tasks/online-compute/FlinkSQL/flinksql_004_en/task.md

metadata

id: online-compute_FlinkSQL_flinksql_004
name: Dual Window Aggregation (Tumble + Hop)
category: online-compute/FlinkSQL
timeout_seconds: 600
modality: pure-text
engine: flink-sql

Prompt

I need you to write a Flink SQL that uses a datagen built-in table to generate order events, and computes the sales volume and order count per product using both a tumbling window and a sliding window, outputting the results to two separate console tables.

Business Background and Objective: Generate order events (order_id BIGINT, product_id INT, quantity INT) using a datagen built-in table. Use LOCALTIMESTAMP as the event time and set a 5-second watermark delay.

Compute statistics using two window types:

1. 1-minute tumbling window: Compute the sales volume SUM(quantity) AS sales and order count COUNT(*) AS order_cnt per product.
2. Sliding window (slides every 30 seconds, covering the past 1 minute): Compute the sales volume SUM(quantity) AS sales and order count COUNT(*) AS order_cnt per product.

Print the results of both window types to two separate console tables, with each record carrying a window type identifier window_type.

Source Table Definition:

• orders_source (order event stream, datagen connector):
  • order_id BIGINT: Order ID, randomly generated in the range 1–1,000,000
  • product_id INT: Product ID, randomly generated in the range 1–10,000
  • quantity INT: Quantity, randomly generated in the range 1–10
  • event_time: Uses LOCALTIMESTAMP to generate event time, with a WATERMARK delay of 5 seconds
  • Generation rate: rows-per-second = 50

Output Table Definitions:

• console_tumble (print connector, tumbling window results):

  • window_type STRING: Window type identifier, fixed as 'TUMBLE'
  • window_start TIMESTAMP(3): Window start time
  • window_end TIMESTAMP(3): Window end time
  • product_id INT: Product ID
  • sales INT: Sales volume of the product within the window
  • order_cnt BIGINT: Order count of the product within the window

• console_hop (print connector, sliding window results):

  • window_type STRING: Window type identifier, fixed as 'HOP'
  • window_start TIMESTAMP(3): Window start time
  • window_end TIMESTAMP(3): Window end time
  • product_id INT: Product ID
  • sales INT: Sales volume of the product within the window
  • order_cnt BIGINT: Order count of the product within the window

Aggregation Logic:

• Tumbling window: GROUP BY TUMBLE(event_time, INTERVAL '1' MINUTE), product_id
• Sliding window: GROUP BY HOP(event_time, INTERVAL '30' SECOND, INTERVAL '1' MINUTE), product_id
• Aggregation metrics: SUM(quantity) AS sales, COUNT(*) AS order_cnt
• Both window times and the window type must be explicitly output

Output Requirements:

• Use two INSERT INTO statements to output to console_tumble and console_hop respectively.
• Output field order: window_type, window_start, window_end, product_id, sales, order_cnt