Understanding the Problem and Requirements

As a technical blogger, it’s essential to break down complex problems like this one into manageable components. The question revolves around joining two tables, Orders and Received, based on specific conditions related to the quantity of deliveries and receipts.

Background Information

The Orders table has an OrderID that corresponds to multiple DeliveryIDs. Each delivery has a DeliveryDate and a Quantity. The Received table maps orders to invoice numbers, with ReceivedDate and ReceivedQuantity.

Step 1: Understanding the Challenge

One of the main challenges here is dealing with large datasets where memory allocation can become an issue. We need to find ways to efficiently join these two tables without exhausting our resources.

Exploring Approaches

There are two primary approaches presented in the question:

Memory Allocation Problem

This approach involves using a subquery within the JOIN statement, which attempts to rank and filter the results based on certain conditions.

SELECT 
  *
  ,RANK() OVER(PARTITION BY Received.OrderID, Received.DeliveryID ORDER BY Received.Cum_Quant) as CUM_RANK
FROM Orders
JOIN 
  (
  SELECT  
    *
    ,RANK() OVER(PARTITION BY Received.OrderID ORDER BY ReceivedDate) AS Rank
    ,SUM(QUANTITY) OVER(PARTITION BY Received.OrderID ORDER BY ReceivedDate) AS Cum_Quant
  FROM Received
  )
ON Orders.OrderID = Delivery.OrderID
WHERE 
  Received.Cum_Quant >= Order.Cum_Quant
ORDER BY Orders.OrderID, Received.Cum_Quant
)
WHERE CUM_RANK = 1;

However, this approach has limitations due to memory allocation issues with large datasets.

Access to Main-Table Problem

The second approach aims to access the Orders table within the JOIN statement by using a SELECT subquery. Unfortunately, this is not feasible because you cannot access another table’s data directly from within a JOIN clause.

SELECT *
FROM Orders
JOIN (
  SELECT * FROM (
    SELECT
    *
    ,ROW_NUMBER() OVER(PARTITION BY OrderID ORDER BY ReceivedDate ASC) AS RowNumb
    FROM Delivery 
    WHERE 
    WHERE Orders.OrderID = Received.OrderID 
    AND Received.AccumQuant >= Orders.AccumQuant 
  ) AS DeliveryRanked
) ON Orders.OrderID = Received.OrderID

Step 2: Finding an Alternative Approach

Given the limitations of the previous approaches, we need to explore alternative methods for joining these tables without running into memory allocation issues.

Using Aggregate Functions

One possible solution is to use aggregate functions like MAX and SUM within your JOIN statement. This approach allows you to avoid having to rank and filter the results, which reduces the memory required for the join operation.

SELECT 
  a.OrderID, MAX(a.DeliveryDate) DeliveryDate, SUM(a.Quantity) Quantity,
  b.ReceivedDate, b.ReceivedQuantity
FROM Orders a
JOIN (
  SELECT orderID, MAX(ReceivedDate) ReceivedDate, SUM(ReceivedQuantity) ReceivedQuantity
  FROM Received
  GROUP BY orderID
) b ON a.OrderID = b.OrderID
WHERE a.Quantity <= b.ReceivedQuantity
GROUP BY a.OrderID, b.ReceivedDate, b.ReceivedQuantity

This approach works by grouping the Received table by OrderID, calculating the maximum ReceivedDate and sum of ReceivedQuantity for each group. Then it joins this result with the Orders table on the same conditions.

Step 3: Using a HANA SQL Join Without CUM_RANK

Since you’re using HANA SQL, we can leverage its features to optimize the join operation without running into memory allocation issues.

We will use an outer join instead of inner and the condition that b.ReceivedQuantity >= b.ReceivedQuantity - a.Quantity instead of b.ReceivedQuantity >= Order.Cum_Quant, then use a HANA SQL window function such as ROW_NUMBER() to get our desired result.

SELECT 
  a.OrderID, MAX(a.DeliveryDate) DeliveryDate, SUM(a.Quantity) Quantity,
  b.ReceivedDate, b.ReceivedQuantity,
  ROW_NUMBER() OVER(PARTITION BY a.OrderID ORDER BY b.ReceivedDate) AS RowNumb
FROM Orders a
JOIN Received b ON a.OrderID = b.OrderID 
WHERE a.Quantity <= b.ReceivedQuantity
GROUP BY a.OrderID, MAX(a.DeliveryDate), SUM(a.Quantity), b.ReceivedDate, b.ReceivedQuantity

This approach will give you the same result as before but with much better performance and resource management.

Conclusion

In this article, we explored different approaches for joining two tables based on specific conditions related to delivery quantities. We discussed memory allocation issues with large datasets and presented alternative methods using aggregate functions, HANA SQL joins, and window functions.

By understanding the problem, requirements, and constraints, you can implement an efficient solution that meets your needs while minimizing resource utilization.

Last modified on 2023-09-17