Join orders and events to find engagement score

Question

Write a query joining orders and rides to produce the combined engagement score.

Codemia · Accepted Answer

To solve the problem of calculating a combined engagement score from orders and rides data, we need to identify the relevant data sources. We will be working with two tables: `orders` and `rides`. The `orders` table contains information about customer orders, including a unique order ID, customer ID, and any relevant timestamps. The `rides` table contains details about rides taken by customers, including a unique ride ID, customer ID, and timestamps for when the rides occurred. Our goal is to join these tables based on the customer ID, calculate an engagement score based on the number of orders and rides, and return the customer ID along with their calculated engagement score.

1. **Identify Join Condition**: The primary key for joining the two tables will be the `customer_id` present in both `orders` and `rides` tables.
2. **Determine Engagement Score Calculation**: Define how engagement score is calculated. For instance, it could be the total number of orders plus the number of rides for each customer.
3. **Write the Base Query**: Start with a simple SQL query to perform the join and select the required fields.
4. **Aggregate Data**: Use `GROUP BY` to aggregate the total orders and rides for each customer, calculating the engagement score.
5. **Handle NULLs**: Ensure that we handle cases where there might be no orders or rides for a customer.

Here is the SQL query that accomplishes the task:
```sql
SELECT o.customer_id,
       COALESCE(COUNT(DISTINCT o.order_id), 0) AS total_orders,
       COALESCE(COUNT(DISTINCT r.ride_id), 0) AS total_rides,
       COALESCE(COUNT(DISTINCT o.order_id), 0) + COALESCE(COUNT(DISTINCT r.ride_id), 0) AS engagement_score
FROM orders o
LEFT JOIN rides r ON o.customer_id = r.customer_id
GROUP BY o.customer_id;
``` 
In this query, we use a LEFT JOIN to ensure all customers from the `orders` table are included, even those without rides. The `COALESCE` function is used to handle NULL values, providing a default of 0 for customers without orders or rides.

For large datasets, consider the following optimizations:
1. **Indexes**: Ensure that both `orders` and `rides` tables have indexes on `customer_id` to speed up join operations.
2. **Partitioning**: If possible, partition the tables based on date or customer segments to reduce the amount of data processed during the join.
3. **Materialized Views**: For frequently accessed data, consider creating a materialized view that pre-computes the engagement scores to improve query performance.
4. **Limit Data Scanned**: If applicable, filter data before the join to limit the total number of rows processed.

1. **NULL Values**: Ensure that customer IDs in both tables are not NULL, as this would affect the join. Use filters in the WHERE clause if necessary.
2. **Duplicates**: Handle duplicate entries in either the `orders` or `rides` tables that might distort the count. Use `COUNT(DISTINCT ...)` in the aggregation to avoid counting duplicates.
3. **Customers with No Activity**: Customers with no orders or rides should still appear in the result with an engagement score of 0, which is handled by the LEFT JOIN and COALESCE functions.
4. **Data Types**: Verify that the data types for the join key (`customer_id`) are the same in both tables to prevent unexpected behavior during the join.

Join orders and events to find engagement score

Bloomberg

What the Interviewer Expects

Key Topics to Cover

How to Approach This

Possible Follow-up Questions

Sharpen Your Skills on Codemia

Sample Answer

Problem Understanding

Approach

Submit Your Answer

Bloomberg Data Scientist Interview Guide

Related Questions

Optimize a slow query on products

Calculate conversion rate per user from orders

Window function: rank over sessions

Optimize a slow query on orders

Write a query to find time to first action from sessions