Coding Questions in Spark SQL, Pyspark, and Python

To compare the time taken for clients who placed their first order with an offer versus those without an offer to make their next order?

Table Name: Orders
Column Names: order_id (Order ID), user_id (User ID), is_offer (Is Offer), Date_Timestamp (Date Timestamp) using pyspark sql, pyspark dataframe, and python

To solve this problem, we’ll follow the below approach:

1. Sample Data Generation: First, generate the Orders table data with columns such as order_id, user_id, is_offer, and date_timestamp.
2. Calculate the time between the first order and the next order for each user who placed an order.
3. Group and compare users who placed their first order with an offer vs. without an offer.

The solution will be provided using:

• PySpark SQL Query
• PySpark DataFrame API
• Python solution with Pandas

1. Sample Data Generation

Let’s generate sample data to represent the Orders table:

data = [
    (1, 101, 1, "2023-01-01 10:00:00"),  # User 101, first order with offer
    (2, 101, 0, "2023-01-05 09:30:00"),  # User 101, next order without offer
    (3, 102, 0, "2023-01-02 11:15:00"),  # User 102, first order without offer
    (4, 102, 1, "2023-01-10 08:45:00"),  # User 102, next order with offer
    (5, 103, 1, "2023-01-03 15:20:00"),  # User 103, first order with offer
    (6, 103, 0, "2023-01-08 16:00:00"),  # User 103, next order without offer
    (7, 104, 0, "2023-01-04 14:10:00"),  # User 104, first order without offer
    (8, 104, 0, "2023-01-15 09:25:00")   # User 104, next order without offer
]

columns = ["order_id", "user_id", "is_offer", "date_timestamp"]

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

2. PySpark SQL Query

# Register DataFrame as a temporary SQL view
df.createOrReplaceTempView("orders")

# SQL query to calculate time difference between first order and the next for users with and without offers
query = """
WITH first_orders AS (
    SELECT 
        user_id, 
        is_offer, 
        MIN(date_timestamp) AS first_order_time
    FROM 
        orders
    GROUP BY 
        user_id, is_offer
),
next_orders AS (
    SELECT 
        o.user_id, 
        o.date_timestamp AS next_order_time,
        f.is_offer,
        f.first_order_time
    FROM 
        orders o
    INNER JOIN 
        first_orders f 
        ON o.user_id = f.user_id AND o.date_timestamp > f.first_order_time
)
SELECT 
    f.user_id,
    f.is_offer,
    MIN(next_order_time) AS next_order_time,
    DATEDIFF(MIN(next_order_time), f.first_order_time) AS days_to_next_order
FROM 
    first_orders f
JOIN 
    next_orders n
ON 
    f.user_id = n.user_id AND f.is_offer = n.is_offer
GROUP BY 
    f.user_id, f.is_offer, f.first_order_time
ORDER BY 
    f.user_id;
"""

# Run the SQL query
result_sql = spark.sql(query)
result_sql.show()

3. PySpark DataFrame API

Here’s how you can achieve the same result using PySpark DataFrame API.

from pyspark.sql import functions as F
from pyspark.sql.window import Window

# Convert date_timestamp to a timestamp type
df = df.withColumn("date_timestamp", F.to_timestamp("date_timestamp"))

# Get the first order time per user
window_spec = Window.partitionBy("user_id").orderBy("date_timestamp")
df_first_order = df.withColumn("first_order_time", F.first("date_timestamp").over(window_spec))

# Filter for first orders only
first_orders = df.filter(F.col("date_timestamp") == F.col("first_order_time"))

# Get next orders for each user after their first order
next_orders = df.alias("o").join(first_orders.alias("f"), (F.col("o.user_id") == F.col("f.user_id")) & (F.col("o.date_timestamp") > F.col("f.date_timestamp")))

# Calculate the time difference between first and next orders
result_df = next_orders.groupBy("o.user_id", "f.is_offer").agg(
    F.min("o.date_timestamp").alias("next_order_time"),
    F.datediff(F.min("o.date_timestamp"), F.col("f.first_order_time")).alias("days_to_next_order")
)

# Show the results
result_df.show()

4. Python Solution (Pandas)

Here’s the equivalent solution using pandas in Python:

import pandas as pd
from datetime import datetime

# Sample data
data = {
    'order_id': [1, 2, 3, 4, 5, 6, 7, 8],
    'user_id': [101, 101, 102, 102, 103, 103, 104, 104],
    'is_offer': [1, 0, 0, 1, 1, 0, 0, 0],
    'date_timestamp': [
        "2023-01-01 10:00:00", "2023-01-05 09:30:00", "2023-01-02 11:15:00",
        "2023-01-10 08:45:00", "2023-01-03 15:20:00", "2023-01-08 16:00:00",
        "2023-01-04 14:10:00", "2023-01-15 09:25:00"
    ]
}

# Create DataFrame
df = pd.DataFrame(data)
df['date_timestamp'] = pd.to_datetime(df['date_timestamp'])

# Get first order time for each user
df['first_order_time'] = df.groupby('user_id')['date_timestamp'].transform('min')

# Filter for first orders
first_orders = df[df['date_timestamp'] == df['first_order_time']]

# Get next orders for each user after their first order
next_orders = df[df['date_timestamp'] > df['first_order_time']]

# Merge first and next orders
merged_orders = first_orders[['user_id', 'is_offer', 'first_order_time']].merge(
    next_orders[['user_id', 'date_timestamp']],
    on='user_id'
)

# Calculate the time difference
merged_orders['days_to_next_order'] = (merged_orders['date_timestamp'] - merged_orders['first_order_time']).dt.days

# Show the result
print(merged_orders)

Explanation:

• PySpark SQL Query: We used window functions to get the first order for each user and joined with other orders to get the next order after the first order.
• PySpark DataFrame API: We used the PySpark functions min, datediff, and first with window specifications to replicate the same logic as in SQL.
• Python Pandas: A straightforward approach using pandas for a Python-based solution, leveraging groupby and date differences to achieve the desired result.

---