Using LEAD or LAG¶

Let us understand LEAD and LAG functions to get column values from following or prior records.

Let us start spark context for this Notebook so that we can execute the code provided. You can sign up for our 10 node state of the art cluster/labs to learn Spark SQL using our unique integrated LMS.

val username = System.getProperty("user.name")

import org.apache.spark.sql.SparkSession

val username = System.getProperty("user.name")
val spark = SparkSession.
    builder.
    config("spark.ui.port", "0").
    config("spark.sql.warehouse.dir", s"/user/${username}/warehouse").
    enableHiveSupport.
    appName(s"${username} | Spark SQL - Windowing Functions").
    master("yarn").
    getOrCreate

If you are going to use CLIs, you can use Spark SQL using one of the 3 approaches.

Using Spark SQL

spark2-sql \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Scala

spark2-shell \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Using Pyspark

pyspark2 \
    --master yarn \
    --conf spark.ui.port=0 \
    --conf spark.sql.warehouse.dir=/user/${USER}/warehouse

Here is the example where we can get prior or following records based on ORDER BY within OVER Clause.

%%sql

USE itversity_retail

%%sql

SELECT * FROM daily_revenue
ORDER BY order_date DESC
LIMIT 10

%%sql

SELECT t.*,
  lead(order_date) OVER (ORDER BY order_date DESC) AS prior_date,
  lead(revenue) OVER (ORDER BY order_date DESC) AS prior_revenue,
  lag(order_date) OVER (ORDER BY order_date) AS lag_prior_date,
  lag(revenue) OVER (ORDER BY order_date) AS lag_prior_revenue
FROM daily_revenue AS t
ORDER BY order_date DESC
LIMIT 10

%%sql

SELECT t.*,
  lead(order_date) OVER (ORDER BY order_date DESC) AS prior_date,
  lead(revenue) OVER (ORDER BY order_date DESC) AS prior_revenue
FROM daily_revenue AS t
ORDER BY order_date
LIMIT 10

We can also pass number of rows as well as default values for nulls as arguments.

%%sql

USE itversity_retail

%%sql

SELECT t.*,
  lead(order_date, 7) OVER (ORDER BY order_date DESC) AS prior_date,
  lead(revenue, 7) OVER (ORDER BY order_date DESC) AS prior_revenue
FROM daily_revenue t
ORDER BY order_date DESC
LIMIT 10

%%sql

SELECT t.*,
  lead(order_date, 7) OVER (ORDER BY order_date DESC) AS prior_date,
  lead(revenue, 7) OVER (ORDER BY order_date DESC) AS prior_revenue
FROM daily_revenue t
ORDER BY order_date
LIMIT 10

%%sql

SELECT t.*,
  lead(order_date, 7, 'NA') OVER (ORDER BY order_date DESC) AS prior_date,
  lead(revenue, 7, 0) OVER (ORDER BY order_date DESC) AS prior_revenue
FROM daily_revenue t
ORDER BY order_date
LIMIT 10

Let us see how we can get prior or following records with in a group based on particular order.

Here is the example where we can get prior or following records based on PARTITION BY and then ORDER BY Clause.

%%sql

USE itversity_retail

%%sql

DESCRIBE daily_product_revenue

%%sql

SELECT * FROM daily_product_revenue 
ORDER BY order_date, revenue DESC
LIMIT 10

%%sql

SELECT t.*,
  LEAD(order_item_product_id) OVER (
    PARTITION BY order_date 
    ORDER BY revenue DESC
  ) next_product_id,
  LEAD(revenue) OVER (
    PARTITION BY order_date 
    ORDER BY revenue DESC
  ) next_revenue
FROM daily_product_revenue t
ORDER BY order_date, revenue DESC
LIMIT 100

spark.sql("""
    SELECT t.*,
      LEAD(order_item_product_id) OVER (
        PARTITION BY order_date 
        ORDER BY revenue DESC
      ) next_product_id,
      LEAD(revenue) OVER (
        PARTITION BY order_date 
        ORDER BY revenue DESC
      ) next_revenue
    FROM daily_product_revenue t
    ORDER BY order_date, revenue DESC
""").
    show(100, false)

We can also pass number of rows as well as default values for nulls as arguments.

%%sql

SELECT t.*,
  LEAD(order_item_product_id) OVER (
    PARTITION BY order_date ORDER BY revenue DESC
  ) next_product_id,
  LEAD(revenue, 1, 0) OVER (
    PARTITION BY order_date ORDER BY revenue DESC
  ) next_revenue
FROM daily_product_revenue t
ORDER BY order_date, revenue DESC
LIMIT 100

spark.sql("""
    SELECT t.*,
      LEAD(order_item_product_id) OVER (
        PARTITION BY order_date ORDER BY revenue DESC
      ) next_product_id,
      LEAD(revenue, 1, 0) OVER (
        PARTITION BY order_date ORDER BY revenue DESC
      ) next_revenue
    FROM daily_product_revenue t
    ORDER BY order_date, revenue DESC
    LIMIT 100
""").
    show(100, false)

Apache Spark using SQL

Using LEAD or LAG¶