Documentation for Spark Notebook Examples

from pyspark.sql import SparkSession

import random

​

# Initialize Spark session

spark = SparkSession.builder \\

    .appName("Estimate Pi") \\

    .getOrCreate()

​

# Function to check if a point is inside the unit circle

def inside_circle(_):

    x = random.uniform(0, 1)

    y = random.uniform(0, 1)

    return x * x + y * y < 1

​

# Function to estimate Pi

def estimate_pi(num_samples):

    count_inside = spark.sparkContext.parallelize(range(num_samples)) \\

        .filter(inside_circle) \\

        .count()

    pi_estimate = 4 * count_inside _ num_samples

    return pi_estimate

​

# Run estimation

num_samples = 100000

pi_value = estimate_pi(num_samples)

print(f"Estimated value of Pi: {pi_value}")

​

# Stop the Spark session

spark.stop()

from pyspark.sql import SparkSession

from datetime import datetime, date

from pyspark.sql import Row

​

# Initialize Spark session

spark = SparkSession.builder \\

    .appName("MySparkApp") \\

    .getOrCreate()

​

# Create a DataFrame

df = spark.createDataFrame([

    Row(a=1, b=2.0, c='string1', d=date(2000, 1, 1), e=datetime(2000, 1, 1, 12, 0)),

    Row(a=2, b=3.0, c='string2', d=date(2000, 2, 1), e=datetime(2000, 1, 2, 12, 0)),

    Row(a=4, b=5.0, c='string3', d=date(2000, 3, 1), e=datetime(2000, 1, 3, 12, 0))

])

​

df.show()

from pyspark.sql import SparkSession, Row

​

spark = SparkSession.builder \\

    .appName("Complex Spark Example") \\

    .master("yarn") \\

    .config("spark.executor.memory", "2g") \\

    .config("spark.driver.memory", "1g") \\

    .getOrCreate()

​

# Sample data

employee_data = [

    Row(emp_id=1, name='John Doe', age=30, dept_id=1, salary=60000),

    Row(emp_id=2, name='Jane Smith', age=25, dept_id=2, salary=70000),

    Row(emp_id=3, name='Sam Brown', age=45, dept_id=1, salary=80000),

]

department_data = [

    Row(dept_id=1, dept_name='HR'),

    Row(dept_id=2, dept_name='Engineering'),

]

​

employee_df = spark.createDataFrame(employee_data)

department_df = spark.createDataFrame(department_data)

​

joined_df = employee_df.join(department_df, on='dept_id', how='inner')

joined_df.show()

from pyspark.sql.functions import col

from pyspark.sql.types import StringType

​

def salary_category(salary):

    if salary < 70000:

        return 'Low'

    elif 70000 <= salary < 100000:

        return 'Medium'

    else:

        return 'High'

​

salary_category_udf = spark.udf.register("salary_category", salary_category, StringType())

categorized_df = joined_df.withColumn("salary_category", salary_category_udf(col("salary")))

​

categorized_df.select("name", "dept_name", "salary", "salary_category").show()

categorized_df.createOrReplaceTempView("employee_view")

​

avg_salary_query = spark.sql("""

    SELECT dept_name, AVG(salary) AS avg_salary

    FROM employee_view

    GROUP BY dept_name

""")

avg_salary_query.show()

from pyspark.sql import SparkSession

​

spark = SparkSession.builder \\

    .appName("Word Count Example") \\

    .master("local[*]") \\

    .getOrCreate()

​

# Sample data

data = ["hello world", "hello spark", "spark is awesome", "hello world again"]

​

# Word count using RDD

rdd = spark.sparkContext.parallelize(data)

word_counts = rdd.flatMap(lambda line: line.split(" ")) \\

                 .map(lambda word: (word, 1)) \\

                 .reduceByKey(lambda a, b: a + b)

​

# Display results

result = word_counts.collect()

for word, count in result:

    print(f"{word}: {count}")

​

spark.stop()

import os

import findspark

​

# Set environment variables for Spark

os.environ['SPARK_HOME'] = '_usr_odp_3.2.3.2-3_spark3'

os.environ['PYSPARK_PYTHON'] = '_usr_bin_python3'

os.environ['PYSPARK_DRIVER_PYTHON'] = 'python3'

​

# Initialize findspark

findspark.init('_usr_odp_3.2.3.2-3_spark3')

​

from pyspark import SparkContext

​

# Initialize SparkContext

sc = SparkContext.getOrCreate()

import random

​

# Define the number of samples

num_samples = 100000

​

# Function to check if a point is inside the unit circle

def inside_unit_circle(_):

    return (random.random() ** 2 + random.random() ** 2) < 1

​

# Perform the estimation by running a parallel computation

rdd = sc.parallelize(range(num_samples))

count = rdd.map(inside_unit_circle).filter(lambda x: x).count()

​

# Estimate the value of Pi

pi_estimate = 4.0 * count _ num_samples

print("Estimated value of Pi:", pi_estimate)

​

# Stop SparkContext after use (optional in a notebook environment)

# sc.stop()

from pyspark import SparkConf, SparkContext

​

conf = SparkConf() \\

    .setAppName("Pi Estimation") \\

    .setMaster("yarn") \\

    .set("spark.executor.memory", "4g") \\

    .set("spark.executor.cores", "2")

​

sc = SparkContext(conf=conf)

import os

import findspark

​

# Set environment variables for Spark

os.environ['SPARK_HOME'] = '_usr_odp_3.2.3.2-3_spark3'

os.environ['PYSPARK_PYTHON'] = '_usr_bin_python3'

os.environ['PYSPARK_DRIVER_PYTHON'] = 'python3'

​

# Initialize findspark

findspark.init('_usr_odp_3.2.3.2-3_spark3')

​

from pyspark.sql import SparkSession

​

# Initialize SparkSession

spark = SparkSession.builder \\

    .appName("ShowTablesExample") \\

    .enableHiveSupport() \\  # Optional if you use Hive

    .getOrCreate()

​

# Optional: Switch to a specific database

# Uncomment and replace 'my_database' with your database name

# spark.sql("USE my_database")

​

# Execute the SHOW TABLES command

tables_df = spark.sql("SHOW TABLES")

​

# Display the tables

tables_df.show()

​

# Stop SparkSession after use (optional in a notebook environment)

# spark.stop()