How to Use Spark SQL row_number() with PartitionBy for Descending Sort?

Spark SQL’s `row_number()` function is a window function that assigns a unique number to each row based on the specified window partition. The `PARTITION BY` clause is used to partition the data into groups, and the `ORDER BY` clause sorts each partition. If you want to apply the `row_number()` function to partitioned data and sort it in descending order, you can do it using PySpark. Below is a detailed explanation along with a code snippet.

How to Use Spark SQL row_number() with PartitionBy for Descending Sort

Let’s say we have a dataset of employee details, and we want to assign a unique row number to each employee within each department, sorted by their salary in descending order. First, we’ll create a DataFrame with sample data.

Step-by-step Implementation

Step 1: Create a Spark Session


from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Row Number Example") \
    .getOrCreate()

Step 2: Create a Sample DataFrame

We’ll prepare a sample dataset with employee details.


data = [
    (1, 'John', 'Finance', 1000),
    (2, 'Sarah', 'Finance', 2000),
    (3, 'Mike', 'Finance', 1500),
    (4, 'Anna', 'Sales', 1200),
    (5, 'James', 'Sales', 2200)
]

columns = ["ID", "Name", "Department", "Salary"]

df = spark.createDataFrame(data, columns)
df.show()

+---+-----+----------+------+
| ID| Name|Department|Salary|
+---+-----+----------+------+
|  1| John|  Finance | 1000 |
|  2|Sarah|  Finance | 2000 |
|  3| Mike|  Finance | 1500 |
|  4| Anna|   Sales  | 1200 |
|  5|James|   Sales  | 2200 |
+---+-----+----------+------+

Step 3: Use row_number() with PartitionBy and OrderBy

We will use the `row_number()` function to partition by `Department` and sort by `Salary` in descending order within each partition.


from pyspark.sql.window import Window
from pyspark.sql.functions import row_number, desc

windowSpec = Window.partitionBy("Department").orderBy(desc("Salary"))

df_with_row_number = df.withColumn("row_number", row_number().over(windowSpec))
df_with_row_number.show()

+---+-----+----------+------+----------+
| ID| Name|Department|Salary|row_number|
+---+-----+----------+------+----------+
|  2|Sarah|  Finance | 2000 |    1     |
|  3| Mike|  Finance | 1500 |    2     |
|  1| John|  Finance | 1000 |    3     |
|  5|James|   Sales  | 2200 |    1     |
|  4| Anna|   Sales  | 1200 |    2     |
+---+-----+----------+------+----------+

Explanation

In the above code:

  • We create a window specification (`windowSpec`) using the `Window.partitionBy` method to partition the data by the `Department` column.
  • The `orderBy(desc(“Salary”))` part sorts the rows within each partition by the `Salary` column in descending order.
  • Finally, we use the `row_number().over(windowSpec)` function to assign a row number to each row within the partition.

By following these steps, you can successfully use Spark SQL’s `row_number()` function with `PartitionBy` for descending sort to assign unique row numbers within partitions based on specific sorting criteria.

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Our Editorial Team is made up of tech enthusiasts who are highly skilled in Apache Spark, PySpark, and Machine Learning. They are also proficient in Python, Pandas, R, Hive, PostgreSQL, Snowflake, and Databricks. They aren't just experts; they are passionate teachers. They are dedicated to making complex data concepts easy to understand through engaging and simple tutorials with examples.

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