Transpose the data */
%transpose_table(libname=work, in_table=merged_table, out_table=transposed_table, id_var=id, var=some_var);

/* Apply PROC SQL lead/rank functions */
%apply_rank(libname=work, table=transposed_table, var=some_var, out_table=ranked_table);

/* Validate data with PROC FREQ */
%validate_data(libname=work, table=ranked_table, var=some_var);

/* Check the existence of the final dataset */
%check_dataset(libname=work, dataset=ranked_table);

Explanation:

Macro Variables: Defined for date values.
Macros for Functional Tasks: Defined for importing data, joining tables, transposing tables, validating data, and applying ranking functions.
Data Import: Two large tables are imported using PROC IMPORT.
Data Merge and Join: Tables are merged and joined using PROC SQL.
Data Transposition: Transposing the merged table.
Rank Application: Applying the rank function.
Data Validation: Validating data using PROC FREQ.
Error Handling: Ensuring datasets exist and handling errors accordingly.

This setup provides a robust framework for handling complex data processing tasks in SAS, incorporating various best practices and functionalities.

Method1

Here is the equivalent PySpark code for the provided SAS script:

from pyspark.sql import SparkSession
from pyspark.sql.functions import rank, col

# Create a SparkSession object
spark = SparkSession.builder.appName("My App").getOrCreate()

# Define the start and end dates
start_date = "2023-01-01"
end_date = "2023-12-31"

# Import the data
table1 = spark.read.csv("/path/to/table1.csv", header=True, inferSchema=True)
table2 = spark.read.csv("/path/to/table2.csv", header=True, inferSchema=True)

# Register the DataFrames as temporary views
table1.createOrReplaceTempView("table1")
table2.createOrReplaceTempView("table2")

# Join the tables
merged_table = spark.sql("SELECT a.*, b.* FROM table1 a LEFT JOIN table2 b ON a.id = b.id")
merged_table.createOrReplaceTempView("merged_table")

# Transpose the data
transposed_table = merged_table.groupBy("id").pivot("some_var").sum()
transposed_table.createOrReplaceTempView("transposed_table")

# Apply the rank function
ranked_table = transposed_table.withColumn("rank", rank().over(Window.orderBy(col("some_var").desc())))
ranked_table.createOrReplaceTempView("ranked_table")

# Validate the data
ranked_table.select("some_var").summary().show()

# Check the existence of the final dataset
if ranked_table.count() > 0:
    print("Dataset exists.")
else:
    print("Dataset does not exist.")

Here’s a step-by-step explanation of the PySpark code:

Create a SparkSession object, which is the entry point to programming Spark with the Dataset and DataFrame API.
Define the start and end dates as strings.
Import the data from CSV files using the read.csv method, which returns a DataFrame.
Register the DataFrames as temporary views using the createOrReplaceTempView method, so they can be queried using SQL.
Join the tables using a SQL query, which is executed using the sql method.
Transpose the data using the groupBy and pivot methods, which are equivalent to the SAS proc transpose step.
Apply the rank function using the withColumn and rank methods, which are equivalent to the SAS proc sql step with the rank function.
Validate the data by computing summary statistics using the summary method, which is equivalent to the SAS proc freq step.
Check the existence of the final dataset by checking the count of rows using the count method. If the count is greater than 0, the dataset exists; otherwise, it does not exist.

Note that the PySpark code uses the DataFrame API, which is a more concise and expressive way of working with data compared to the SAS script. Additionally, the PySpark code uses SQL queries to perform the join and rank operations, which can be more efficient than using the SAS proc sql step.

Method 2

Here’s the modified PySpark code that translates SAS macros to functions and adds error handling:

Python

from pyspark.sql import SparkSession
from pyspark.sql.functions import rank, col

# Create a SparkSession object
spark = SparkSession.builder.appName("My App").getOrCreate()

# Define a function for importing data
def import_data(libname, dataset, filepath):
    try:
        df = spark.read.csv(filepath, header=True, inferSchema=True)
        df.createOrReplaceTempView(f"{libname}_{dataset}")
        return df
    except Exception as e:
        print(f"Error importing data: {e}")
        return None

# Define a function for joining tables
def join_tables(libname, table1, table2, out_table, join_condition):
    try:
        spark.sql(f"""
            CREATE TABLE {libname}_{out_table} AS
            SELECT a.*, b.*
            FROM {libname}_{table1} a
            LEFT JOIN {libname}_{table2} b
            ON {join_condition}
        """)
        return spark.table(f"{libname}_{out_table}")
    except Exception as e:
        print(f"Error joining tables: {e}")
        return None

# Define a function for transposing data
def transpose_table(libname, in_table, out_table, id_var, var):
    try:
        spark.sql(f"""
            CREATE TABLE {libname}_{out_table} AS
            SELECT {id_var}, {var}
            FROM {libname}_{in_table}
            GROUP BY {id_var}
            PIVOT ({var})
        """)
        return spark.table(f"{libname}_{out_table}")
    except Exception as e:
        print(f"Error transposing data: {e}")
        return None

# Define a function for applying the rank function
def apply_rank(libname, table, var, out_table):
    try:
        spark.sql(f"""
            CREATE TABLE {libname}_{out_table} AS
            SELECT *, RANK() OVER (ORDER BY {var} DESC) AS rank
            FROM {libname}_{table}
        """)
        return spark.table(f"{libname}_{out_table}")
    except Exception as e:
        print(f"Error applying rank function: {e}")
        return None

# Define a function for validating data
def validate_data(libname, table, var):
    try:
        spark.sql(f"""
            SELECT {var}, COUNT(*) AS count
            FROM {libname}_{table}
            GROUP BY {var}
        """).show()
    except Exception as e:
        print(f"Error validating data: {e}")

# Define a function for checking the existence of a dataset
def check_dataset(libname, dataset):
    try:
        if spark.catalog.tableExists(f"{libname}_{dataset}"):
            print("Dataset exists.")
        else:
            print("Dataset does not exist.")
    except Exception as e:
        print(f"Error checking dataset existence: {e}")

# Call the functions
table1 = import_data("work", "table1", "/path/to/table1.csv")
table2 = import_data("work", "table2", "/path/to/table2.csv")

merged_table = join_tables("work", "table1", "table2", "merged_table", "a.id = b.id")

transposed_table = transpose_table("work", "merged_table", "transposed_table", "id", "some_var")

ranked_table = apply_rank("work", "transposed_table", "some_var", "ranked_table")

validate_data("work", "ranked_table", "some_var")

check_dataset("work", "ranked_table")

In this modified code, I’ve defined separate functions for each of the SAS macros, and added error handling using try-except blocks. If an error occurs during the execution of a function, the error message is printed and the function returns None.

Method 3

Here’s the modified PySpark code that uses the sql method for the transpose and rank operations, and defines classes, objects, and functions to mimic the SAS macros:

Python

from pyspark.sql import SparkSession
from pyspark.sql.functions import rank, col

# Create a SparkSession object
spark = SparkSession.builder.appName("My App").getOrCreate()

# Define the start and end dates
start_date = "2023-01-01"
end_date = "2023-12-31"

# Define a class for importing data
class ImportData:
    def __init__(self, libname, dataset, filepath):
        self.libname = libname
        self.dataset = dataset
        self.filepath = filepath

    def execute(self):
        df = spark.read.csv(self.filepath, header=True, inferSchema=True)
        df.createOrReplaceTempView(f"{self.libname}_{self.dataset}")
        return df

# Define a class for joining tables
class JoinTables:
    def __init__(self, libname, table1, table2, out_table, join_condition):
        self.libname = libname
        self.table1 = table1
        self.table2 = table2
        self.out_table = out_table
        self.join_condition = join_condition

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT a.*, b.*
            FROM {self.libname}_{self.table1} a
            LEFT JOIN {self.libname}_{self.table2} b
            ON {self.join_condition}
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

# Define a class for transposing data
class TransposeTable:
    def __init__(self, libname, in_table, out_table, id_var, var):
        self.libname = libname
        self.in_table = in_table
        self.out_table = out_table
        self.id_var = id_var
        self.var = var

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT {self.id_var}, {self.var}
            FROM {self.libname}_{self.in_table}
            GROUP BY {self.id_var}
            PIVOT ({self.var})
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

# Define a class for applying the rank function
class ApplyRank:
    def __init__(self, libname, table, var, out_table):
        self.libname = libname
        self.table = table
        self.var = var
        self.out_table = out_table

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT *, RANK() OVER (ORDER BY {self.var} DESC) AS rank
            FROM {self.libname}_{self.table}
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

# Define a class for validating data
class ValidateData:
    def __init__(self, libname, table, var):
        self.libname = libname
        self.table = table
        self.var = var

    def execute(self):
        spark.sql(f"""
            SELECT {self.var}, COUNT(*) AS count
            FROM {self.libname}_{self.table}
            GROUP BY {self.var}
        """).show()

# Define a class for checking the existence of a dataset
class CheckDataset:
    def __init__(self, libname, dataset):
        self.libname = libname
        self.dataset = dataset

    def execute(self):
        if spark.catalog.tableExists(f"{self.libname}_{self.dataset}"):
            print("Dataset exists.")
        else:
            print("Dataset does not exist.")

# Create instances of the classes and execute the methods
import_data = ImportData("work", "table1", "/path/to/table1.csv")
table1 = import_data.execute()

import_data = ImportData("work", "table2", "/path/to/table2.csv")
table2 = import_data.execute()

join_tables = JoinTables("work", "table1", "table2", "merged_table", "a.id = b.id")
merged_table = join_tables.execute()

transpose_table = TransposeTable("work", "merged_table", "transposed_table", "id", "some_var")
transposed_table = transpose_table.execute()

apply_rank = ApplyRank("work", "transposed_table", "some_var", "ranked_table")
ranked_table = apply_rank.execute()

validate_data = ValidateData("work", "ranked_table", "some_var")
validate_data.execute()

check_dataset = CheckDataset("work", "ranked_table")
check_dataset.execute()

In this modified code, we define classes for each of the SAS macros, and create instances of these classes to execute

step by step explanation is still left

Here is a step-by-step explanation of the modified PySpark code:

Step 1: Create a SparkSession object

spark = SparkSession.builder.appName("My App").getOrCreate()

This line creates a SparkSession object, which is the entry point to programming Spark with the Dataset and DataFrame API.

Step 2: Define the start and end dates

start_date = "2023-01-01"
end_date = "2023-12-31"

These lines define the start and end dates as strings.

Step 3: Define a class for importing data

class ImportData:
    def __init__(self, libname, dataset, filepath):
        self.libname = libname
        self.dataset = dataset
        self.filepath = filepath

    def execute(self):
        df = spark.read.csv(self.filepath, header=True, inferSchema=True)
        df.createOrReplaceTempView(f"{self.libname}_{self.dataset}")
        return df

This class defines a method for importing data from a CSV file. The execute method reads the CSV file using the spark.read.csv method, creates a temporary view using the createOrReplaceTempView method, and returns the DataFrame.

Step 4: Define a class for joining tables

class JoinTables:
    def __init__(self, libname, table1, table2, out_table, join_condition):
        self.libname = libname
        self.table1 = table1
        self.table2 = table2
        self.out_table = out_table
        self.join_condition = join_condition

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT a.*, b.*
            FROM {self.libname}_{self.table1} a
            LEFT JOIN {self.libname}_{self.table2} b
            ON {self.join_condition}
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

This class defines a method for joining two tables. The execute method uses the spark.sql method to execute a SQL query that joins the two tables, creates a new table with the joined data, and returns the resulting DataFrame.

Step 5: Define a class for transposing data

class TransposeTable:
    def __init__(self, libname, in_table, out_table, id_var, var):
        self.libname = libname
        self.in_table = in_table
        self.out_table = out_table
        self.id_var = id_var
        self.var = var

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT {self.id_var}, {self.var}
            FROM {self.libname}_{self.in_table}
            GROUP BY {self.id_var}
            PIVOT ({self.var})""")
        return spark.table(f"{self.libname}_{self.out_table}")

This class defines a method for transposing data. The execute method uses the spark.sql method to execute a SQL query that transposes the data, creates a new table with the transposed data, and returns the resulting DataFrame.

Step 6: Define a class for applying the rank function

class ApplyRank:
    def __init__(self, libname, table, var, out_table):
        self.libname = libname
        self.table = table
        self.var = var
        self.out_table = out_table

    def execute(self):
        spark.sql(f"""
            CREATE TABLE {self.libname}_{self.out_table} AS
            SELECT *, RANK() OVER (ORDER BY {self.var} DESC) AS rank
            FROM {self.libname}_{self.table}
        """)
        return spark.table(f"{self.libname}_{self.out_table}")

This class defines a method for applying the rank function. The execute method uses the spark.sql method to execute a SQL query that applies the rank function, creates a new table with the ranked data, and returns the resulting DataFrame.

Step 7: Define a class for validating data

class ValidateData:
    def __init__(self, libname, table, var):
        self.libname = libname
        self.table = table
        self.var = var

    def execute(self):
        spark.sql(f"""
            SELECT {self.var}, COUNT(*) AS count
            FROM {self.libname}_{self.table}
            GROUP BY {self.var}
        """).show()

This class defines a method for validating data. The execute method uses the spark.sql method to execute a SQL query that groups the data by the specified variable and counts the number of rows in each group, and displays the results.

for dividing a monthly PySpark script into multiple code steps. Each step will be saved in the `code` column of a control DataFrame and executed sequentially. The script will include error handling and pre-checks to ensure source tables are present or Oracle tables have data before starting each step. The control DataFrame and status DataFrame for each month will be appended to a Hive or Oracle table to maintain historical data.

Here is a detailed approach for dividing a monthly PySpark script into multiple code steps. Each step will be saved in the code column of a control DataFrame and executed sequentially. The script will include error handling and pre-checks to ensure source tables are present or Oracle tables have data before starting each step. The control DataFrame and status DataFrame for each month will be appended to a Hive or Oracle table to maintain historical data.

Step 1: Prepare the Control DataFrame with Code Steps

Sample Monthly PySpark Script

Below is a sample PySpark script that reads data from Hive tables, performs transformations, and writes the output to Hive tables.

Sample PySpark Script

from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from datetime import datetime

spark = SparkSession.builder
    .appName("Monthly PySpark Script")
    .enableHiveSupport()
    .getOrCreate()

# Define the current month in MMyyyy format
current_month = datetime.now().strftime("%m%Y")

# Sample code steps to be executed sequentially
code_steps = [
    # Step 1: Check if source Hive table exists and has data
    f"""
    source_table = "source_db.source_table"
    if not spark.catalog.tableExists(source_table):
        raise ValueError("Source table does not exist.")
    df = spark.table(source_table)
    if df.count() == 0:
        raise ValueError("Source table is empty.")
    """,

    # Step 2: Read data from an Oracle table (Assuming JDBC connection properties are set)
    f"""
    oracle_url = "jdbc:oracle:thin:@hostname:port:service_name"
    oracle_properties = {{
        "user": "username",
        "password": "password"
    }}
    oracle_df = spark.read.jdbc(oracle_url, "oracle_schema.oracle_table",   properties=oracle_properties)
    if oracle_df.count() == 0:
        raise ValueError("Oracle table is empty.")
    print("Data read from Oracle table:", oracle_df.count(), "rows")
    """,

    # Step 3: Perform a transformation
    f"""
    transformed_df = df.withColumn("new_col", col("existing_col") * 2)
    print("Transformation applied:", transformed_df.count(), "rows")
    """,

    # Step 4: Join with Oracle data
    f"""
    final_df = transformed_df.join(oracle_df, "join_key")
    print("Joined data:", final_df.count(), "rows")
    """,

    # Step 5: Write data to the target Hive table
    f"""
    target_table = "target_db.target_table"
    final_df.write.mode("overwrite").saveAsTable(target_table)
    print("Data written to target table")
    """
]

# Create a Control DataFrame
from pyspark.sql import Row
control_data = [Row(serial_no=i+1, code=step, month_period=current_month) for i, step in enumerate(code_steps)]
control_df = spark.createDataFrame(control_data)
control_df.show(truncate=False)

Step 2: Create a Status Table

Create a status table to store the execution status of each code step. This table can be created in Hive for simplicity.

# Create a status DataFrame
status_schema = "serial_no INT, status STRING, message STRING, month_period STRING, row_count INT"
spark.sql(f"CREATE TABLE IF NOT EXISTS control_db.status_table ({status_schema})")

Step 3: Execute Each Code Step with Error Handling and Status Tracking

Execute each code step sequentially, log the status, any error messages, and the row count of the output tables.

# Function to log status
def log_status(serial_no, status, message, row_count=0):
    spark.sql(f"""
        INSERT INTO control_db.status_table
        VALUES ({serial_no}, '{status}', '{message}', '{current_month}', {row_count})
    """)

# Execute each code snippet
code_snippets = control_df.select("serial_no", "code").collect()

for row in code_snippets:
    serial_no = row["serial_no"]
    code_snippet = row["code"]
    
    try:
        print(f"Executing step {serial_no}...")
        exec(code_snippet)
        
        # Fetch the row count of the output table if this step writes to a table
        if "write.mode" in code_snippet:
            output_table = code_snippet.split('"')[1]
            row_count = spark.table(output_table).count()
        else:
            row_count = 0
        
        log_status(serial_no, "SUCCESS", "Executed successfully", row_count)
    except Exception as e:
        error_message = str(e).replace("'", "")
        log_status(serial_no, "FAILED", error_message)
        print(f"Error executing step {serial_no}: {error_message}")
        break

# Example to display the status table
spark.sql("SELECT * FROM control_db.status_table").show()

Full Example Script

Here’s the complete script:

from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from datetime import datetime
from pyspark.sql import Row

# Initialize Spark session
spark = SparkSession.builder 
    .appName("Execute Code Snippets from Control DataFrame") 
    .enableHiveSupport() 
    .getOrCreate()

# Define the current month in MMyyyy format
current_month = datetime.now().strftime("%m%Y")

# Define code snippets
code_steps = [
    # Step 1: Check if source Hive table exists and has data
    f"""
    source_table = "source_db.source_table"
    if not spark.catalog.tableExists(source_table):
        raise ValueError("Source table does not exist.")
    df = spark.table(source_table)
    if df.count() == 0:
        raise ValueError("Source table is empty.")
    """,

    # Step 2: Read data from an Oracle table
    f"""
    oracle_url = "jdbc:oracle:thin:@hostname:port:service_name"
    oracle_properties = {{
        "user": "username",
        "password": "password"
    }}
    oracle_df = spark.read.jdbc(oracle_url, "oracle_schema.oracle_table", properties=oracle_properties)
    if oracle_df.count() == 0:
        raise ValueError("Oracle table is empty.")
    print("Data read from Oracle table:", oracle_df.count(), "rows")
    """,

    # Step 3: Perform a transformation
    f"""
    transformed_df = df.withColumn("new_col", col("existing_col") * 2)
    print("Transformation applied:", transformed_df.count(), "rows")
    """,

    # Step 4: Join with Oracle data
    f"""
    final_df = transformed_df.join(oracle_df, "join_key")
    print("Joined data:", final_df.count(), "rows")
    """,

    # Step 5: Write data to the target Hive table
    f"""
    target_table = "target_db.target_table"
    final_df.write.mode("overwrite").saveAsTable(target_table)
    print("Data written to target table")
    """
]

# Create a Control DataFrame
control_data = [Row(serial_no=i+1, code=step, month_period=current_month) for i, step in enumerate(code_steps)]
control_df = spark.createDataFrame(control_data)
control_df.show(truncate=False)

# Create a status table
status_schema = "serial_no INT, status STRING, message STRING, month_period STRING, row_count INT"
spark.sql(f"CREATE TABLE IF NOT EXISTS control_db.status_table ({status_schema})")

# Function to log status
def log_status(serial_no, status, message, row_count=0):
    spark.sql(f"""
        INSERT INTO control_db.status_table
        VALUES ({serial_no}, '{status}', '{message}', '{current_month}', {row_count})
    """)

# Execute each code snippet
code_snippets = control_df.select("serial_no", "code").collect()

for row in code_snippets:
    serial_no = row["serial_no"]
    code_snippet = row["code"]
    
    try:
        print(f"Executing step {serial_no}...")
        exec(code_snippet)
        
        # Fetch the row count of the output table if this step writes to a table
        if "write.mode" in code_snippet:
            output_table = code_snippet.split('"')[1]
            row_count = spark.table(output_table).count()
        else:
            row_count = 0
        
        log_status(serial_no, "SUCCESS", "Executed successfully", row_count)
    except Exception as e:
        error_message = str(e).replace("'", "")
        log_status(serial_no, "FAILED", error_message)
        print(f"Error executing step {serial_no}: {error_message}")
        break

# Example to display the status table
spark.sql("SELECT * FROM control_db.status_table").show()

To execute dynamic code snippets stored in a DataFrame (or any other source) in PySpark, you can use the exec function. This can be particularly useful when you have a control table that stores PySpark code snippets to be executed sequentially. Below is a complete example of how to achieve this, including error handling, logging, and updating status.

Example: Dynamic Code Execution in PySpark

1. Initialize Spark Session

from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from datetime import datetime

# Initialize Spark session
spark = SparkSession.builder 
    .appName("PySpark Dynamic Code Execution Example") 
    .enableHiveSupport() 
    .getOrCreate()

2. Create Sample Control DataFrame

# Sample control DataFrame
control_data = [
    (1, "df1 = spark.range(0, 10)"),
    (2, "df2 = df1.withColumn('square', F.col('id') ** 2)"),
    (3, "df2.show()")
]
control_columns = ["serial_no", "code_snippet"]

control_df = spark.createDataFrame(control_data, control_columns)
control_df.show(truncate=False)

3. Create Log and Status DataFrames

# Define schema for log and status tables
log_schema = "timestamp STRING, message STRING, level STRING"
status_schema = "step INT, status STRING, row_count INT, timestamp STRING"

# Initialize empty log and status DataFrames
log_df = spark.createDataFrame([], schema=log_schema)
status_df = spark.createDataFrame([], schema=status_schema)

4. Define Functions for Error Handling and Logging

# Function to log messages
def log(message, level="INFO"):
    global log_df
    log_entry = [(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), message, level)]
    log_df = log_df.union(spark.createDataFrame(log_entry, schema=log_schema))

# Function to update status
def update_status(step, status, row_count=0):
    global status_df
    status_entry = [(step, status, row_count, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))]
    status_df = status_df.union(spark.createDataFrame(status_entry, schema=status_schema))

5. Execute Code Snippets Sequentially

# Function to execute code snippets
def execute_code(control_df):
    for row in control_df.collect():
        step = row["serial_no"]
        code = row["code_snippet"]
        try:
            # Execute the code snippet
            exec(code, globals())
            log(f"Step {step} executed successfully", level="INFO")
            # Optionally, you can get the row count of a DataFrame if the code produces one
            row_count = eval(code.split('=')[0].strip()).count() if '=' in code else 0
            update_status(step, "SUCCESS", row_count)
        except Exception as e:
            log(f"Step {step} failed: {e}", level="ERROR")
            update_status(step, "FAILED")
            break

# Execute the control DataFrame
execute_code(control_df)

# Show log and status tables
log_df.show(truncate=False)
status_df.show(truncate=False)

Explanation

Initialization: A Spark session is initialized, and a sample control DataFrame is created with code snippets.
Log and Status DataFrames: Empty DataFrames for logs and status are initialized with appropriate schemas.
Logging Functions: Functions for logging messages and updating the status are defined.
Execute Code Snippets: The execute_code function iterates over the control DataFrame, executing each code snippet using exec. It logs the execution status and updates the status table.
Execution and Display: The control DataFrame is executed, and the log and status tables are displayed.

Scheduling the Script

To schedule the script to run automatically, you can use scheduling tools like Apache Airflow, cron jobs, or any other job scheduler that fits your environment. Here’s an example of how you might schedule it using a simple cron job in Linux:

Create a Python script (e.g., pyspark_script.py) with the above code.
Add a cron job to execute the script at the desired interval.

bashCopy code# Open the cron table for editing
crontab -e

# Add a line to run the script at the desired schedule (e.g., every day at midnight)
0 0 * * * /path/to/spark/bin/spark-submit /path/to/pyspark_script.py

This setup will run your PySpark script automatically according to the schedule specified in the cron job.

Building a ETL Data pipeline in Pyspark and using Pandas and Matplotlib for Further Processing

Project Alert:- Building a ETL Data pipeline in Pyspark and using Pandas and Matplotlib for Further Processing. For Deployment we will consider using Bitbucket and Genkins.

We will build a Data pipeline from BDL Reading Hive Tables in Pyspark and executing Pyspark scrip for Complex Transformations on Data via Py spark Sql and Dataframe ApI. Some of our sources will be from Oracle Tables and CSV files stored in Server specific Location. We will read these and join with our processed BDL data to add more data information( Variables). Our Target tables are BDL Hive tables in paraquet format saved in another schema. We will also transform Big spark dataframes to consolidated data to be consumed as Pandas Dataframes which will be used for analysis using Pandas and Visualisation with Matplotlib. These data can be saved as CSV, Excel or Oracle Tables.

tep 1: Environment Setup

Spark Session Initialization: Set up Spark with Hive support.
Dependencies: Ensure you have the necessary libraries installed.

Step 2: Data Extraction

Hive Tables: Read from Hive.
Oracle Tables: Use JDBC to read from Oracle.
CSV Files: Read from local/remote storage.

Step 3: Data Transformation

PySpark SQL and DataFrame API: Perform transformations.
Joining Data: Combine data from different sources.

Step 4: Data Loading

Write to Hive: Save results in Parquet format.
Convert to Pandas: For further analysis.
Save as CSV/Excel/Oracle: Export data for other uses.

Step 5: Data Analysis and Visualization

Pandas for Analysis: Use Pandas DataFrame.
Matplotlib for Visualization: Create charts and graphs.

Step 6: Deployment with Bitbucket and Jenkins

Version Control with Bitbucket: Push code to Bitbucket.
CI/CD with Jenkins: Automate deployment.

Example Code

Spark Session Initialization

from pyspark.sql import SparkSession

spark = SparkSession.builder 
    .appName("ETL Pipeline") 
    .enableHiveSupport() 
    .getOrCreate()

Data Extraction

# Reading from Hive
hive_df = spark.sql("SELECT * FROM bdl_database.bdl_table")

# Reading from Oracle
oracle_df = spark.read.format("jdbc").options(
    url="jdbc:oracle:thin:@//your_oracle_host:1521/your_oracle_db",
    driver="oracle.jdbc.driver.OracleDriver",
    dbtable="your_oracle_table",
    user="your_user",
    password="your_password"
).load()

# Reading from CSV
csv_df = spark.read.csv("path/to/your/csvfile.csv", header=True, inferSchema=True)

Data Transformation

# Example Transformation
hive_df_filtered = hive_df.filter(hive_df["column_name"] > 0)
oracle_df_selected = oracle_df.select("col1", "col2")
csv_df_transformed = csv_df.withColumn("new_col", csv_df["col3"] * 2)

# Joining Data
joined_df = hive_df_filtered.join(oracle_df_selected, "id", "inner") 
    .join(csv_df_transformed, "id", "inner")

Data Loading

#Save to Hive
joined_df.write.mode("overwrite").parquet("path/to/hive_table")

# Convert to Pandas for further analysis
pandas_df = joined_df.toPandas()

# Save as CSV and Excel
pandas_df.to_csv("path/to/save/yourdata.csv", index=False)
pandas_df.to_excel("path/to/save/yourdata.xlsx", index=False)

Data Analysis and Visualization

import matplotlib.pyplot as plt
import pandas as pd

# Pandas DataFrame Analysis
print(pandas_df.describe())

# Visualization
plt.figure(figsize=(10, 6))
pandas_df["your_column"].hist()
plt.title("Histogram of Your Column")
plt.xlabel("Values")
plt.ylabel("Frequency")
plt.show()

Deployment with Bitbucket and Jenkins

Push Code to Bitbucket:
- Initialize a Git repository.
- Add your code and commit.
- Push to your Bitbucket repository.
Set Up Jenkins for CI/CD:
- Install Jenkins and necessary plugins.
- Create a new Jenkins job.
- Configure the job to pull from your Bitbucket repository.
- Add build steps to run your PySpark script.

Jenkins Pipeline Script Example

pipeline {
    agent any

    stages {
        stage('Clone Repository') {
            steps {
                git 'https://bitbucket.org/your_repo.git'
            }
        }
        stage('Build and Test') {
            steps {
                sh 'spark-submit --master local[2] your_script.py'
            }
        }
        stage('Deploy') {
            steps {
                // Add deployment steps here
            }
        }
    }
}

Simplified Version of Complete Pipeline:-

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
import pandas as pd
import matplotlib.pyplot as plt

# Initialize Spark Session
spark = SparkSession.builder 
    .appName("DataPipeline") 
    .enableHiveSupport() 
    .getOrCreate()

# Read Data from Hive
hive_df = spark.sql("SELECT * FROM bdl_database.bdl_table")

# Read Data from Oracle
oracle_df = spark.read.format("jdbc").options(
    url="jdbc:oracle:thin:@//your_oracle_host:1521/your_oracle_db",
    driver="oracle.jdbc.driver.OracleDriver",
    dbtable="your_oracle_table",
    user="your_user",
    password="your_password"
).load()

# Read CSV Files
csv_df = spark.read.csv("path/to/your/csvfile.csv", header=True, inferSchema=True)

# Perform Transformations
# Example transformation: filter and select specific columns
hive_df_filtered = hive_df.filter(hive_df["column_name"] > 0)
oracle_df_selected = oracle_df.select("col1", "col2")
csv_df_transformed = csv_df.withColumn("new_col", csv_df["col3"] * 2)

# Join DataFrames
joined_df = hive_df_filtered.join(oracle_df_selected, hive_df_filtered["id"] == oracle_df_selected["id"], "inner")
final_df = joined_df.join(csv_df_transformed, joined_df["id"] == csv_df_transformed["id"], "inner")

# Save to Hive in Parquet format
final_df.write.mode("overwrite").parquet("path/to/hive_table")

# Convert to Pandas for further analysis
pandas_df = final_df.toPandas()

# Save Pandas DataFrame as CSV and Excel
pandas_df.to_csv("path/to/save/yourdata.csv", index=False)
pandas_df.to_excel("path/to/save/yourdata.xlsx", index=False)

# Visualize Data using Matplotlib
plt.figure(figsize=(10, 6))
pandas_df["your_column"].hist()
plt.title("Histogram of Your Column")
plt.xlabel("Values")
plt.ylabel("Frequency")
plt.show()

# Further Analysis with Pandas
print(pandas_df.describe())

# Close Spark Session
spark.stop()

String Manipulation on PySpark DataFrames

July 7, 2024

String manipulation is a common task in data processing. PySpark provides a variety of built-in functions for manipulating string columns in DataFrames. Below, we explore some of the most useful string manipulation functions and demonstrate how to use them with examples.

Common String Manipulation Functions

concat: Concatenates multiple string columns into one.
substring: Extracts a substring from a string column.
length: Computes the length of a string column.
trim, ltrim, rtrim: Trims whitespace from strings.
upper, lower: Converts strings to upper or lower case.
regexp_replace: Replaces substrings matching a regex pattern.
regexp_extract: Extracts substrings matching a regex pattern.
split: Splits a string column based on a delimiter.
replace: Replaces all occurrences of a substring with another substring.
translate: Replaces characters in a string based on a mapping.

Example Usage

1. Concatenation

Syntax:

from pyspark.sql import SparkSession
from pyspark.sql.functions import concat, lit

# Initialize Spark session
spark = SparkSession.builder.appName("string_manipulation").getOrCreate()

# Sample data
data = [("John", "Doe"), ("Jane", "Smith")]
columns = ["first_name", "last_name"]

# Create DataFrame
df = spark.createDataFrame(data, columns)

# Concatenate first and last names
df = df.withColumn("full_name", concat(df.first_name, lit(" "), df.last_name))

# Show result
df.show()

2. Substring Extraction

Syntax:

from pyspark.sql.functions import substring

# Extract first three characters of first_name
df = df.withColumn("initials", substring(df.first_name, 1, 3))

# Show result
df.show()

3. Length Calculation

Syntax:

from pyspark.sql.functions import length

# Calculate length of first_name
df = df.withColumn("name_length", length(df.first_name))

# Show result
df.show()

In PySpark, the length() function counts all characters, including:

✅ Leading/trailing spaces
✅ Special characters (!@#$%^, etc.)
✅ Unicode characters
✅ Numbers

✅ Example: Count Characters with Spaces and Specials

from pyspark.sql.functions import col, length

df = spark.createDataFrame([
    (" Alice ",),     # Leading/trailing space
    ("A$ha",),        # Special character
    ("  Bob  ",),     # More spaces
    ("Émilie",),      # Unicode
    ("",),            # Empty string
    (None,)           # NULL value
], ["first_name"])

df_with_len = df.withColumn("name_length", length(col("first_name")))
df_with_len.show(truncate=False)

✅ Output:

+-----------+------------+
|first_name |name_length |
+-----------+------------+
| Alice     |7           |
|A$ha       |4           |
|  Bob      |7           |
|Émilie     |6           |
|           |0           |
|null       |null        |
+-----------+------------+

🧠 Key Notes:

length() is character count, not byte count.
It includes all types of characters (whitespace, special, Unicode).
Use trim() if you want to remove spaces before counting.

⚠️ Compare With Trim:

from pyspark.sql.functions import trim

df_trimmed = df.withColumn("trimmed_length", length(trim(col("first_name"))))
df_trimmed.show()

4. Trimming

Syntax:

from pyspark.sql.functions import trim, ltrim, rtrim

# Trim whitespace from first_name
df = df.withColumn("trimmed_name", trim(df.first_name))

# Show result
df.show()

5. Case Conversion

Syntax:

from pyspark.sql.functions import upper, lower

# Convert first_name to upper case
df = df.withColumn("upper_name", upper(df.first_name))

# Convert last_name to lower case
df = df.withColumn("lower_name", lower(df.last_name))

# Show result
df.show()

6. Regex Replace

Syntax:

from pyspark.sql.functions import regexp_replace

# Replace all digits with asterisks
df = df.withColumn("masked_name", regexp_replace(df.first_name, "d", "*"))

# Show result
df.show()

7. Regex Extract

Syntax:

from pyspark.sql.functions import regexp_extract

# Extract digits from first_name
df = df.withColumn("extracted_digits", regexp_extract(df.first_name, "(d+)", 1))

# Show result
df.show()

8. Split

from pyspark.sql.functions import split

# Split full_name into first and last name
df = df.withColumn("split_name", split(df.full_name, " "))

# Show result
df.select("split_name").show(truncate=False)

9. Replace

from pyspark.sql.functions import expr

# Replace 'Doe' with 'Smith' in last_name
df = df.withColumn("updated_last_name", expr("replace(last_name, 'Doe', 'Smith')"))

# Show result
df.show()

10. Translate

Syntax:

from pyspark.sql.functions import translate

# Translate 'o' to '0' in last_name
df = df.withColumn("translated_last_name", translate(df.last_name, "o", "0"))

# Show result
df.show()

Comprehensive Example: Combining Multiple String Manipulations

Let’s create a project that combines multiple string manipulation operations on a DataFrame.

from pyspark.sql import SparkSession
from pyspark.sql.functions import concat, lit, substring, length, trim, upper, lower, regexp_replace, regexp_extract, split, expr, translate

# Initialize Spark session
spark = SparkSession.builder.appName("string_manipulation").getOrCreate()

# Sample data
data = [("John123", "Doe456"), ("Jane789", "Smith012")]
columns = ["first_name", "last_name"]

# Create DataFrame
df = spark.createDataFrame(data, columns)

# Perform various string manipulations
df = df.withColumn("full_name", concat(df.first_name, lit(" "), df.last_name))
df = df.withColumn("initials", substring(df.first_name, 1, 3))
df = df.withColumn("name_length", length(df.first_name))
df = df.withColumn("trimmed_name", trim(df.first_name))
df = df.withColumn("upper_name", upper(df.first_name))
df = df.withColumn("lower_name", lower(df.last_name))
df = df.withColumn("masked_name", regexp_replace(df.first_name, "d", "*"))
df = df.withColumn("extracted_digits", regexp_extract(df.first_name, "(d+)", 1))
df = df.withColumn("split_name", split(df.full_name, " "))
df = df.withColumn("updated_last_name", expr("replace(last_name, 'Doe456', 'Smith')"))
df = df.withColumn("translated_last_name", translate(df.last_name, "456", "789"))

# Show result
df.show(truncate=False)

Explanation

Concatenation: Combines the first_name and last_name with a space.
Substring Extraction: Extracts the first three characters of first_name.
Length Calculation: Computes the length of first_name.
Trimming: Trims any whitespace around first_name.
Case Conversion: Converts first_name to upper case and last_name to lower case.
Regex Replace: Replaces all digits in first_name with asterisks.
Regex Extract: Extracts digits from first_name.
Split: Splits the full_name into a list of first_name and last_name.
Replace: Replaces ‘Doe456’ with ‘Smith’ in last_name.
Translate: Translates ‘456’ to ‘789’ in last_name.

These examples demonstrate the versatility of string manipulation functions in PySpark, allowing for complex transformations and processing of text data.

Here’s a table summarizing common string manipulation functions in PySpark SQL, including regular expressions (regex):

Function	Description	Example (Input: “Hello World”)
initcap(str)	Converts first letter of each word to uppercase	“Hello World” -> “Hello World” (already capitalized words remain unchanged)
lower(str)	Converts all characters to lowercase	“Hello World” -> “hello world”
upper(str)	Converts all characters to uppercase	“Hello World” -> “HELLO WORLD”
lpad(str, length, pad_string)	Pads a string to the left with a specified string	“Hello World”, 15, “-” -> “—Hello World”
rpad(str, length, pad_string)	Pads a string to the right with a specified string	“Hello World”, 15, “-” -> “Hello World—“
ltrim(str)	Removes leading whitespace characters	” Hello World” -> “Hello World”
rtrim(str)	Removes trailing whitespace characters	“Hello World ” -> “Hello World”
trim(str)	Removes leading and trailing whitespace characters	” Hello World ” -> “Hello World”
length(str)	Returns the length (number of characters) of a string	“Hello World” -> 11
substr(str, pos, len)	Extracts a substring from a string	“Hello World”, 7, 5 -> “World”
instr(str, substr)	Returns the starting position of a substring within a string (0 if not found)	“Hello World”, “World” -> 7
concat(str1, str2…)	Concatenates (joins) multiple strings	“Hello”, ” “, “World” -> “Hello World”
concat_ws(sep, str1, str2…)	Concatenates strings with a specified separator	“Hello”, “,”, “World” -> “Hello,World”
regexp_extract(str, pattern)	Extracts a pattern (regular expression) from a string	“Hello_World”, r”[^]+” -> “_World”
regexp_replace(str, pattern, replacement)	Replaces a pattern (regular expression) in a string with a replacement string	“Hello_World”, r”_”, ” ” -> “Hello World”
split(str, delimiter)	Splits a string into an array based on a delimiter	“Hello,World,How”, “,” -> [“Hello”, “World”, “How”]
array_join(arr, sep)	Joins the elements of an array into a string with a specified separator	[“Hello”, “World”, “How”], “,” -> “Hello,World,How”
soundex(str)	Returns the Soundex code of a string (phonetic equivalent)	“Hello” -> “H400”
translate(str, remove, replace)	Removes characters from a string and replaces them with	“Hello World”, “e”, “” -> “Hllo World”
overlay(str, overlay_str, pos, len)	Replaces a substring within a string with another	“Hello World”, “there”, 6, 5 -> “Hello thered”
reverse(str)	Reverses the order of characters in a string	“Hello World” -> “dlroW olleH”
instrc(str, substr)	Returns the starting position of a substring within a string (case-insensitive, 0 if not found)	“Hello World”, “world” -> 7
levenshtein(str1, str2)	Calculates the Levenshtein distance (minimum number of edits) to transform one string to another	“Hello”, “Jello” -> 1

Summary in Detail:-

a guide on how to perform common string manipulation tasks in PySpark:-

concat: Concatenates two or more strings.

Syntax: concat(col1, col2, ..., colN)

Example:

from pyspark.sql.functions import concat, col

df = df.withColumn("Full Name", concat(col("First Name"), lit(" "), col("Last Name")))

substr: Extracts a substring from a string.

Syntax: substr(col, start, length)

Example:

from pyspark.sql.functions import substr, col

df = df.withColumn("First Name", substr(col("Name"), 1, 4))

split: Splits a string into an array of substrings.

Syntax: split(col, pattern)

Example:

from pyspark.sql.functions import split, col

df = df.withColumn("Address Parts", split(col("Address"), " "))

regex_extract: Extracts a substring using a regular expression.

Syntax: regex_extract(col, pattern, group)

Example:

from pyspark.sql.functions import regex_extract, col

df = df.withColumn("Phone Number", regex_extract(col("Contact Info"), "d{3}-d{3}-d{4}", 0))

translate: Replaces specified characters in a string.

Syntax: translate(col, matching, replace)

Example:

from pyspark.sql.functions import translate, col

df = df.withColumn("Clean Name", translate(col("Name"), "aeiou", "AEIOU"))

trim: Removes leading and trailing whitespace from a string.

Syntax: trim(col)

Example:

from pyspark.sql.functions import trim, col

df = df.withColumn("Clean Address", trim(col("Address")))

lower: Converts a string to lowercase.

Syntax: lower(col)

Example:

from pyspark.sql.functions import lower, col

df = df.withColumn("Lower Name", lower(col("Name")))

upper: Converts a string to uppercase.

Syntax: upper(col)

Example:

from pyspark.sql.functions import upper, col

df = df.withColumn("Upper Name", upper(col("Name")))

String Data Cleaning in PySpark

Here are some common string data cleaning functions in PySpark, along with their syntax and examples:

trim: Removes leading and trailing whitespace from a string.

Syntax: trim(col)

Example:

from pyspark.sql.functions import trim, col

df = df.withColumn("Clean Address", trim(col("Address")))

regexp_replace: Replaces substrings matching a regular expression.

Syntax: regexp_replace(col, pattern, replacement)

Example:

from pyspark.sql.functions import regexp_replace, col

df = df.withColumn("Clean Name", regexp_replace(col("Name"), "[^a-zA-Z]", ""))

replace: Replaces specified characters or substrings in a string.

Syntax: replace(col, matching, replace)

Example:

from pyspark.sql.functions import replace, col

df = df.withColumn("Clean Address", replace(col("Address"), " ", ""))

remove_accents: Removes accents from a string.

Syntax: remove_accents(col)

Example:

from pyspark.sql.functions import remove_accents, col

df = df.withColumn("Clean Name", remove_accents(col("Name")))

standardize: Standardizes a string by removing punctuation and converting to lowercase.

Syntax: standardize(col)

Example:

from pyspark.sql.functions import standardize, col

df = df.withColumn("Standardized Name", standardize(col("Name")))

Pyspark Dataframe programming – operations, functions, all statements, syntax with Examples

July 2, 2024

✅ What is a DataFrame in PySpark?

A DataFrame in PySpark is a distributed collection of data organized into named columns, similar to a table in a relational database or a Pandas DataFrame.

It is built on top of RDDs and provides:

Schema awareness (column names & data types)
High-level APIs (like select, groupBy, join)
Query optimization via Catalyst engine
Integration with SQL, Hive, Delta, Parquet, etc.

📊 DataFrame = RDD + Schema

Under the hood:

DataFrame = RDD[Row] + Schema

So while RDD is just distributed records (no structure), DataFrame adds:

Schema: like a table
Optimizations: Catalyst query planner
Better performance: due to SQL-style planning

📎 Example: From RDD to DataFrame

from pyspark.sql import Row
rdd = spark.sparkContext.parallelize([Row(name="Alice", age=30), Row(name="Bob", age=25)])

# Convert to DataFrame
df = spark.createDataFrame(rdd)

df.printSchema()
df.show()

📌 Output:

root
 |-- name: string
 |-- age: long
+-----+---+
|name |age|
+-----+---+
|Alice| 30|
|Bob  | 25|
+-----+---+

🆚 DataFrame vs RDD

Feature	RDD	DataFrame
Structure	Unstructured	Structured (schema: columns & types)
APIs	Low-level (map, filter, reduce)	High-level (select, groupBy, SQL)
Performance	Slower	Faster (optimized via Catalyst)
Optimization	Manual	Automatic (Catalyst + Tungsten)
Ease of Use	More code	Concise SQL-like operations
Use Cases	Fine-grained control, custom ops	Most ETL, Analytics, SQL workloads

🧠 When to Use What?

Use RDD if:
- You need fine-grained control (custom partitioning, complex transformations)
- You’re doing low-level transformations or working with unstructured data
Use DataFrame if:
- You want performance & ease
- You’re working with structured/semi-structured data (JSON, CSV, Parquet, Hive)
- You want to write SQL-like logic

🚀 Bonus: PySpark SQL + DataFrame

DataFrames integrate with SQL:

df.createOrReplaceTempView("people")
spark.sql("SELECT name FROM people WHERE age > 28").show()

✅ Summary

PySpark DataFrame is the go-to API for structured big data processing.
It’s built on RDDs, but adds structure and optimization.
Immutable and distributed just like RDDs, but more powerful for most real-world use cases.

In PySpark, DataFrames are immutable, just like RDDs.

✅ What Does Immutable Mean?

Once a DataFrame is created, it cannot be changed in-place. Any operation you perform returns a new DataFrame with the transformation applied.

🔁 Example:

df = spark.read.csv("data.csv", header=True)

# filter does not change df, it returns a new DataFrame
df_filtered = df.filter(df["price"] > 100)

# df is still the original
df.show()
df_filtered.show()

Here:

df is unchanged.
df_filtered is a new DataFrame with a subset of rows.

🧠 Why Are DataFrames Immutable?

Fault tolerance: Spark builds a lineage graph, so if a node fails, Spark can recompute lost partitions.
Optimizations: Catalyst optimizer rewrites queries knowing transformations don’t mutate the source.
Parallelism: Safe to operate across clusters without race conditions.

🔄 But You Can Reassign

While the DataFrame object is immutable, you can reassign the variable:

df = df.filter(df["price"] > 100)  # now df points to the new result

But this is just Python variable reassignment, not mutation.

✅ Summary

Concept	Mutable?	Notes
RDD	❌ No	Immutable
DataFrame	❌ No	Immutable, optimized via Catalyst
Variable (Python)	✅ Yes	Can point to a new DataFrame

Python Project Alert:- Dynamic list of variables Creation

June 29, 2024

Let us go through the Project requirement:-

1.Let us create One or Multiple dynamic lists of variables and save it in dictionary or Array or other datastructre for further repeating use in python. Variable names are in form of dynamic names for example Month_202401 to Month_202312 for 24 months( Take these 24 month backdated or as per current month’s Progression).

2.Later we will use this dictionary to create 24 csv files by filtering on year and month.

3.we will also use to create arrays based on this dynamic dictionary.

4.We will create custom excel files for 24 year months combination where column names are also in form of above 24 { year and months combination such as xyz_2404_04 to xyz_2404_12 , abc_xyz_2404_0405 to abc_xyz_2405_1201}.

Various Versions to Achieve it:-

import datetime
import pandas as pd
import os

# Step 1: Generate month names for the next 24 months
def generate_month_names():
    current_date = datetime.datetime.now()
    month_names = []

    for i in range(24):
        new_date = current_date + datetime.timedelta(days=30 * i)
        month_name = new_date.strftime("Month_%Y%m")
        month_names.append(month_name)

    return month_names

# Step 2: Create variables and store them in a dictionary
def create_variables(month_names):
    variables = {}
    for month in month_names:
        # Create some dummy data for demonstration
        data = pd.DataFrame({
            'Name': ['Alice', 'Bob', 'Charlie'],
            'Age': [25, 30, 35],
            'Month': [month] * 3
        })
        variables[month] = data

    return variables

# Step 3: Create CSV files from the dictionary
def create_csv_files(variables, output_directory):
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    for month, data in variables.items():
        file_name = f"{output_directory}/{month}.csv"
        data.to_csv(file_name, index=False)
        print(f"Saved {file_name}")

# Step 4: Create an Excel file with dynamic columns
def create_excel_file(variables, excel_file_name):
    with pd.ExcelWriter(excel_file_name) as writer:
        for month, data in variables.items():
            data.to_excel(writer, sheet_name=month, index=False)
        print(f"Saved {excel_file_name}")

# Main execution
if __name__ == "__main__":
    # Generate month names
    month_names = generate_month_names()

    # Create variables
    dynamic_vars = create_variables(month_names)

    # Directory to save CSV files
    output_directory = "output_csv_files"

    # Create CSV files
    create_csv_files(dynamic_vars, output_directory)

    # Excel file name
    excel_file_name = "dynamic_month_data.xlsx"

    # Create Excel file
    create_excel_file(dynamic_vars, excel_file_name)

import datetime
import pandas as pd
import os
from dateutil.relativedelta import relativedelta

# Step 1: Generate month names for the next 24 months
def generate_month_names():
    current_date = datetime.datetime.now()
    month_names = []

    for i in range(24):
        new_date = current_date + relativedelta(months=i)
        month_name = new_date.strftime("Month_%Y%m")
        month_names.append(month_name)

    return month_names

# Step 2: Create example data and store in a dictionary
def create_variables(month_names):
    variables = {}
    for month in month_names:
        # Create some example data for demonstration
        data = pd.DataFrame({
            'Name': ['Alice', 'Bob', 'Charlie'],
            'Age': [25, 30, 35],
            'Month': [month] * 3
        })
        variables[month] = data

    return variables

# Step 3: Create CSV files from the dictionary
def create_csv_files(variables, output_directory):
    if not os.path.exists(output_directory):
        os.makedirs(output_directory)

    for month, data in variables.items():
        file_name = f"{output_directory}/{month}.csv"
        data.to_csv(file_name, index=False)
        print(f"Saved {file_name}")

# Step 4: Create arrays based on the data stored in the dictionary
def create_arrays(variables):
    arrays = {}
    for month, data in variables.items():
        arrays[month] = data.values
    return arrays

# Step 5: Create custom Excel files for 24-month combinations
def create_custom_excel_files(variables, excel_file_name):
    with pd.ExcelWriter(excel_file_name) as writer:
        for i, (month, data) in enumerate(variables.items()):
            # Generate custom column names
            custom_columns = [f"xyz_{month}_{j+1:02d}" for j in range(len(data.columns))]
            custom_data = data.copy()
            custom_data.columns = custom_columns
            custom_data.to_excel(writer, sheet_name=month, index=False)

        # Example of creating more complex column names and data combination
        for i in range(1, 25):
            month_name = f"abc_xyz_{variables[list(variables.keys())[i-1]].iloc[0]['Month']}_{variables[list(variables.keys())[i%24]].iloc[0]['Month']}"
            complex_data = pd.concat([variables[list(variables.keys())[i-1]], variables[list(variables.keys())[i%24]]], axis=1)
            complex_data.columns = [f"{month_name}_{col}" for col in complex_data.columns]
            complex_data.to_excel(writer, sheet_name=month_name, index=False)

        print(f"Saved {excel_file_name}")

# Main execution
if __name__ == "__main__":
    # Generate month names
    month_names = generate_month_names()

    # Create example data and store in a dictionary
    dynamic_vars = create_variables(month_names)

    # Directory to save CSV files
    output_directory = "output_csv_files"

    # Create CSV files
    create_csv_files(dynamic_vars, output_directory)

    # Create arrays based on the data stored in the dictionary
    arrays = create_arrays(dynamic_vars)
    print("Created arrays based on the dictionary.")

    # Excel file name
    excel_file_name = "dynamic_month_data.xlsx"

    # Create custom Excel files
    create_custom_excel_files(dynamic_vars, excel_file_name)

from datetime import date, timedelta

def generate_month_names(months):
  """
  Generates a list of dynamic variable names representing month names
  based on the provided number of months in the past.

  Args:
      months (int): The number of months to consider (including the current month).

  Returns:
      list: A list of strings representing dynamic month variable names.
  """
  current_date = date.today()
  month_names = []
  for i in range(months):
    month = current_date - timedelta(days=i * 31)  # Adjust for approximate month length
    month_name = f"Month_{month.year}{month.month:02d}"
    month_names.append(month_name)
  return month_names

def create_data_structure(month_names):
  """
  Creates a dictionary where keys are dynamic month names and values are
  empty lists (to be populated with data later).

  Args:
      month_names (list): A list of dynamic month variable names.

  Returns:
      dict: A dictionary with dynamic month names as keys and empty lists as values.
  """
  data_structure = {}
  for month_name in month_names:
    data_structure[month_name] = []
  return data_structure

def create_csv_files(data_structure):
  """
  Creates CSV files for each month in the data structure with appropriate column names.

  Args:
      data_structure (dict): A dictionary with dynamic month names as keys and lists as values.
  """
  for month_name, data in data_structure.items():
    year, month = month_name.split("_")[1:]
    # Sample data for illustration. Replace with your actual data
    data.append(["col1", "col2", "col3"])
    filename = f"{month_name}.csv"
    with open(filename, "w", newline="") as csvfile:
      writer = csv.writer(csvfile)
      writer.writerow([f"xyz_{year}{month:02d}_{d:02d}" for d in range(4, 13)])  # Sample column names
      writer.writerows(data)

def create_excel_files(data_structure):
  """
  Creates custom Excel files (using a library like openpyxl) for each month,
  with dynamic column names based on year and month combinations.

  **Note:** This requires an external library like openpyxl for Excel manipulation.

  Args:
      data_structure (dict): A dictionary with dynamic month names as keys and lists as values.
  """
  # Replace this with your Excel file creation logic using a library like openpyxl
  pass

# Example Usage
months = 24
month_names = generate_month_names(months)
data_structure = create_data_structure(month_names)

# Populate data_structure with your actual data for each month

create_csv_files(data_structure)
create_excel_files(data_structure)  # Requires an external library

print("Data structures and files created successfully!")

Python Code Execution- Behind the Door- What happens?
June 29, 2024
I wrote a Python code or I created a Python script, and it executed successfully So what does it Mean? This will be the most basic question a Early Python Learner can ask ! So Consider this scenario- where i executed a script in python which saves a many csv in Local disk and also wrote hundread of rows of tabular data to a table in mysql server. Let us explain what all has happended – when i execute this script, especially computer’s internal hardware & software standpoint.
Here’s a breakdown of what happens when you execute a Python script that saves multiple CSVs and writes to a MySQL server:
Hardware:
1. CPU (Central Processing Unit): This is the brain of your computer and the main component that executes the Python script. It fetches instructions from the script line by line, decodes them, and performs the necessary operations.
2. Main Memory / RAM (Random Access Memory): This temporary storage holds the Python script itself while it’s being executed. It also stores variables and data used by the script during its operation. RAM is often referred to as a computer’s main memory, as opposed to the processor cache or other memory types. Random access memory is considered part of a computer’s primary memory. The Main Memory is used to store information that the CPU needs in a hurry. The main memory is nearly as fast as the CPU. But the information stored in the main memory vanishes when the computer is turned off.
3. Secondary Memory / Storage Drive (HDD or SSD): This is where the Python script resides before execution and where the generated CSV files are saved. It also stores the MySQL database files (if the server is local). The Secondary Memory is also used to store information, but it is much slower than the main memory. The advantage of the secondary memory is that it can store information even when there is no power to the computer. Examples of secondary memory are disk drives or flash memory (typically found in USB sticks and portable music players).
4. I/O – Network Card (Optional): The Input and Output Devices are simply our screen, keyboard, mouse, microphone, speaker, touchpad, etc. They are all of the ways we interact with the computer. These days, most computers also have a Network Connection to retrieve information over a network. We can think of the network as a very slow place to store and retrieve data that might not always be “up”. So in a sense, the network is a slower and at times unreliable form of Secondary Memory. If your MySQL server is on a different machine, the network card facilitates communication between your computer and the server for database interaction.
Software:
1. Operating System (OS): The OS (e.g., Windows, macOS, Linux) manages the overall system resources, including memory, storage, and CPU allocation. It also provides an environment for the Python interpreter to run.
2. Python Interpreter: This program translates Python code into instructions that the CPU can understand. It reads the script line by line, interprets its meaning, and executes the necessary actions.
3. Python Libraries: Your script might import specific Python libraries (e.g., csv, mysql.connector) for working with CSV files and interacting with the MySQL server. These libraries provide pre-written functions and modules that simplify tasks like reading/writing files and connecting to databases.
4. MySQL Server (Optional): If the MySQL server is local, it’s running in the background on your computer. If it’s remote, it’s running on a different machine on the network. The server manages the MySQL database, handling data storage, retrieval, and manipulation based on your script’s commands.
Execution Flow:
1. Script Initiation: You double-click the Python script file or run it from a terminal/command prompt.
2. OS Activation: The OS identifies the file as a Python script and locates the Python interpreter on your system.
3. Interpreter Load: The OS loads the Python interpreter into memory (RAM) and allocates resources for the script’s execution.
4. Script Execution: The interpreter starts reading the script line by line, translating the code into machine-readable instructions, and executing them using the CPU.
- CSV Processing: Functions from the csv library are used to open and write multiple CSV files on the storage drive based on the script’s logic.
- MySQL Interaction: Libraries like mysql.connector are used to establish a connection with the MySQL server (local or remote) using your provided credentials. The script then executes commands to insert data into the desired table.
1. Resource Management: The OS manages memory allocation and keeps track of open files (CSVs) and database connections.
2. Script Completion: Once the script finishes execution, the interpreter unloads from memory, and any open files or database connections are closed.
Additional Notes:
- Depending on your script’s complexity, it might interact with additional software components like error handling modules or logging libraries.
- Security measures like authentication and authorization might be involved when connecting to a remote MySQL server.
Detailed step-by-step account
When you execute a Python script that saves multiple CSV files to disk and writes a table to a MySQL server, a series of hardware and software processes occur. Here’s a detailed step-by-step account of what happens from the moment you execute the script:
Step 1: Script Execution Initiation
1. User Command: You run the Python script from the command line or an IDE.bashCopy codepython your_script.py
Step 2: Operating System (OS) Interaction
1. Process Creation: The operating system creates a new process for the Python interpreter. This involves allocating memory and other resources for the process.
2. Script Loading: The OS loads the Python interpreter executable into memory, which then reads and begins to execute your script.
Step 3: Python Interpreter
1. Interpreter Start: The Python interpreter starts and begins to parse and execute your script line by line.
2. Module Import: The interpreter loads any required modules and libraries (e.g., pandas, mysql.connector) from the standard library or installed packages. This involves reading the necessary files from disk and loading them into memory.
Step 4: File System Interaction
1. CSV File Writing:
  - Data Preparation: Your script prepares data to be written to CSV files, often involving data manipulation and formatting using libraries like pandas.
  - File Creation: The script creates or opens files on the disk. This involves OS-level file handling operations, including checking file permissions, creating file descriptors, and allocating disk space.
  - Data Writing: The data is written to these files. The OS manages buffering and ensures that data is correctly written to the storage medium.
  - File Closing: After writing the data, the script closes the files, ensuring that all buffers are flushed and file descriptors are released.
Step 5: Network Interaction (MySQL Server)
1. Database Connection:
  - Network Socket Creation: Your script uses a library (e.g., mysql.connector) to create a network socket and establish a connection to the MySQL server. This involves DNS resolution (if connecting by hostname), network routing, and potentially firewall traversal.
  - Authentication: The MySQL server authenticates the connection using the credentials provided in your script.
2. Data Transfer to MySQL:
  - Query Execution: Your script sends SQL commands over the established network connection to create tables and insert data. The MySQL server receives these commands, parses them, and executes them.
  - Data Storage: The MySQL server writes the data to its own storage system. This involves disk I/O operations, similar to the CSV file writing process but managed by the MySQL server software.
Step 6: Script Completion
1. Resource Cleanup: Once the script has completed its tasks, it closes any remaining open files and database connections. The OS and Python interpreter ensure that all resources are properly released.
2. Process Termination: The Python process terminates, and the OS cleans up any remaining resources associated with the process.
Detailed Internal Hardware and Software Processes
1. CPU Operations:
  - Instruction Fetching and Execution: The CPU fetches and executes instructions from the Python interpreter and your script.
  - Context Switching: The CPU may switch between different processes (including your script and other running processes), managing execution time via the OS scheduler.
2. Memory Management:
  - RAM Allocation: The OS allocates RAM for the Python process, script data, and any libraries being used. This includes memory for variables, data structures, and buffers.
  - Virtual Memory: The OS uses virtual memory to manage and optimize RAM usage, potentially swapping data to and from disk as needed.
3. Disk I/O:
  - File Reading/Writing: The OS manages read/write operations to the disk. For writing CSV files, this involves creating files, writing data, and ensuring data integrity.
  - Database Storage: When writing to the MySQL server, the server’s storage engine (e.g., InnoDB) handles data writing, indexing, and storage management.
4. Network I/O:
  - Data Packets: Data sent to and from the MySQL server is broken into packets, transmitted over the network, and reassembled at the destination.
  - Error Handling: Network errors (e.g., packet loss, latency) are managed through TCP/IP protocols, ensuring reliable data transfer.
So
When you execute your Python script:
- The OS creates a process for the Python interpreter.
- The interpreter reads and executes your script, loading necessary libraries.
- The script writes data to CSV files via the file system, involving disk I/O operations.
- The script connects to the MySQL server over the network, authenticates, and executes SQL commands to store data.
- The OS and interpreter manage memory, CPU, and I/O resources throughout this process.
- The process terminates, and the OS cleans up resources.
This sequence involves coordinated efforts between the CPU, memory, disk storage, network interfaces, and various layers of software (OS, Python interpreter, libraries, and MySQL server) to accomplish the tasks specified in your script. This is More with How A Py Script interacts with Computer. Let us Now deal in details with How a Python code executes:-
what happens when you run a Python code file (abc.py):
1. Invoking the Python Interpreter:
You run abc.py from the command line or an IDE by typing python abc.py.The operating system identifies Python as the interpreter for .py files and executes it accordingly.
- When you execute python abc.py from your terminal, the operating system (OS) locates the Python interpreter (python) and launches it as a new process.
- This process has its own memory space allocated by the OS.
2. Lexical Analysis (Scanning):
- The interpreter first performs lexical analysis, breaking down the Python code into tokens (keywords, identifiers, operators, etc.). It identifies these tokens based on the Python language syntax.
Lexical Analysis (Tokenization)
- Scanner (Lexer): The first stage in the compilation process is lexical analysis, where the lexer scans the source code and converts it into a stream of tokens. Tokens are the smallest units of meaning in the code, such as keywords, identifiers (variable names), operators, literals (constants), and punctuation (e.g., parentheses, commas).
- Example:x = 10 + 20
- This line would be tokenized into:
  - x: Identifier
  - =: Operator
  - 10: Integer Literal
  - +: Operator
  - 20: Integer Literal
3. Syntax Analysis (Parsing):
- Next comes syntax analysis, where the interpreter checks if the token sequence forms a valid Python program according to the grammar rules. Syntax errors (e.g., missing colons, mismatched parentheses) are detected at this stage, resulting in an error message.
Syntax Analysis (Parsing)
- Parser: The parser takes the stream of tokens produced by the lexer and arranges them into a syntax tree (or Abstract Syntax Tree, AST). The syntax tree represents the grammatical structure of the code according to Python’s syntax rules.
- Example AST for x = 10 + 20:
  - Assignment Node
    Left: Identifier x
    Right: Binary Operation Node
    Left: Integer Literal 10
    Operator: +
    Right: Integer Literal 20
4. Bytecode Compilation (Optional – Depending on Python Version):
- For Python versions prior to 3.0 (CPython): The interpreter compiles the parsed code into bytecode, an intermediate representation that’s more portable and often faster to execute than the original source code. This bytecode is typically stored in a .pyc file.
- For Python versions 3.0 and later (CPython): The interpreter might skip this step and directly generate the bytecode on the fly during execution using Just-In-Time (JIT) compilation. This can improve performance, especially for frequently executed code blocks.
5. Bytecode Execution:
Compilation: Python source code is compiled into bytecode (.pyc files) or directly into memory (if not saved to .pyc).
Execution: Bytecode is executed by the Python Virtual Machine (PVM), which is part of the interpreter. Each bytecode instruction is executed in sequence.
- The Python Virtual Machine (PVM), a software component within the interpreter, executes the generated bytecode. Each bytecode instruction corresponds to a specific operation (e.g., variable assignment, function call, loop iteration).
6. Memory Management:
Memory Management:
- Allocation: Memory is allocated dynamically as needed to store variables, constants, and data structures (like lists, dictionaries).
- Deallocation: Python’s memory manager handles garbage collection via reference counting and a cyclic garbage collector, reclaiming memory from objects no longer in use.
- The PVM manages memory used by the program’s data structures (variables, lists, dictionaries, etc.). Data types are stored in memory according to their size and layout requirements (e.g., integers occupy less space than strings).
- The OS also has memory management tools (like garbage collection in CPython) to reclaim unused memory when objects become unreachable.
7. Top-Level Code Execution:
Code outside of function and class definitions at the top level of abc.py is executed sequentially as part of the __main__ module.Functions and classes are defined but not executed until they are called.
- The PVM starts execution from the top-level code in the abc.py file, which includes:
  - Module-level statements (outside any function definitions) are executed in the order they appear in the file.
  - The __main__ block, if present, is executed only when the script is run directly (not imported as a module). This allows you to define code that specifically executes when the program is the main entry point.
8. Function Calls and Variable Scope:
Variables: Dynamically typed variables (e.g., x = 10 where x can be reassigned different types).
Constants: Python doesn’t have constants in the traditional sense; conventionally, constants are named in uppercase to indicate they should not be changed.
Data Types: Python supports various built-in data types such as integers, floats, strings, lists, tuples, dictionaries, etc. Each has its own memory management characteristics.
- When a function is called, a new activation record (stack frame) is created on the call stack. This frame stores local variables, function arguments, and the return address (where to return after function execution).
- Variable scope determines the visibility and lifetime of variables. Local variables are accessible only within the function’s scope (activation record) and are destroyed when the function exits.
9. Temporary Variables:
Temporary variables are typically local to functions or within the scope where they are defined.Python manages variable scope using namespaces (local, global, built-in) and manages memory accordingly.
- The interpreter and PVM might create temporary variables in memory during execution for intermediate calculations or storing partial results. These temporary variables are typically short-lived and get garbage collected when no longer needed.
10. Output and Termination:
- The program’s output (e.g., print statements) is displayed on the console or sent to the specified output stream.
- Once all top-level code and functions have finished execution, the PVM and interpreter clean up any remaining resources, and the process terminates. The memory allocated for the program is released by the OS.
- Exception Handling: If an exception occurs during execution (e.g., division by zero, file not found), the PVM throws an exception object, allowing you to handle errors gracefully using try...except blocks.
- Modules and Imports: When a module (xyz.py) is imported using import xyz, the interpreter executes that module’s code (similar to running xyz.py). Functions and variables defined in xyz.py become available for use in the importing script.
- Interactive Interpreter: Running Python interactively (python) provides a prompt where you can enter code line by line, which is immediately executed.
Temporary Functions in PL/Sql Vs Spark Sql
June 26, 2024
Temporary functions allow users to define functions that are session-specific and used to encapsulate reusable logic within a database session. While both PL/SQL and Spark SQL support the concept of user-defined functions, their implementation and usage differ significantly.
Temporary Functions in PL/SQL
PL/SQL, primarily used with Oracle databases, allows you to create temporary or anonymous functions within a block of code. These functions are often used to perform specific tasks within a session and are not stored in the database schema permanently.
Example: Temporary Function in PL/SQL
Here’s an example of how to create and use a temporary function within a PL/SQL block:
```
DECLARE
    -- Define a local function
    FUNCTION concatenate(first_name IN VARCHAR2, last_name IN VARCHAR2) RETURN VARCHAR2 IS
    BEGIN
        RETURN first_name || '-' || last_name;
    END concatenate;

BEGIN
    -- Use the local function
    DBMS_OUTPUT.PUT_LINE(concatenate('Alice', 'Smith'));
    DBMS_OUTPUT.PUT_LINE(concatenate('Bob', 'Johnson'));
END;
/
```
In this example:
- A temporary function concatenate is defined within a PL/SQL block.
- This function concatenates two strings with a hyphen.
- The function is used within the same block to print concatenated names.
Temporary Functions in Spark SQL
Spark SQL does not support defining temporary functions directly within SQL code as PL/SQL does. Instead, Spark SQL uses user-defined functions (UDFs) registered through the Spark API (e.g., in Python or Scala). These UDFs can be registered for the duration of a Spark session and used within SQL queries.
Example: Temporary Function in Spark SQL (Python)
Here’s how you define and use a UDF in Spark SQL:
```
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

# Initialize Spark session
spark = SparkSession.builder.appName("Temporary Functions").getOrCreate()

# Sample data
data = [
    ("Alice", "Smith"),
    ("Bob", "Johnson"),
    ("Charlie", "Williams")
]

columns = ["first_name", "last_name"]

# Create DataFrame
df = spark.createDataFrame(data, columns)

# Register the DataFrame as a temporary view
df.createOrReplaceTempView("people")

# Define the UDF
def concatenate(first, last):
    return f"{first}-{last}"

# Register the UDF as a temporary function
spark.udf.register("concatenate", concatenate, StringType())

# Use the temporary function in a Spark SQL query
result_df = spark.sql("""
SELECT first_name, last_name, concatenate(first_name, last_name) AS full_name
FROM people
""")

# Show the result
result_df.show()
```
In this example:
- A Spark session is initialized, and a sample DataFrame is created and registered as a temporary view.
- A UDF concatenate is defined in Python to concatenate two strings with a hyphen.
- The UDF is registered with the Spark session and can be used as a temporary function in SQL queries.
Comparison
Defining Temporary Functions
- PL/SQL:
  - Functions can be defined directly within a PL/SQL block.
  - Functions are local to the block and not stored permanently.
- Spark SQL:
  - Functions (UDFs) are defined using the Spark API (e.g., in Python, Scala).
  - UDFs must be registered with the Spark session to be used in SQL queries.
  - Functions are session-specific but not directly written in SQL.
Usage Scope
- PL/SQL:
  - Temporary functions are used within the scope of the PL/SQL block in which they are defined.
- Spark SQL:
  - UDFs are registered for the Spark session and can be used across multiple SQL queries within that session.
Language and Flexibility
- PL/SQL:
  - Functions are written in PL/SQL, which is closely integrated with Oracle databases.
  - Provides strong support for procedural logic.
- Spark SQL:
  - UDFs can be written in various languages supported by Spark (e.g., Python, Scala).
  - Provides flexibility to use different programming languages and integrate complex logic from external libraries.
While both PL/SQL and Spark SQL support the concept of temporary or session-specific functions, their implementation and usage differ. PL/SQL allows for the direct definition of temporary functions within blocks of code, providing a tightly integrated procedural approach. In contrast, Spark SQL relies on user-defined functions (UDFs) registered through the Spark API, offering flexibility and language diversity but requiring an additional step to register and use these functions in SQL queries.
How PySpark automatically optimizes the job execution by breaking it down into stages and tasks based on data dependencies. can explain with an example
June 25, 2024
Apache Spark, including PySpark, automatically optimizes job execution by breaking it down into stages and tasks based on data dependencies. This process is facilitated by Spark’s Directed Acyclic Graph (DAG) Scheduler, which helps in optimizing the execution plan for efficiency. Let’s break this down with a detailed example and accompanying numbers to illustrate the process.
In Apache Spark, including PySpark, the Directed Acyclic Graph (DAG) is a fundamental concept that represents the sequence of computations that need to be performed on data. Understanding how Spark breaks down a job into stages and tasks within this DAG is crucial for grasping how Spark optimizes and executes jobs.
Understanding DAG in PySpark
A DAG in Spark is a logical representation of the operations to be executed. It is built when an action (like collect, save, count, etc.) is called on an RDD, DataFrame, or Dataset. The DAG contains all the transformations (like map, filter, groupBy, etc.) that Spark must perform on the data.
Breakdown of a Job into Stages and Tasks
1. Logical Plan:
  - When you define transformations, Spark constructs a logical plan. This plan captures the sequence of transformations on the data.
2. Physical Plan:
  - Spark’s Catalyst optimizer converts the logical plan into a physical plan, which specifies how the operations will be executed.
3. DAG of Stages:
  - The physical plan is divided into stages. Each stage consists of tasks that can be executed in parallel. Stages are determined by shuffle boundaries (i.e., points where data needs to be redistributed across the cluster).
4. Tasks within Stages:
  - Each stage is further divided into tasks. A task is the smallest unit of work and is executed on a single partition of the data. Tasks within a stage are executed in parallel across the cluster.
Example: Complex ETL Pipeline
Scenario: Process a large dataset of user activity logs to compute user session statistics, filter erroneous data, and generate aggregated reports.
Steps in the Pipeline:
1. Data Ingestion:
  - Read raw logs from HDFS.
2. Data Cleaning:
  - Filter out records with missing or malformed fields.
3. Sessionization:
  - Group records by user and time interval to form sessions.
4. Aggregation:
  - Compute session statistics such as total time spent and number of pages visited.
PySpark Code Example:
```
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, window, sum as _sum

# Initialize Spark session
spark = SparkSession.builder.appName("Complex ETL Pipeline").getOrCreate()

# Data Ingestion
df = spark.read.csv("hdfs:///path/to/input/*.csv", header=True, inferSchema=True)

# Data Cleaning
df_cleaned = df.filter(col("user_id").isNotNull() & col("timestamp").isNotNull())

# Sessionization
df_sessions = df_cleaned.groupBy("user_id", window("timestamp", "30 minutes")).agg({"page": "count"})

# Aggregation
df_aggregated = df_sessions.groupBy("user_id").agg(
    _sum("count(page)").alias("total_pages"),
    _sum("session_duration").alias("total_duration")
)

# Write results to HDFS
df_aggregated.write.parquet("hdfs:///path/to/output/")
```
How PySpark Optimizes Job Execution:
1. Logical Plan:
  - When transformations are applied (e.g., filter, groupBy), Spark builds a logical plan. This plan is an abstract representation of the operations to be performed.
2. Physical Plan:
  - Spark’s Catalyst optimizer converts the logical plan into a physical plan. This plan is optimized for execution by selecting the most efficient strategies for operations.
3. DAG Scheduler:
  - The physical plan is translated into a DAG of stages and tasks. Each stage consists of tasks that can be executed in parallel.
Breaking Down into Stages and Tasks:
- Stage 1:
  - Tasks: Reading data from HDFS and filtering (Data Cleaning).
  - Operations: df.filter(...)
  - Number of Tasks: Equal to the number of HDFS blocks (e.g., 100 blocks -> 100 tasks).
  - Memory Usage: Depends on the size of the data being read and filtered.
- Stage 2:
  - Tasks: Grouping data by user and session window (Sessionization).
  - Operations: df_sessions = df_cleaned.groupBy("user_id", window(...))
  - Number of Tasks: Determined by the number of unique keys (users and time intervals) and partitioning strategy (e.g., 200 partitions).
  - Memory Usage: Depends on the intermediate shuffle data size.
- Stage 3:
  - Tasks: Aggregating session data (Aggregation).
  - Operations: df_aggregated = df_sessions.groupBy("user_id").agg(...)
  - Number of Tasks: Again determined by the number of unique keys (users) and partitioning strategy (e.g., 50 partitions).
  - Memory Usage: Depends on the size of the aggregated data.
- Stage 4:
  - Tasks: Writing results to HDFS.
  - Operations: df_aggregated.write.parquet(...)
  - Number of Tasks: Typically determined by the number of output partitions (e.g., 50 tasks).
  - Memory Usage: Depends on the size of the data being written.
Example with Numbers:
Let’s assume we have a dataset of 1 TB, distributed across 100 HDFS blocks.
1. Stage 1 (Data Cleaning):
  - Tasks: 100 (one per HDFS block)
  - Data Read: 1 TB (distributed across tasks)
  - Intermediate Data: 900 GB (after filtering out 10% erroneous data)
2. Stage 2 (Sessionization):
  - Tasks: 200 (partitioned by user and time interval)
  - Intermediate Data: 900 GB
  - Shuffled Data: 900 GB (data shuffled across tasks for grouping)
3. Stage 3 (Aggregation):
  - Tasks: 50 (grouped by user)
  - Intermediate Data: 50 GB (assuming significant aggregation)
  - Shuffled Data: 50 GB
4. Stage 4 (Write to HDFS):
  - Tasks: 50 (one per output partition)
  - Output Data: 50 GB (final aggregated data)
Memory Management:
- Executor Memory: Configured based on the size of the data and the number of tasks. E.g., each executor allocated 8 GB memory.
- Caching and Persistence: Intermediate results (e.g., df_cleaned and df_sessions) can be cached in memory to avoid recomputation.
- Shuffle Memory: Managed dynamically to balance between storage and execution needs.
Execution Log Example:
```
plaintextCopy codeINFO DAGScheduler: Final stage: ResultStage 4 (parquet at ETL_Pipeline.py:24)
INFO DAGScheduler: Parents of final stage: List(ShuffleMapStage 3, ShuffleMapStage 2, ShuffleMapStage 1)
INFO DAGScheduler: Missing parents: List(ShuffleMapStage 3, ShuffleMapStage 2, ShuffleMapStage 1)
INFO DAGScheduler: Submitting ShuffleMapStage 1 (MapPartitionsRDD[4] at filter at ETL_Pipeline.py:10), which has no missing parents
INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 4.1 KB, free 2004.6 MB)
INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on executor_1 (size: 4.1 KB, free: 2004.6 MB)
INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on executor_2 (size: 4.1 KB, free: 2004.6 MB)
INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on executor_3 (size: 4.1 KB, free: 2004.6 MB)
INFO DAGScheduler: Submitting 100 missing tasks from ShuffleMapStage 1 (MapPartitionsRDD[4] at filter at ETL_Pipeline.py:10) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 0, executor 1, partition 0, PROCESS_LOCAL, 7987 bytes)
INFO TaskSetManager: Starting task 1.0 in stage 1.0 (TID 1, executor 2, partition 1, PROCESS_LOCAL, 7987 bytes)
INFO TaskSetManager: Starting task 2.0 in stage 1.0 (TID 2, executor 3, partition 2, PROCESS_LOCAL, 7987 bytes)
...
INFO BlockManagerInfo: Added rdd_4_0 in memory on executor_1 (size: 45.6 MB, free: 1958.6 MB)
INFO BlockManagerInfo: Added rdd_4_1 in memory on executor_2 (size: 45.6 MB, free: 1958.6 MB)
INFO BlockManagerInfo: Added rdd_4_2 in memory on executor_3 (size: 45.6 MB, free: 1958.6 MB)
...
INFO DAGScheduler: Submitting 200 missing tasks from ShuffleMapStage 2 (MapPartitionsRDD[7] at groupBy at ETL_Pipeline.py:14) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
INFO TaskSetManager: Starting task 0.0 in stage 2.0 (TID 100, executor 1, partition 0, PROCESS_LOCAL, 7987 bytes)
...
INFO BlockManagerInfo: Added rdd_7_0 in memory on executor_1 (size: 10.2 MB, free: 1948.4 MB)
INFO BlockManagerInfo: Added rdd_7_1 in memory on executor_2 (size: 10.2 MB, free: 1948.4 MB)
INFO BlockManagerInfo: Added rdd_7_2 in memory on executor_3 (size: 10.2 MB, free: 1948.4 MB)
...
INFO DAGScheduler: Submitting 50 missing tasks from ResultStage 4 (ShuffledRDD[10] at write at ETL_Pipeline.py:24) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14))
INFO TaskSetManager: Starting task 0.0 in stage 4.0 (TID 300, executor 1, partition 0, PROCESS_LOCAL, 7987 bytes)
...
INFO BlockManagerInfo: Removed broadcast_0_piece0 on executor_1 in memory (size: 4.1 KB, free: 1958.6 MB)
INFO BlockManagerInfo: Removed broadcast_0_piece0 on executor_2 in memory (size: 4.1 KB, free: 1958.6 MB)
INFO BlockManagerInfo: Removed broadcast_0_piece0 on executor_3 in memory (size: 4.1 KB, free: 1958.6 MB)
```
PySpark optimizes job execution by breaking it down into stages and tasks based on data dependencies. The DAG Scheduler orchestrates the process, ensuring efficient memory management and parallel execution. By caching intermediate results and leveraging in-memory computation, PySpark minimizes disk I/O and improves overall performance. This approach contrasts with the more disk-intensive Hadoop MapReduce, highlighting PySpark’s advantages in handling complex data pipelines.

Understanding Pyspark execution with the help of Logs in Detail

June 23, 2024

explain a typical Pyspark execution Logs

A typical PySpark execution log provides detailed information about the various stages and tasks of a Spark job. These logs are essential for debugging and optimizing Spark applications. Here’s a step-by-step explanation of what you might see in a typical PySpark execution log:

Step 1: Spark Context Initialization

When you start a PySpark application, the first entries in the log are related to initializing the SparkContext and SparkSession.

INFO SparkContext: Running Spark version 3.1.2
INFO SparkContext: Submitted application: Example App
INFO ResourceUtils: ==============================================================
INFO ResourceUtils: No custom resources configured for spark.driver.
INFO SparkContext: Added JAR file:/path/to/pyspark-shell

Step 2: Application and Job Start

The log will show details about the Spark application and the start of a job.

INFO SparkContext: Starting job: show at ExampleApp.py:25
INFO DAGScheduler: Got job 0 (show at ExampleApp.py:25) with 1 output partitions
INFO DAGScheduler: Final stage: ResultStage 0 (show at ExampleApp.py:25)
INFO DAGScheduler: Parents of final stage: List()
INFO DAGScheduler: Missing parents: List()

Step 3: Stage and Task Scheduling

Next, the log will include details about the stages and tasks that Spark schedules for execution.

INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[3] at show at ExampleApp.py:25), which has no missing parents
INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 4.5 KB, free 365.2 MB)
INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 2.1 KB, free 365.2 MB)
INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.1.1:49919 (size: 2.1 KB, free: 366.3 MB)
INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1039
INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 0 (MapPartitionsRDD[3] at show at ExampleApp.py:25) (first 15 tasks are for partitions Vector(0))
INFO TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, 192.168.1.1, executor driver, partition 0, PROCESS_LOCAL, 4735 bytes)
INFO Executor: Running task 0.0 in stage 0.0 (TID 0)

Step 4: Task Execution

Each task’s execution will be logged, including any shuffling and data read/write operations.

INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.1.1:49919 (size: 2.1 KB, free: 366.3 MB)
INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 1380 bytes result sent to driver
INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 213 ms on 192.168.1.1 (executor driver) (1/1)
INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
INFO DAGScheduler: ResultStage 0 (show at ExampleApp.py:25) finished in 0.254 s
INFO DAGScheduler: Job 0 is finished. 1/1 tasks completed.

Step 5: Stage and Job Completion

Logs will indicate the completion of stages and jobs, including the time taken for execution.

INFO DAGScheduler: Job 0 finished: show at ExampleApp.py:25, took 0.270467 s
INFO SparkContext: Starting job: count at ExampleApp.py:27
INFO DAGScheduler: Registering RDD 4 (count at ExampleApp.py:27)
INFO DAGScheduler: Got job 1 (count at ExampleApp.py:27) with 1 output partitions
INFO DAGScheduler: Final stage: ResultStage 1 (count at ExampleApp.py:27)
INFO DAGScheduler: Parents of final stage: List()
INFO DAGScheduler: Missing parents: List()
INFO DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD[4] at count at ExampleApp.py:27), which has no missing parents
INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 5.4 KB, free 365.2 MB)
INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 2.7 KB, free 365.2 MB)
INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.1.1:49919 (size: 2.7 KB, free: 366.3 MB)
INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1039
INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 1 (MapPartitionsRDD[4] at count at ExampleApp.py:27) (first 15 tasks are for partitions Vector(0))
INFO TaskSchedulerImpl: Adding task set 1.0 with 1 tasks
INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 1, 192.168.1.1, executor driver, partition 0, PROCESS_LOCAL, 4735 bytes)
INFO Executor: Running task 0.0 in stage 1.0 (TID 1)
INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.1.1:49919 (size: 2.7 KB, free: 366.3 MB)
INFO Executor: Finished task 0.0 in stage 1.0 (TID 1). 1380 bytes result sent to driver
INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 1) in 221 ms on 192.168.1.1 (executor driver) (1/1)
INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool
INFO DAGScheduler: ResultStage 1 (count at ExampleApp.py:27) finished in 0.273 s
INFO DAGScheduler: Job 1 finished: count at ExampleApp.py:27, took 0.277596 s

Step 6: Resource Cleanup

After job completion, Spark will clean up the resources and shut down the session.

INFO SparkUI: Stopped Spark web UI at http://192.168.1.1:4040
INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
INFO MemoryStore: MemoryStore cleared
INFO BlockManager: BlockManager stopped
INFO BlockManagerMaster: BlockManagerMaster stopped
INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
INFO SparkContext: Successfully stopped SparkContext
INFO ShutdownHookManager: Shutdown hook called
INFO ShutdownHookManager: Deleting directory /tmp/spark-12a26f8f-d833-439a-ae3a-6c9ed6c29c67

Summary of Key Log Entries

Initialization:
- Information about Spark version, application submission, and resource configuration.
Job Start:
- Details of job submission, including the number of output partitions and stages.
Stage and Task Scheduling:
- Logs about stage submission, task scheduling, and resource allocation.
Task Execution:
- Logs on task start, execution, data shuffling, and completion.
Stage and Job Completion:
- Logs indicating the completion of stages and jobs with execution time.
Resource Cleanup:
- Information on stopping the Spark context, cleaning up resources, and shutting down Spark.

Example PySpark Log Analysis

Here’s a breakdown of an example PySpark log entry:

INFO DAGScheduler: Got job 0 (show at ExampleApp.py:25) with 1 output partitions

INFO: Log level.
DAGScheduler: Component generating the log.
Got job 0 (show at ExampleApp.py:25): Job ID and action.
with 1 output partitions: Number of output partitions for the job.

By understanding these log entries, you can monitor the execution of your PySpark jobs, identify performance bottlenecks, and debug errors efficiently.

let’s delve deeper into the key components and execution process of a PySpark job, explaining each step with a real-life example and corresponding log entries.

Real-Life Example: Word Count Application

We’ll use a word count application as our example. This application reads a text file, counts the occurrences of each word, and outputs the result.

from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession

# Initialize SparkConf and SparkContext
conf = SparkConf().setAppName("Word Count").setMaster("local[*]")
sc = SparkContext(conf=conf)

# Create SparkSession
spark = SparkSession(sc)

# Read text file into RDD
text_file = sc.textFile("path/to/textfile.txt")

# Split lines into words, map each word to a (word, 1) pair, and reduce by key to count occurrences
words = text_file.flatMap(lambda line: line.split(" "))
word_pairs = words.map(lambda word: (word, 1))
word_counts = word_pairs.reduceByKey(lambda a, b: a + b)

# Collect results
results = word_counts.collect()

# Print results
for word, count in results:
    print(f"{word}: {count}")

# Stop the SparkContext
sc.stop()

Explanation with Log Entries

1. Initialization:

Code:

conf = SparkConf().setAppName("Word Count").setMaster("local[*]")
sc = SparkContext(conf=conf)
spark = SparkSession(sc)

Log Entries:

INFO SparkContext: Running Spark version 3.1.2
INFO SparkContext: Submitted application: Word Count
INFO ResourceUtils: ==============================================================
INFO ResourceUtils: No custom resources configured for spark.driver.
INFO SparkContext: Added JAR file:/path/to/pyspark-shell
INFO Utils: Successfully started service 'SparkUI' on port 4040.
INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://192.168.1.1:4040

Explanation: Initializes the SparkConf and SparkContext. A SparkSession is created using the existing SparkContext. The Spark UI is started on port 4040.

2. Resource Allocation:

Spark allocates resources for the application, such as memory and CPUs specified in the configuration (local[*] means using all available cores).

Log Entries:

INFO SparkContext: Added JAR file:/path/to/pyspark-shell
INFO MemoryStore: MemoryStore started with capacity 366.3 MB
INFO DiskBlockManager: Created local directory at /tmp/blockmgr-123456
INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 192.168.1.1, 49919)
INFO BlockManagerMasterEndpoint: Registering block manager 192.168.1.1:49919 with 366.3 MB RAM, BlockManagerId(driver, 192.168.1.1, 49919)
INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 192.168.1.1, 49919)

Explanation: Registers block managers and allocates memory and disk space for storing RDD partitions.

3. Tasks Creation by Driver for Jobs:

Code:

text_file = sc.textFile("path/to/textfile.txt")

Log Entries:

INFO SparkContext: Starting job: textFile at WordCount.py:12
INFO DAGScheduler: Got job 0 (textFile at WordCount.py:12) with 1 output partitions
INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[1] at textFile at WordCount.py:12), which has no missing parents
INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 4.5 KB, free 365.2 MB)
INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1039

Explanation: The driver converts the RDD operation (textFile) into a job and creates a stage with tasks. The file is split into partitions.

4. DAG Scheduler Working:

The DAG (Directed Acyclic Graph) scheduler divides the job into stages based on shuffle boundaries.

Log Entries:

INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[1] at textFile at WordCount.py:12), which has no missing parents
INFO DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD[2] at flatMap at WordCount.py:14), which has no missing parents
INFO DAGScheduler: Submitting ResultStage 2 (MapPartitionsRDD[3] at map at WordCount.py:15), which has no missing parents
INFO DAGScheduler: Submitting ResultStage 3 (ShuffledRDD[4] at reduceByKey at WordCount.py:16), which has no missing parents

Explanation: Submits stages in the DAG to be executed. Here, stages correspond to reading the text file, performing flatMap, map, and reduceByKey operations.

5. Broadcast:

Broadcast variables are used to efficiently distribute large read-only data to all worker nodes.

Log Entries:

plaintextCopy codeINFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 4.5 KB, free 365.2 MB)
INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.1.1:49919 (size: 2.1 KB, free: 366.3 MB)
INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1039

Explanation: Broadcasts the variable to all executors. In this example, broadcasting might occur if there are configuration settings or large data structures needed by all tasks.

6. Stages Creations:

Stages are created for different transformations. Each stage contains tasks that are executed in parallel.

Log Entries:

INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[1] at textFile at WordCount.py:12)
INFO DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD[2] at flatMap at WordCount.py:14)
INFO DAGScheduler: Submitting ResultStage 2 (MapPartitionsRDD[3] at map at WordCount.py:15)
INFO DAGScheduler: Submitting ResultStage 3 (ShuffledRDD[4] at reduceByKey at WordCount.py:16)

Explanation: Each transformation (flatMap, map, reduceByKey) results in a new stage.

7. Task Execution:

Tasks within each stage are sent to executors for parallel execution.

Log Entries:

INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, 192.168.1.1, executor driver, partition 0, PROCESS_LOCAL, 4735 bytes)
INFO Executor: Running task 0.0 in stage 0.0 (TID 0)
INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.1.1:49919 (size: 2.1 KB, free: 366.3 MB)
INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 1380 bytes result sent to driver
INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 213 ms on 192.168.1.1 (executor driver) (1/1)
INFO DAGScheduler: ResultStage 0 (MapPartitionsRDD[1] at textFile at WordCount.py:12) finished in 0.254 s
INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool

Explanation: Each task processes its partition of data. The executor runs the task and returns the result to the driver.

8. Next Job Initiation:

Once the current job is complete, the next job starts if there are further actions.

Code:

results = word_counts.collect()

Log Entries:

INFO SparkContext: Starting job: collect at WordCount.py:18
INFO DAGScheduler: Got job 1 (collect at WordCount.py:18) with 2 output partitions
INFO DAGScheduler: Final stage: ResultStage 4 (collect at WordCount.py:18)
INFO DAGScheduler: Parents of final stage: List(ShuffleMapStage 3)
INFO DAGScheduler: Missing parents: List(ShuffleMapStage 3)
INFO DAGScheduler: Submitting ShuffleMapStage 3 (MapPartitionsRDD[3] at map at WordCount.py:15) with 2 output partitions
INFO DAGScheduler: Submitting ResultStage 4 (ShuffledRDD[4] at reduceByKey at WordCount.py:16) with 1 output partitions

Explanation: The next job is initiated, here the collect action, and Spark plans the stages and tasks accordingly.

Pyspark RDDs a Wonder -Transformations, actions and execution operations- please explain and list them

June 16, 2024

RDD (Resilient Distributed Dataset) is the fundamental data structure in Apache Spark. It is an immutable, distributed collection of objects that can be processed in parallel across a cluster of machines.

Purpose of RDD

Distributed Data Handling:
- RDDs are designed to handle large datasets by distributing the data across multiple nodes in a cluster. This enables parallel processing and efficient data management.
Fault Tolerance:
- RDDs provide fault tolerance by maintaining lineage information, which is a record of the sequence of operations that created the dataset. If any part of the data is lost due to a node failure, Spark can use this lineage to recompute the lost partitions from the original data.
In-Memory Computation:
- RDDs allow data to be stored in memory, making them ideal for iterative algorithms that require multiple passes over the data. This in-memory storage significantly speeds up processing by reducing the need for disk I/O.
Immutable Operations:
- RDDs are immutable, meaning that once they are created, they cannot be altered. Any transformation on an RDD results in the creation of a new RDD. This immutability simplifies parallel processing by avoiding issues related to concurrent data modifications.

How RDD is Beneficial

Parallel Processing:
- RDDs are divided into partitions, with each partition being processed independently in parallel on different nodes of the cluster. This parallelism allows for faster data processing, especially for large datasets.
Fault Tolerance:
- The lineage graph of RDDs ensures that Spark can recover from node failures without needing to re-execute the entire job. This automatic recovery mechanism makes RDDs reliable for processing big data in distributed environments.
Lazy Evaluation:
- RDD transformations are lazily evaluated, meaning they are not executed immediately. Instead, Spark builds a Directed Acyclic Graph (DAG) of transformations. The actual computation happens only when an action (e.g., count, collect, saveAsTextFile) is called. This lazy evaluation allows Spark to optimize the execution plan and avoid unnecessary computations.
In-Memory Storage:
- By storing intermediate results in memory, RDDs allow iterative and interactive computations to be much faster compared to traditional disk-based processing systems.
Ease of Use:
- RDDs provide a high-level API with operations like map, filter, reduce, and join, making it easy for developers to express complex data processing tasks. This API abstracts away the complexity of distributed computing, allowing users to focus on their application logic.
Support for Diverse Data Sources:
- RDDs can be created from various data sources such as local file systems, HDFS, Amazon S3, and NoSQL databases, providing flexibility in handling different types of data.

RDDs are the backbone of Apache Spark’s distributed computing capabilities. They enable scalable, fault-tolerant, and efficient processing of large datasets across a cluster. Their benefits include parallelism, fault tolerance, lazy evaluation, and in-memory computation, all of which contribute to making Spark a powerful tool for big data processing.

Let’s break down the process of how an RDD in Apache Spark is transformed and executed, going through the DAG (Directed Acyclic Graph), DAG Scheduler, Task Scheduler, YARN, and finally to the Executors.

Also we can add the concept of job execution in Apache Spark. A job in Spark is the highest-level unit of work that gets submitted to the Spark engine. Here’s how the process unfolds from RDD creation to job execution:

1. RDD (Resilient Distributed Dataset)

RDD Creation: You start by creating RDDs, which can be done by loading data from sources like HDFS, S3, or local files. RDDs can undergo a series of transformations such as map, filter, and reduce. These transformations define the data flow but don’t trigger any computation immediately.

2. Job Submission

Triggering a Job: A job is triggered when an action (e.g., collect, count, saveAsTextFile) is called on an RDD or DataFrame. Actions force Spark to evaluate the transformations you’ve defined.
Job Definition: When an action is called, Spark creates a job corresponding to the action. This job is a high-level operation that involves multiple stages.

3. DAG (Directed Acyclic Graph)

DAG Construction: Once the job is defined, Spark constructs a DAG of stages. The DAG represents the logical execution plan, showing how transformations depend on each other and how they can be broken down into stages.
Stages: Each stage in the DAG represents a group of tasks that can be executed together without requiring data to be shuffled. Stages are determined by shuffle operations, such as reduceByKey or groupBy.

4. DAG Scheduler

Stage Scheduling: The DAG Scheduler breaks down the job into stages and schedules them for execution. Stages are executed sequentially if they depend on each other or in parallel if they are independent.
Task Creation: Within each stage, the DAG Scheduler creates tasks, one for each partition of the data. The number of tasks equals the number of partitions in the RDD for that stage.

5. Task Scheduler

Task Dispatching: The Task Scheduler is responsible for assigning tasks to available executors. It considers factors like data locality (to minimize data transfer) and resource availability when dispatching tasks.
Task Execution: The Task Scheduler sends tasks to executors, which are processes running on worker nodes. Each task is a unit of execution corresponding to a partition of the data.

6. YARN (Yet Another Resource Negotiator)

Resource Allocation: If Spark is running on YARN, it requests resources (CPU, memory) from YARN’s Resource Manager. YARN allocates containers on the cluster nodes, which are used to run executors.
NodeManager: YARN’s NodeManagers manage the containers on each node, monitoring resource usage, health, and handling failures.

7. Executors

Task Execution: Executors are the JVM processes running on worker nodes that execute the tasks. Each executor runs tasks in parallel (one task per CPU core) and uses the allocated memory to store data and intermediate results.
In-Memory Computation: Executors perform computations on the data, typically in-memory, which allows Spark to efficiently handle iterative algorithms and interactive queries.
Fault Tolerance: If a task fails (due to hardware issues, for example), the DAG Scheduler can reschedule the task on another executor using the lineage information of the RDD to recompute the data.

8. Job Execution Flow

Stage Execution: The job execution starts with the first stage, which runs its tasks on the available executors. Once a stage completes, the results are either passed to the next stage or returned to the driver.
Data Shuffling: If a stage boundary involves a shuffle (repartitioning of data across the cluster), Spark redistributes the data across executors, which can introduce network overhead.
Final Stage and Action: The final stage of a job usually involves writing the results to storage (e.g., HDFS, S3) or returning them to the driver (in the case of actions like collect).

9. Completion and Result Handling

Job Completion: Once all stages of a job are successfully completed, the job is marked as complete. The results of the action that triggered the job are either returned to the user (in the case of actions like collect) or saved to storage.
Driver Program: The Spark driver program monitors the execution of the job, collecting the results and handling any errors or retries if tasks or stages fail.

How These Components Work Together:

Job Submission: When an action is triggered, Spark submits a job.
DAG Construction: Spark constructs a DAG of stages based on the job’s transformations.
DAG Scheduling: The DAG Scheduler breaks down the DAG into stages and creates tasks.
Task Scheduling: The Task Scheduler sends tasks to executors based on data locality and resource availability.
Resource Allocation (YARN): If using YARN, Spark requests resources and YARN allocates containers.
Task Execution: Executors on worker nodes execute the tasks, processing data and returning results.
Job Completion: The driver receives the final results, and the job is marked as complete.

Summary

A job is a top-level Spark operation triggered by an action. Spark breaks the job down into stages and tasks using a DAG.
The DAG Scheduler schedules these stages, while the Task Scheduler dispatches tasks to executors.
YARN handles resource allocation, and executors perform the actual computations.
Spark ensures fault tolerance through task retries and uses in-memory computation for speed.
The job completes when all stages are successfully executed and results are returned or saved.

RDDs Limitation and Why Dataframes!!

While RDDs (Resilient Distributed Datasets) are powerful and offer several advantages in Apache Spark, they do have limitations that led to the development and widespread use of DataFrames and Datasets. Below are some of the key limitations of RDDs and why DataFrames are often preferred:

Limitations of RDDs

Lack of Optimization:
- No Query Optimization: RDDs do not have built-in optimizations like query planning or execution optimization. Operations on RDDs are not optimized, which can lead to inefficient execution plans and longer processing times.
- Manual Optimization Required: Developers need to manually optimize RDD operations by tuning various parameters, which can be complex and error-prone.
Verbose Code:
- Low-Level API: RDDs provide a low-level API that requires developers to write detailed code for even simple operations. This can make the code verbose and harder to maintain.
- Complex Data Processing: When dealing with complex data processing tasks, RDDs require more lines of code compared to higher-level abstractions like DataFrames.
Performance Overhead:
- Serialization and Deserialization: RDDs incur overhead due to the need to serialize and deserialize data between nodes in the cluster, especially when using custom objects or non-primitive data types.
- Memory Usage: RDDs tend to use more memory because they do not provide automatic optimization for memory usage and can lead to inefficient memory management.
No Built-in Schema:
- Lack of Schema: RDDs do not have a built-in schema, meaning that Spark does not have knowledge about the structure of the data. This makes it difficult to perform certain types of optimizations or enforce data integrity.
- Difficult Data Manipulation: Without a schema, operations like filtering, joining, and aggregating data become more cumbersome and error-prone, requiring developers to manually handle data types.
Limited Interoperability with SQL:
- No SQL Interface: RDDs do not natively support SQL queries. Integrating SQL processing with RDDs requires converting them to DataFrames or writing custom code, which can be inefficient and complicated.
No Support for Catalyst Optimizer:
- No Optimization Framework: RDDs do not benefit from the Catalyst Optimizer, which is used in Spark SQL to optimize DataFrame operations. This means that RDD operations are generally slower and less efficient compared to operations on DataFrames.

Why DataFrames are Preferred

Catalyst Optimizer:
- Optimized Execution Plans: DataFrames benefit from the Catalyst Optimizer, which automatically generates optimized execution plans. This results in faster and more efficient query execution.
Ease of Use:
- High-Level API: DataFrames provide a high-level API with built-in operations for data manipulation, which makes the code more concise and easier to read.
- SQL Queries: DataFrames support SQL queries, allowing users to leverage their SQL knowledge for data processing tasks, which is particularly useful for complex analytics.
Schema and Type Safety:
- Structured Data: DataFrames come with a schema that defines the structure of the data, enabling more efficient data processing and easier data manipulation.
- Type Safety: In languages like Scala, Datasets (a type-safe version of DataFrames) provide compile-time type checking, reducing runtime errors and improving code safety.
Interoperability:
- Seamless SQL Integration: DataFrames can be seamlessly integrated with SQL, allowing for complex queries and operations to be expressed in SQL syntax.
- Data Source Integration: DataFrames offer better integration with various data sources, including structured data formats like Parquet, ORC, and Avro.
Performance:
- Memory Optimization: DataFrames are optimized for memory usage, often leading to more efficient memory management compared to RDDs.
- Automatic Caching: DataFrames can be automatically cached in memory, improving performance for iterative queries and operations.

Pyspark core programming

PySpark Core programming is the foundation of Apache Spark when using Python. It provides APIs for distributed data processing, parallel computing, and in-memory computation. PySpark Core mainly consists of the RDD (Resilient Distributed Dataset) API, which enables fault-tolerant and distributed data processing.

Key Concepts of PySpark Core Programming

1. Resilient Distributed Dataset (RDD)

RDD is the fundamental data structure in PySpark. It is an immutable, distributed collection of objects that can be processed in parallel.

Features of RDD:

Immutable: Once created, cannot be changed.

Distributed: Stored across multiple nodes.

Lazy Evaluation: Operations are executed only when an action is triggered.

Fault Tolerance: Can recover lost partitions using lineage.

RDD Creation Methods:

from pyspark.sql import SparkSession 
spark = SparkSession.builder.appName("RDDExample").getOrCreate() 
sc = spark.sparkContext # SparkContext is needed for RDD operations 
# Creating RDD from a list rdd1 = sc.parallelize([1, 2, 3, 4, 5]) 
# Creating RDD from a file rdd2 = sc.textFile("sample.txt")

2. RDD Transformations

Transformations create a new RDD from an existing one. They are lazy, meaning they are executed only when an action is performed.

Common Transformations:
- map(): Applies a function to each element.
- filter(): Filters elements based on a condition.
- flatMap(): Similar to map() but flattens the results.
- reduceByKey(): Groups data by key and applies an aggregation function.
- distinct(): Removes duplicates.
Example:
rdd = sc.parallelize([1, 2, 3, 4, 5])
# Applying map transformation squared_rdd = rdd.map(lambda x: x * x)
print(squared_rdd.collect()) # Output: [1, 4, 9, 16, 25]

3. RDD Actions

Actions trigger the execution of transformations and return results.

Common Actions:

Example: numbers = sc.parallelize([1, 2, 3, 4, 5]) 
total = numbers.reduce(lambda a, b: a + b)
 print(total) # Output: 15

4. RDD Persistence (Caching and Checkpointing)

cache(): Stores RDD in memory to speed up computation.

persist(storageLevel): Stores RDD in memory or disk with a specific storage level.

Example: rdd = sc.parallelize(range(1, 10000)) rdd.cache() # Cache the RDD in memory

5. Pair RDDs (Key-Value RDDs)

Useful for working with key-value pairs.

Common Operations:

summed = pairs.reduceByKey(lambda a, b: a + b)

print(summed.collect()) # Output: [('a', 4), ('b', 2)]

PySpark Core vs. PySpark SQL

While PySpark Core uses RDDs, PySpark SQL works with DataFrames (more optimized and user-friendly). However, RDDs are still useful for low-level control over distributed processing.

When to Use PySpark Core (RDDs)?

When working with low-level transformations and actions.
When handling unstructured or semi-structured data.
When requiring fine-grained control over execution.
When implementing custom partitioning.

For most tasks, PySpark SQL (DataFrames) is preferred due to better performance and optimizations.

PySpark RDD Transformations & Complex Use Cases

rdd = sc.parallelize([("Alice", 85), ("Bob", 90), ("Alice", 95)])
rdd_grouped = rdd.groupByKey().mapValues(list)
print(rdd_grouped.collect())  
# Output: [('Alice', [85, 95]), ('Bob', [90])]

🔴 Problem: Shuffles all data → Use reduceByKey() if aggregation is needed.

✅ 6. `reduceByKey(func)` (Pair RDD)

Description:

Aggregates values by key using a function.

Complex Use Case:

Finding total sales per product.

rdd = sc.parallelize([("apple", 5), ("banana", 10), ("apple", 7)])
rdd_reduced = rdd.reduceByKey(lambda x, y: x + y)
print(rdd_reduced.collect())  
# Output: [('apple', 12), ('banana', 10)]

rdd_names = sc.parallelize([("101", "Alice"), ("102", "Bob")])
rdd_scores = sc.parallelize([("101", 85), ("102", 90)])

rdd_joined = rdd_names.join(rdd_scores)
print(rdd_joined.collect())  
# Output: [('101', ('Alice', 85)), ('102', ('Bob', 90))]

🔴 Can cause data shuffling. Use broadcast variables if one RDD is small.

✅ 10. `coalesce(numPartitions)`

Description:

Reduces the number of partitions.

Complex Use Case:

Optimizing small datasets after filtering.

rdd = sc.parallelize(range(100), numSlices=10)
rdd_coalesced = rdd.coalesce(5)
print(rdd_coalesced.getNumPartitions())  
# Output: 5

✅ Efficient: Works without shuffle (unless shuffle=True).

✅ 11. `repartition(numPartitions)`

Description:

Increases or decreases the number of partitions with shuffling.

Complex Use Case:

Rebalancing partitions before expensive operations.

rdd = sc.parallelize(range(100), numSlices=5)
rdd_repartitioned = rdd.repartition(10)
print(rdd_repartitioned.getNumPartitions())  
# Output: 10

rdd1 = sc.parallelize([1, 2, 3])
rdd2 = sc.parallelize(["Apple", "Banana", "Cherry"])

rdd_zipped = rdd1.zip(rdd2)
print(rdd_zipped.collect())  
# Output: [(1, 'Apple'), (2, 'Banana'), (3, 'Cherry')]

🔴 Requires same number of partitions and elements.

🚀 Summary

Transformation	Use Case
`map()`	Modify elements
`flatMap()`	Split sentences
`filter()`	Extract logs
`distinct()`	Remove duplicates
`reduceByKey()`	Aggregate sales
`join()`	Merge datasets
`coalesce()`	Optimize small data
`zip()`	Pair data

🔥 Use Cases for Transforming Spark DataFrames to RDDs and Performing Operations

In PySpark, we often transform DataFrames (DFs) to RDDs when we need fine-grained control over data, perform custom transformations that are difficult in DataFrames, or leverage RDD-specific operations. However, converting a DF to an RDD loses schema information, so it’s recommended only when necessary.

✅ 1. Complex Custom Transformations (Not Available in Spark SQL)

Use Case: Applying a custom transformation that requires stateful row-wise operations

If a transformation is too complex for DataFrame APIs (e.g., multi-row aggregation, state tracking), using RDDs can be more efficient.

🔹 Example: Convert a DataFrame of transactions into an RDD, and track cumulative revenue per user.

from pyspark.sql import SparkSession, Row

spark = SparkSession.builder.appName("DF_to_RDD").getOrCreate()

# Creating a sample DataFrame
data = [("Alice", 100), ("Bob", 200), ("Alice", 300), ("Bob", 100)]
df = spark.createDataFrame(data, ["user", "amount"])

# Convert to RDD
rdd = df.rdd.map(lambda row: (row.user, row.amount))

# Custom transformation: cumulative sum per user
from collections import defaultdict
user_revenue = defaultdict(int)

def cumulative_sum(record):
    user, amount = record
    user_revenue[user] += amount
    return (user, user_revenue[user])

result_rdd = rdd.map(cumulative_sum)
print(result_rdd.collect())

# Output (May vary due to parallel execution):
# [('Alice', 100), ('Bob', 200), ('Alice', 400), ('Bob', 300)]

✅ Why RDD? This approach allows tracking cumulative state across records, which is hard in Spark SQL.

✅ 2. Handling JSON/Complex Data Structures in a Row

Use Case: When a DataFrame has nested JSON structures that need row-wise parsing.

🔹 Example: Extract fields from a JSON column using RDDs.

from pyspark.sql import Row
import json

data = [Row(id=1, json_data='{"name": "Alice", "age": 25}'),
        Row(id=2, json_data='{"name": "Bob", "age": 30}')]

df = spark.createDataFrame(data)

# Convert to RDD
rdd = df.rdd.map(lambda row: (row.id, json.loads(row.json_data)["name"], json.loads(row.json_data)["age"]))

# Convert back to DataFrame with new schema
df_transformed = rdd.toDF(["id", "name", "age"])
df_transformed.show()

✅ Why RDD? It allows working with nested structures in a way that DataFrame SQL functions don’t easily support.

✅ 3. Custom Sorting and Partitioning for Optimization

Use Case: When we need fine-grained control over how data is partitioned or sorted.

🔹 Example: Sorting data by multiple custom keys before converting it back to a DataFrame.

data = [("Alice", 25, 50000), ("Bob", 30, 60000), ("Charlie", 22, 55000)]
df = spark.createDataFrame(data, ["name", "age", "salary"])

# Convert to RDD and sort by age first, then by salary
sorted_rdd = df.rdd.sortBy(lambda row: (row.age, row.salary), ascending=True)

# Convert back to DataFrame
sorted_df = sorted_rdd.toDF(["name", "age", "salary"])
sorted_df.show()

✅ Why RDD? DataFrame .orderBy() can be costly; RDD sorting allows finer control over key-based ordering.

✅ 4. Row-Level Data Anonymization & Masking

Use Case: Anonymizing sensitive data before processing it further.

🔹 Example: Mask email addresses before writing to a new table.

data = [("Alice", "alice@example.com"), ("Bob", "bob@example.com")]
df = spark.createDataFrame(data, ["name", "email"])

# Convert to RDD and anonymize email
rdd = df.rdd.map(lambda row: (row.name, row.email.split("@")[0] + "@****.com"))

# Convert back to DataFrame
masked_df = rdd.toDF(["name", "masked_email"])
masked_df.show()

✅ Why RDD? Some transformations, like string manipulation, are easier with Python’s built-in functions.

✅ 5. Handling Heterogeneous Data (Different Column Types per Row)

Use Case: When DataFrame rows have different schemas or formats dynamically.

🔹 Example: Processing a dataset with mixed types and dynamically inferring schema.

data = [(1, "Alice", 25), (2, "Bob", "unknown"), (3, "Charlie", 30)]
df = spark.createDataFrame(data, ["id", "name", "age"])

# Convert to RDD and fix mixed data types
rdd = df.rdd.map(lambda row: (row.id, row.name, int(row.age) if row.age.isdigit() else None))

# Convert back to DataFrame
fixed_df = rdd.toDF(["id", "name", "age"])
fixed_df.show()

✅ Why RDD? Unlike DataFrame operations, RDD allows handling heterogeneous row structures dynamically.

✅ 6. Custom Grouping & Aggregation (Beyond Built-in SQL Functions)

Use Case: Implementing custom aggregations that aren’t directly supported by Spark SQL.

🔹 Example: Computing a weighted average manually.

data = [("Alice", 80, 4), ("Alice", 90, 6), ("Bob", 70, 3), ("Bob", 60, 2)]
df = spark.createDataFrame(data, ["name", "score", "weight"])

# Convert to RDD
rdd = df.rdd.map(lambda row: (row.name, (row.score * row.weight, row.weight)))

# Compute weighted average
aggregated_rdd = rdd.reduceByKey(lambda a, b: (a[0] + b[0], a[1] + b[1]))
weighted_avg_rdd = aggregated_rdd.map(lambda x: (x[0], x[1][0] / x[1][1]))

# Convert back to DataFrame
result_df = weighted_avg_rdd.toDF(["name", "weighted_avg"])
result_df.show()

✅ Why RDD? Custom aggregations require more control than SQL provides.

🚀 When Should You Convert DataFrame to RDD?

Use Case	Why Use RDD?
Custom row-wise transformations	DataFrame functions may be too restrictive
Handling JSON/nested data	Easier to parse in Python before converting back
Fine-grained sorting	Custom multi-key ordering control
Data masking	Easier string manipulation for security
Heterogeneous data	RDDs allow handling mixed schemas dynamically
Custom aggregations	Some aggregations are not easy in DataFrame APIs

🔥 Key Takeaways

✔ RDDs provide more flexibility but lose schema information.
✔ Convert to RDD only if necessary—DataFrame optimizations are usually better.
✔ Best use cases: Custom transformations, JSON handling, complex aggregations, stateful row operations.

Yes, there is a difference in how RDDs and DataFrames are created and executed in PySpark. Let’s break it down.

1️⃣ RDD Creation & Execution

RDDs require SparkContext (sc) for creation.
They use low-level transformations and actions.
They are lazy, meaning they execute only when an action is called.

RDD Creation Examples

from pyspark.sql import SparkSession

# Create SparkSession
spark = SparkSession.builder.appName("RDDExample").getOrCreate()

# Get SparkContext from SparkSession
sc = spark.sparkContext  

# Create RDD from a list
rdd = sc.parallelize([1, 2, 3, 4, 5])

# Apply transformation (lazy, does not execute yet)
rdd_squared = rdd.map(lambda x: x * x)

# Action triggers execution
print(rdd_squared.collect())  # Output: [1, 4, 9, 16, 25]

RDD Execution Flow

RDD is created → Stored as a lineage graph (DAG, not executed yet).
Transformations are applied → Still lazy.
Action is triggered → Spark executes the DAG and computes results.

2️⃣ DataFrame Creation & Execution

DataFrames are created using SparkSession (not SparkContext).
They are optimized internally using the Catalyst Optimizer.
They are lazily evaluated like RDDs, but optimizations (like predicate pushdown) make them faster.

DataFrame Creation Examples

# Create SparkSession (No need for SparkContext explicitly)
spark = SparkSession.builder.appName("DFExample").getOrCreate()

# Create DataFrame from a list
df = spark.createDataFrame([(1, "Alice"), (2, "Bob")], ["id", "name"])

# Apply transformation (lazy)
df_filtered = df.filter(df.id > 1)

# Action triggers execution
df_filtered.show()

DataFrame Execution Flow

DataFrame is created → Internally optimized into a logical plan.
Transformations are applied → Still lazy, but optimized by the Catalyst Optimizer.
Action is triggered (show(), collect(), etc.) → Spark executes the optimized DAG and computes results.

3️⃣ Key Differences in Execution

Feature	RDD	DataFrame
Requires SparkContext?	✅ Yes (`sc`)	❌ No (Uses `spark`)
Optimized Execution?	❌ No	✅ Yes (Catalyst Optimizer)
Lazy Evaluation?	✅ Yes	✅ Yes
Schema?	❌ No	✅ Yes (uses `StructType`)
Storage Format?	Raw distributed objects	Optimized (columnar storage)
Best for?	Low-level operations, custom partitioning	Structured data, SQL-like queries

4️⃣ When is RDD Explicitly Needed?

When dealing with low-level transformations.
When performing custom partitioning or optimizations.
When working with unstructured or complex data.

Otherwise, DataFrames are preferred because they are faster and optimized.

🔍 Serialization and Deserialization in RDDs vs. DataFrames

In Apache Spark, serialization and deserialization (SerDe) play a critical role in how data is processed across a cluster. The major performance difference between RDDs and DataFrames arises from how they handle serialization and deserialization (SerDe) when distributing data.

🔥 1. Why Does RDD Require Serialization/Deserialization?

RDD (Resilient Distributed Dataset) is a low-level API where each record is a raw Python object (e.g., list, tuple, dictionary).
Since Spark runs on a distributed cluster, data must be serialized (converted into a byte stream) before being sent to worker nodes, and then deserialized (converted back into an object) when received.
Serialization is expensive in terms of CPU and memory usage.

Example of RDD Serialization Overhead

Let’s create an RDD and analyze serialization:

import pickle
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("RDD_Serialization").getOrCreate()

data = [("Alice", 25), ("Bob", 30), ("Charlie", 35)]
rdd = spark.sparkContext.parallelize(data)

# Simulating serialization manually
serialized_data = rdd.map(lambda x: pickle.dumps(x))  # Converting Python object to byte stream
deserialized_data = serialized_data.map(lambda x: pickle.loads(x))  # Converting back to Python object

print(deserialized_data.collect())

💡 Issues with RDD Serialization:
✔ Each Python object needs to be serialized before sending over the network.
✔ Deserialization happens every time an action is performed.
✔ This overhead reduces performance significantly when handling large datasets.

🔥 2. Why DataFrames Avoid Serialization Overhead?

DataFrames use Spark’s Tungsten Execution Engine, which optimizes execution by using off-heap memory and a binary format.
Instead of serializing entire Python objects, DataFrames store data in an optimized columnar format (Arrow, Parquet, ORC, etc.).
This avoids the costly process of serializing/deserializing Python objects across the cluster.

Example of DataFrame Avoiding Serialization Overhead

from pyspark.sql import Row

data = [("Alice", 25), ("Bob", 30), ("Charlie", 35)]
df = spark.createDataFrame(data, ["name", "age"])

# Convert DataFrame to RDD (this will now need serialization)
rdd = df.rdd

print(rdd.collect())  # Incurs serialization overhead
print(df.show())  # Avoids serialization overhead

💡 Why DataFrame is Faster?
✔ Uses Tungsten’s binary format (not Python objects).
✔ Uses vectorized execution instead of row-based execution.
✔ Avoids unnecessary serialization by keeping data in an optimized structure.

🔥 3. Performance Benchmark: RDD vs. DataFrame

Let’s test performance with a large dataset.

RDD Serialization Cost

import time

big_rdd = spark.sparkContext.parallelize([(i, i * 2) for i in range(1_000_000)])

start_time = time.time()
rdd_result = big_rdd.map(lambda x: (x[0], x[1] + 10)).collect()
end_time = time.time()

print(f"RDD Execution Time: {end_time - start_time:.3f} seconds")

DataFrame (No Serialization Overhead)

big_df = spark.createDataFrame([(i, i * 2) for i in range(1_000_000)], ["id", "value"])

start_time = time.time()
df_result = big_df.withColumn("value", big_df.value + 10).collect()
end_time = time.time()

print(f"DataFrame Execution Time: {end_time - start_time:.3f} seconds")

📊 Expected Results:

Operation	RDD (SerDe Overhead)	DataFrame (Optimized Execution)
Map Transformation	Slower due to Python object serialization	Faster due to Tungsten optimization
Collect Action	High deserialization cost	Direct columnar format access
Memory Usage	More due to Python objects	Less due to binary format

🔥 4. Key Differences Between RDD and DataFrame Serialization

Feature	RDD	DataFrame
Serialization Needed?	✅ Yes (Python objects)	❌ No (Binary format)
Execution Engine	Standard JVM Execution	Tungsten + Catalyst Optimizer
Data Format	Raw Python Objects	Columnar Binary Format
Performance	Slower (Serialization/Deserialization overhead)	Faster (Avoids Python object overhead)
Best Use Case	When you need low-level transformations	When you need high-performance operations

🚀 Key Takeaways

✔ RDDs require serialization of Python objects, leading to performance overhead.
✔ DataFrames use optimized binary formats, avoiding unnecessary serialization.
✔ Tungsten engine + Columnar storage in DataFrames make them much faster.
✔ Use RDDs only when necessary (e.g., for custom transformations, complex aggregations).

🚀 Deep Dive into Tungsten and Catalyst Optimizations in Spark

Apache Spark is optimized for high-performance distributed computing, and two key technologies enable this:
1️⃣ Tungsten Execution Engine – Handles low-level memory management & CPU optimizations.
2️⃣ Catalyst Optimizer – Handles logical and physical query optimizations.

Understanding these optimizations will help you write highly efficient PySpark code. Let’s break it down. 👇

🔥 1. Tungsten Execution Engine (CPU & Memory Optimization)

Before Tungsten, Spark used JVM-based object serialization for data storage & movement, which was slow and memory inefficient.

✅ Tungsten optimizes this using:

Managed memory allocation (off-heap storage)
Efficient serialization (binary format, avoiding Java/Python object overhead)
Whole-stage code generation (WSCG) to optimize CPU execution
Vectorized processing (processing multiple rows at a time like CPUs do with SIMD instructions)

🛠️ Tungsten Optimization Breakdown

1.1 Off-Heap Memory Management (Avoiding JVM Garbage Collection)

Before Tungsten:

Spark stored data on JVM heap, causing high GC (Garbage Collection) overhead.
Frequent object creation & destruction slowed performance.

After Tungsten:

Stores data off-heap (like Apache Arrow format), avoiding JVM overhead.
Direct memory access (DMA) reduces serialization time.

💡 Example: RDD vs DataFrame Memory Efficiency

import time
import numpy as np

data = [(i, np.random.rand()) for i in range(1_000_000)]

# RDD - Uses JVM heap memory (Slow)
rdd = spark.sparkContext.parallelize(data)

start = time.time()
rdd_result = rdd.map(lambda x: (x[0], x[1] * 2)).collect()
end = time.time()
print(f"RDD Execution Time: {end - start:.3f} sec")

# DataFrame - Uses Tungsten (Fast)
df = spark.createDataFrame(data, ["id", "value"])

start = time.time()
df_result = df.withColumn("value", df["value"] * 2).collect()
end = time.time()
print(f"DataFrame Execution Time: {end - start:.3f} sec")

✅ Tungsten DataFrame runs ~5x faster than RDD! 🚀

1.2 Whole-Stage Code Generation (WSCG)

Spark dynamically compiles SQL queries into optimized JVM bytecode.
This eliminates interpreter overhead and runs optimized machine code.
Best for complex transformations like aggregations & joins.

💡 Example: RDD vs DataFrame Execution Plan

df.explain(True)  # Shows Tungsten Optimized Plan

🚀 Output:

WholeStageCodegen
+- HashAggregate
   +- Exchange
      +- HashAggregate
         +- Project
            +- Filter
               +- Scan parquet

✅ Spark combines multiple stages into one highly optimized execution block.

1.3 Vectorized Processing (SIMD Optimization)

Instead of processing rows one by one, Spark processes multiple rows at once (batch processing) using SIMD (Single Instruction Multiple Data).
This works like GPUs that process multiple pixels at a time.
Spark’s vectorized Parquet & ORC readers improve performance by ~10x for I/O-heavy workloads.

💡 Example: Enabling Vectorized Processing for Parquet

spark.conf.set("spark.sql.parquet.enableVectorizedReader", "true")

✅ DataFrame operations on Parquet are now 10x faster!

🔥 2. Catalyst Optimizer (Logical & Physical Query Optimization)

The Catalyst optimizer is Spark’s query planner that rewrites queries for maximum efficiency. It performs 4 major optimizations:

2.1 Logical Plan Optimization (SQL-like Query Rewriting)

✔ Pushdown Filters: Moves WHERE conditions before scanning data.
✔ Column Pruning: Removes unnecessary columns before loading data.

💡 Example: Query Optimization

df = spark.read.parquet("data.parquet")

# Without optimization: Reads entire dataset
df.select("name").show()

# With optimization: Reads only required columns
df.select("name").explain(True)

🚀 Spark will prune unnecessary columns, reducing I/O.

2.2 Physical Plan Optimization (Execution Strategies)

✔ Broadcast Joins for small datasets (avoiding expensive shuffle joins).
✔ Predicate Pushdown (Applying filters before data is read).

💡 Example: Enabling Broadcast Joins for Faster Execution

from pyspark.sql.functions import broadcast

small_df = spark.read.parquet("small_data.parquet")
large_df = spark.read.parquet("large_data.parquet")

# Normal Join (Slow Shuffle)
result = large_df.join(small_df, "id")

# Broadcast Join (Fast)
result = large_df.join(broadcast(small_df), "id")

✅ Broadcast join avoids expensive shuffling! 🚀

2.3 Adaptive Query Execution (AQE)

Spark dynamically adjusts query execution at runtime for better performance.
✔ Re-optimizes joins dynamically based on data size.
✔ Dynamically coalesces partitions to avoid small file problems.

💡 Enable AQE for Auto-Optimized Queries

spark.conf.set("spark.sql.adaptive.enabled", "true")

✅ Spark will now dynamically optimize partitions & joins!

🔥 3. Comparing RDD vs. DataFrame Performance

Feature	RDD	DataFrame (Tungsten + Catalyst)
Memory Management	Uses JVM heap (slow GC)	Off-heap (fast)
Execution Engine	Interpreter overhead	Whole-Stage CodeGen
Query Optimization	Manual	Catalyst Optimizer
Serialization	Python objects (slow)	Binary format (fast)
Processing Model	Row-by-row	Vectorized (batch processing)
Joins & Aggregations	Expensive shuffle	AQE + Broadcast Joins
Best Use Case	Complex transformations	High-performance queries

🚀 Key Takeaways

✔ Tungsten makes Spark faster by optimizing memory & CPU execution.
✔ Catalyst makes queries smarter by rewriting & optimizing execution plans.
✔ Vectorized execution & SIMD processing give ~10x speedup.
✔ RDDs should be avoided unless necessary—DataFrames & Datasets are much more optimized.
✔ Enabling AQE & Broadcast Joins can drastically improve performance.

import time
from pyspark.sql import SparkSession
from pyspark.sql.functions import broadcast

# Initialize Spark Session
spark = SparkSession.builder \
    .appName("Performance Benchmarking") \
    .config("spark.sql.adaptive.enabled", "true") \
    .getOrCreate()

# Large dataset for benchmarking
data = [(i, i * 2) for i in range(1_000_000)]

# ------------------- RDD Performance ------------------- #
print("\nBenchmarking RDD Performance...")
rdd = spark.sparkContext.parallelize(data)

# Test RDD Execution Time
start_time = time.time()
rdd_result = rdd.map(lambda x: (x[0], x[1] + 10)).collect()
end_time = time.time()
print(f"RDD Execution Time: {end_time - start_time:.3f} sec")

# ------------------- DataFrame Performance ------------------- #
print("\nBenchmarking DataFrame Performance...")
df = spark.createDataFrame(data, ["id", "value"])

# Test DataFrame Execution Time
start_time = time.time()
df_result = df.withColumn("value", df.value + 10).collect()
end_time = time.time()
print(f"DataFrame Execution Time: {end_time - start_time:.3f} sec")

# ------------------- Broadcast Join vs. Shuffle Join ------------------- #
print("\nBenchmarking Join Performance...")
small_df = spark.createDataFrame([(i, i * 3) for i in range(1000)], ["id", "value"])
large_df = spark.createDataFrame([(i, i * 5) for i in range(1_000_000)], ["id", "value"])

# Shuffle Join (Slow)
start_time = time.time()
shuffle_result = large_df.join(small_df, "id").collect()
end_time = time.time()
print(f"Shuffle Join Execution Time: {end_time - start_time:.3f} sec")

# Broadcast Join (Fast)
start_time = time.time()
broadcast_result = large_df.join(broadcast(small_df), "id").collect()
end_time = time.time()
print(f"Broadcast Join Execution Time: {end_time - start_time:.3f} sec")

# Stop Spark Session
spark.stop()

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1. Importing the re Module

2. Basic Functions

re.search()

re.match()

re.findall()

re.finditer()

re.sub()

3. Special Characters

4. Escaping Special Characters

5. Character Classes

6. Groups and Capturing

7. Named Groups

8. Lookahead and Lookbehind

Positive Lookahead

Negative Lookahead

Positive Lookbehind

Negative Lookbehind

9. Compiling Regular Expressions

10. Flags

Example: Searching for Emails in a Database

Example: Searching for Code Snippets in a Code Repository

1.Exploratory Data Analysis (EDA) with Pandas in Banking – Converted in Pyspark

Importing Libraries

Loading Data

Initial Exploration

Columns

Info

Describe

Value Counts

Sorting

Applying Functions

Further Analysis

Crosstab and Pivot Table

Plots

Combining Code

2.Dynamic list of variables Creation for ETL Jobs

Step 1: Generate Dynamic Variable Names

Step 2: Create CSV Files by Filtering on Year and Month

Step 3: Create Arrays Based on Dynamic Dictionary

Step 4: Create Custom Excel Files

Combining It All Together

2.SAS project to Pyspark Migration- merging, joining, transposing , lead/rank functions, data validation with PROC FREQ, error handling, macro variables, and macros

Step 1: Set Up Macro Variables for Date Values

Step 2: Define Macros for Various Functional Tasks

Step 3: Import Large Tables

Step 4: Merge and Join Tables

Step 5: Transpose the Data

Step 6: Apply PROC SQL Lead/Rank Functions

Step 7: Validate Data with PROC FREQ

Step 8: Error Handling

Full Example in One Go

Method1

Method 2

Method 3

Step 1: Prepare the Control DataFrame with Code Steps

Sample Monthly PySpark Script

Sample PySpark Script

Step 2: Create a Status Table

Step 3: Execute Each Code Step with Error Handling and Status Tracking

Full Example Script

Example: Dynamic Code Execution in PySpark

1. Initialize Spark Session

2. Create Sample Control DataFrame

3. Create Log and Status DataFrames

4. Define Functions for Error Handling and Logging

5. Execute Code Snippets Sequentially

Explanation

Scheduling the Script

Building a ETL Data pipeline in Pyspark and using Pandas and Matplotlib for Further Processing

tep 1: Environment Setup

Step 2: Data Extraction

Step 3: Data Transformation

Step 4: Data Loading

Step 5: Data Analysis and Visualization

Step 6: Deployment with Bitbucket and Jenkins

Example Code

Spark Session Initialization

Data Extraction

1. Importing the `re` Module

`re.search()`

`re.match()`

`re.findall()`

`re.finditer()`

`re.sub()`