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This approach involves using SQL JOIN operations to merge relevant data from the Users and Orders tables. We focus on selecting only those orders where order_date falls in the year 2019, and then count the occurrences for each user.
The time complexity is approximately O(n + m), where n is the number of users and m is the number of orders in 2019. The space complexity is O(n) for storing the result set.
1SELECT u.user_id AS buyer_id, u.join_date, COUNT(o.order_id) AS orders_in_2019 FROM Users u LEFT JOIN (SELECT buyer_id, order_id FROM Orders WHERE YEAR(order_date) = 2019) o ON u.user_id = o.buyer_id GROUP BY u.user_id, u.join_date;We're leveraging an SQL LEFT JOIN to ensure that all users appear in the result, even if they haven't made any orders. We join Users with a filtered subquery of Orders (where the year is 2019), count the number of orders for each user, and group the results by user ID.
This approach is more general-purpose and involves looping through the datasets of Users and Orders, filtering appropriate records, and calculating the desired order count for each user manually without relying on direct SQL tools.
The time complexity is approximately O(n + m), given that we iterate through each collection separately. The process involves two main passes: one for collating the order data and another for joining this data with user data. The space complexity is O(m), where we preserve order counts in a map.
1import java.util.*;
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This Java implementation traverses the list of orders, filtering by date to the year 2019 and populating a map with counts of orders per buyer. It then iterates over users, matching and appending their order count data.