Aggregating Data¶
Let us understand how to aggregate the data.
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 - Basic Transformations").
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
We can perform global aggregations as well as aggregations by key.
Global Aggregations
Get total number of orders.
Get revenue for a given order id.
Get number of records with order_status either COMPLETED or CLOSED.
Aggregations by key - using
GROUP BY
Get number of orders by date or status.
Get revenue for each order_id.
Get daily product revenue (using order date and product id as keys).
We can also use
HAVING
clause to apply filtering on top of aggregated data.Get daily product revenue where revenue is greater than $500 (using order date and product id as keys).
Rules while using
GROUP BY
.We can have the columns which are specified as part of
GROUP BY
inSELECT
clause.On top of those, we can have derived columns using aggregate functions.
We cannot have any other columns that are not used as part of
GROUP BY
on derived column using non aggregate functions.We will not be able to use aggregate functions or aliases used in the select clause as part of the where clause.
If we want to filter based on aggregated results, then we can leverage
HAVING
on top ofGROUP BY
(specifyingWHERE
is not an option)
Typical query execution - FROM -> WHERE -> GROUP BY -> SELECT
%%sql
SELECT count(order_id) FROM orders
%%sql
SELECT count(DISTINCT order_date) FROM orders
%%sql
SELECT round(sum(order_item_subtotal), 2) AS order_revenue
FROM order_items
WHERE order_item_order_id = 2
%%sql
SELECT count(1)
FROM orders
WHERE order_status IN ('COMPLETE', 'CLOSED')
%%sql
SELECT order_date,
count(1)
FROM orders
GROUP BY order_date
%%sql
SELECT order_status,
count(1) AS status_count
FROM orders
GROUP BY order_status
%%sql
SELECT order_item_order_id,
round(sum(order_item_subtotal), 2) AS order_revenue
FROM order_items
GROUP BY order_item_order_id LIMIT 10
%%sql
SELECT o.order_date,
oi.order_item_product_id,
round(sum(oi.order_item_subtotal), 2) AS revenue
FROM orders o JOIN order_items oi
ON o.order_id = oi.order_item_order_id
WHERE o.order_status IN ('COMPLETE', 'CLOSED')
GROUP BY o.order_date,
oi.order_item_product_id
LIMIT 10
%%sql
SELECT o.order_date,
oi.order_item_product_id,
round(sum(oi.order_item_subtotal), 2) AS revenue
FROM orders o JOIN order_items oi
ON o.order_id = oi.order_item_order_id
WHERE o.order_status IN ('COMPLETE', 'CLOSED')
AND revenue >= 500
GROUP BY o.order_date,
oi.order_item_product_id
LIMIT 10
%%sql
SELECT o.order_date,
oi.order_item_product_id,
round(sum(oi.order_item_subtotal), 2) AS revenue
FROM orders o JOIN order_items oi
ON o.order_id = oi.order_item_order_id
WHERE o.order_status IN ('COMPLETE', 'CLOSED')
GROUP BY o.order_date,
oi.order_item_product_id
HAVING revenue >= 500
LIMIT 10
Using Spark SQL with Python or Scala
spark.sql("SELECT count(order_id) FROM orders").show()
spark.sql("SELECT count(DISTINCT order_date) FROM orders").show()
spark.sql("""
SELECT round(sum(order_item_subtotal), 2) AS order_revenue
FROM order_items
WHERE order_item_order_id = 2
""").show()
spark.sql("""
SELECT count(1)
FROM orders
WHERE order_status IN ('COMPLETE', 'CLOSED')
""").show()
spark.sql("""
SELECT order_date,
count(1)
FROM orders
GROUP BY order_date
""").show()
spark.sql("""
SELECT order_status,
count(1) AS status_count
FROM orders
GROUP BY order_status
""").show()
spark.sql("""
SELECT order_item_order_id,
round(sum(order_item_subtotal), 2) AS order_revenue
FROM order_items
GROUP BY order_item_order_id
""").show()
spark.sql("""
SELECT o.order_date,
oi.order_item_product_id,
round(sum(oi.order_item_subtotal), 2) AS revenue
FROM orders o JOIN order_items oi
ON o.order_id = oi.order_item_order_id
WHERE o.order_status IN ('COMPLETE', 'CLOSED')
GROUP BY o.order_date,
oi.order_item_product_id
""").show()
spark.sql("""
SELECT o.order_date,
oi.order_item_product_id,
round(sum(oi.order_item_subtotal), 2) AS revenue
FROM orders o JOIN order_items oi
ON o.order_id = oi.order_item_order_id
WHERE o.order_status IN ('COMPLETE', 'CLOSED')
AND revenue >= 500
GROUP BY o.order_date,
oi.order_item_product_id
""").show()
spark.sql("""
SELECT o.order_date,
oi.order_item_product_id,
round(sum(oi.order_item_subtotal), 2) AS revenue
FROM orders o JOIN order_items oi
ON o.order_id = oi.order_item_order_id
WHERE o.order_status IN ('COMPLETE', 'CLOSED')
GROUP BY o.order_date,
oi.order_item_product_id
HAVING revenue >= 500
""").show()