Write a CTE in Spark SQL / Pyspark Dataframe to determine the longest streak of consecutive sales by an employee?
WITH cte_ranked AS (
SELECT
employee_id,
sale_date,
ROW_NUMBER() OVER (PARTITION BY employee_id ORDER BY sale_date) AS rn
FROM sales
),
cte_grouped AS (
SELECT
employee_id,
sale_date,
rn - DATEDIFF(sale_date, '1970-01-01') AS group_id
FROM cte_ranked
),
cte_streaks AS (
SELECT
employee_id,
MIN(sale_date) AS start_date,
MAX(sale_date) AS end_date,
COUNT(*) AS streak_length
FROM cte_grouped
GROUP BY employee_id, group_id
)
SELECT
employee_id,
MAX(streak_length) AS longest_streak
FROM cte_streaks
GROUP BY employee_id
ORDER BY employee_id;
PySpark Implementation
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, row_number, lag, datediff, max as spark_max
from pyspark.sql.window import Window
# Initialize Spark Session
spark = SparkSession.builder.appName("LongestStreak").getOrCreate()
# Sample Data
data = [
(1, "2023-12-01", 500),
(1, "2023-12-02", 700),
(1, "2023-12-04", 300),
(2, "2023-12-01", 400),
(2, "2023-12-02", 200),
(2, "2023-12-03", 100),
]
columns = ["employee_id", "sale_date", "sales_amount"]
# Create DataFrame
df = spark.createDataFrame(data, columns)
# Convert sale_date to date type
df = df.withColumn("sale_date", col("sale_date").cast("date"))
# Step 1: Add row number for each employee ordered by sale_date
window_spec = Window.partitionBy("employee_id").orderBy("sale_date")
df_with_rn = df.withColumn("row_num", row_number().over(window_spec))
# Step 2: Calculate group_id by subtracting row_num from the numeric representation of sale_date
df_with_group_id = df_with_rn.withColumn(
"group_id", datediff(col("sale_date"), "1970-01-01") - col("row_num")
)
# Step 3: Group by employee_id and group_id to calculate streak length
streak_df = (
df_with_group_id.groupBy("employee_id", "group_id")
.agg(
spark_max("sale_date").alias("end_date"),
spark_max("sale_date").alias("start_date"),
count("*").alias("streak_length"),
)
)
# Step 4: Determine the longest streak for each employee
longest_streak_df = (
streak_df.groupBy("employee_id").agg(spark_max("streak_length")alias=("longest streak))
)
Streak_df.longest_streak.collect()
Here’s the correct implementation for determining the longest streak of consecutive sales in PySpark:
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, row_number, datediff, max as spark_max, count
from pyspark.sql.window import Window
# Initialize Spark Session
spark = SparkSession.builder.appName("LongestStreak").getOrCreate()
# Sample Data
data = [
(1, "2023-12-01", 500),
(1, "2023-12-02", 700),
(1, "2023-12-04", 300),
(2, "2023-12-01", 400),
(2, "2023-12-02", 200),
(2, "2023-12-03", 100),
]
columns = ["employee_id", "sale_date", "sales_amount"]
# Create DataFrame
df = spark.createDataFrame(data, columns)
# Convert sale_date to date type
df = df.withColumn("sale_date", col("sale_date").cast("date"))
# Step 1: Add row number for each employee ordered by sale_date
window_spec = Window.partitionBy("employee_id").orderBy("sale_date")
df_with_rn = df.withColumn("row_num", row_number().over(window_spec))
# Step 2: Calculate group_id by subtracting row_num from the numeric representation of sale_date
df_with_group_id = df_with_rn.withColumn(
"group_id", datediff(col("sale_date"), "1970-01-01") - col("row_num")
)
# Step 3: Group by employee_id and group_id to calculate streak length
streak_df = (
df_with_group_id.groupBy("employee_id", "group_id")
.agg(
count("*").alias("streak_length"),
spark_max("sale_date").alias("end_date"),
spark_max("sale_date").alias("start_date"),
)
)
# Step 4: Determine the longest streak for each employee
longest_streak_df = (
streak_df.groupBy("employee_id")
.agg(spark_max("streak_length").alias("longest_streak"))
.orderBy("employee_id")
)
# Show the result
longest_streak_df.show()
Generate a sequence of Fibonacci numbers up to a specific value using a recursive CTE or any other method in spark sql?
Spark SQL does not natively support recursive Common Table Expressions (CTEs). Recursive CTEs are often used in traditional SQL databases to generate sequences or hierarchical data structures. However, since Spark SQL doesn’t support recursion in its query language, we need alternative approaches to achieve the same result.
Alternative Methods to Generate Fibonacci Numbers in Spark SQL
Option 1: Iterative Logic with a Temporary Table
You can simulate the Fibonacci sequence generation using iterative logic and a loop in PySpark DataFrame transformations.
Implementation in PySpark:
from pyspark.sql import SparkSession
from pyspark.sql.functions import lit, col, when
# Initialize Spark Session
spark = SparkSession.builder.appName("FibonacciSequence").getOrCreate()
# Define the maximum value up to which the Fibonacci sequence should be generated
max_value = 100
# Step 1: Initialize the DataFrame with the first two Fibonacci numbers
fib_df = spark.createDataFrame([(0, 0), (1, 1)], ["n", "fib_value"])
# Step 2: Iteratively add Fibonacci numbers until the maximum value is reached
while True:
# Calculate the next Fibonacci number
last_two = fib_df.orderBy("n", ascending=False).limit(2).collect()
next_fib = last_two[0]["fib_value"] + last_two[1]["fib_value"]
if next_fib > max_value:
break
# Append the new Fibonacci number to the DataFrame
fib_df = fib_df.union(spark.createDataFrame([(len(fib_df.collect()), next_fib)], ["n", "fib_value"]))
# Show the resulting Fibonacci sequence
fib_df.orderBy("n").show()
Option 2: Generate Fibonacci Using Spark SQL with Self-Joins
Since Spark SQL lacks recursion, you can generate a Fibonacci sequence using a sequence of self-joins up to a predetermined number of iterations.
Implementation in PySpark SQL:
# Create a temporary table for Fibonacci
spark.sql("""
CREATE OR REPLACE TEMP VIEW fibonacci_seed AS
SELECT 0 AS n, 0 AS fib_value
UNION ALL
SELECT 1 AS n, 1 AS fib_value
""")
# Generate Fibonacci sequence up to a certain number of iterations (e.g., 10)
iterations = 10
for i in range(iterations):
spark.sql("""
INSERT INTO fibonacci_seed
SELECT
MAX(n) + 1 AS n,
(SELECT fib_value FROM fibonacci_seed WHERE n = MAX(n) - 1) +
(SELECT fib_value FROM fibonacci_seed WHERE n = MAX(n)) AS fib_value
FROM fibonacci_seed
""")
# Query the resulting Fibonacci sequence
spark.sql("SELECT * FROM fibonacci_seed ORDER BY n").show()Recursive CTE for Fibonacci in SQL
WITH RECURSIVE fibonacci_cte AS (
SELECT 0 AS fib1, 1 AS fib2, 0 AS current_fib -- Base case: First two Fibonacci numbers
UNION ALL
SELECT fib2 AS fib1, fib1 + fib2 AS fib2, fib1 + fib2 AS current_fib
FROM fibonacci_cte
WHERE fib1 + fib2 <= 1000 -- Replace 1000 with your desired maximum value
)
SELECT current_fib
FROM fibonacci_cte
ORDER BY current_fib;