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.