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As you can see, even though the tree is only three levels deep, some of the HIERARCHYID values became quite long because of the insertion order of children. The solution that normalizes the values involves the following steps: 1. De ne a CTE called EmpsRN that calculates for each node a row number, partitioned by parent and ordered by current hid value. 2. De ne a recursive CTE called EmpPaths that iterates through the levels of the tree, starting with the root node and proceeding to the next level of children in each iteration. Use this CTE to construct a new canonical path for the nodes. The root should be assigned the path /, and for each node in the next level the path is obtained by concatenating the parent s path, the current node s row number from the previous step, and another / character. 3. Join the Employees table with the EmpPaths CTE and update the existing hid values with new ones converted from the canonical paths generated in the previous step. 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Note that the model allows you to choose in which order you assign values to siblings. In this particular case, I chose to traverse siblings by employee name order. You start with the root, traversing the tree counterclockwise. Every time you enter a node, you increment a counter and set it as the node's left value. Every time you leave a node, you increment the counter and set it as the node's right value. This algorithm can be implemented to the letter as an iterative/recursive routine that assigns each node with left and right values. However, such an implementation requires traversing the tree a node at a time, which can be very slow. I'll show an algorithm that traverses the tree a level at a time, which is faster. The core algorithm is based on logic I discussed earlier in the chapter, traversing the tree a level at a time and calculating binary sort paths. To understand this algorithm, it will help to examine Figure 9-6. 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