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SQL Tips by Donald Burleson

So far emphasis has been placed on how to query the tree structure in various encodings. The presentation layer often demands an ordered output. The difficulty here is that the end user is expected to specify some ordering criteria on runtime, and the order might be nonconforming to the order encoded in the tree structure. For example, consider a familiar hierarchy or employees with nested sets encoding:

The tree is effectively ordered by the LEFT column. A user might ask to display it locally, ordered by the employee names at each hierarchy level. It is easy to see that the global ordering criteria is essentially the path from the root to a node, which is made of concatenated names:

Unlike the materialized path, the path of concatenated names must be generated dynamically. The technical problem here is aggregating the names into a path string. Fortunately, in Chapter 3 the LIST aggregate function was explained. Therefore, the process continues by taking the familiar nested sets query, which returns a list of all node ancestors, and aggregating those lists into paths:

SELECT ii.left
       ,CONCAT_LIST(CAST( COLLECT('.'||i.ename) AS strings )) path
FROM   Employees i, Employees ii
where ii.left between i.left and i.right
group by ii.left; 

On a cautious note, this particular implementation of the LIST aggregate is agnostic of the order of the aggregation summands. The reader must double check that his favorite string concatenation method concatenates summands in the right order.

Ordering by dynamic path seems to be a natural solution until we are asked to order by numeric criteria, salary, for example. Numbers sorted as strings go in the wrong order. They have to be padded. The other concern is that of negative values. All these inconveniences make the dynamic path solution unsatisfactory.

An alternative solution is recreating the hierarchy dynamically with the structure, which is conforming to the required ordering. At first thought, recreating the hierarchy smells performance problems, but our rationale hinges upon a typical usage scenario. It must be the GUI that wants to display the ordered hierarchy, and GUI rendering capabilities impose common sense limits on the tree size. Even if the GUI were able to display the whole tree, an end user would be overwhelmed by the volume of information that he were presented ?at once?. It is fair to assume that a reasonably designed GUI would display only a local portion of the hierarchy, no matter how big the whole hierarchy might be.

Therefore, the issue of hierarchy ordering should be considered within the scope of application design. The application programmer designs a method of hierarchy navigation and display, which guarantees that the GUI displays only a small part of the hierarchy. A familiar example is an organizational chart where no more than two levels of the hierarchy are displayed at each tree node: employee subordinates, and his supervisor.  Ordering such a puny tree is a laughably easy exercise for a reader who has advanced thus far in this book.

An alternative design is a dynamic tree widget where each node exists in on of the two states: expanded or collapsed. The Windows file manager utility is a typical example. In principle, a directory tree could be expanded fully, but it is unreasonable to expect that there is a user who is up to the challenge of manually expanding a hierarchy of any significant size to the full depth.       

Exotic Labeling Schemas

The tree encoding area is flourishing with various methods. It is so easy to invent yet another tree labeling schema! This contrasts to general graphs, which appear to defy encoding ideas. This section reviews others, arguably, less practical tree encoding methods.

Dietz Encoding

One natural way to label a tree is the pre-order traversal as shown in Figure 5.10.

It is natural because the tree nodes are indexed in the depth-first order, and tree node records are in the depth-first order in a nearly ubiquitous tree display with the levels laid out horizontally:

The post-order traversal is a less intuitive way of navigating a tree. The nodes are visited in the same order, but the node's index number assignment is postponed until all of the node's children are indexed: 

The Dietz tree encoding assigns a pair of indexes: preorder#, postorder# to each node.

It is immediate that the Dietz tree encoding is volatile. Inserting a new node disrupts existing encodings, both pre-order and post-order.

Querying the Dietz encoded tree is based upon the following ancestor criteria. Node x is an ancestor of y if and only if x.preorder# ≤ y.preorder# and y.postorder# ≤ x.postorder#. This criteria appears to be identical to that of nested intervals, although unlike nested intervals neither preorder# < postorder#, nor preorder# < postorder# is universally true for all the nodes.

Figure 5.12 is a two-dimensional picture of Dietz encoding. It is assumed that preorder# is the horizontal axis, and postorder# is the vertical axis.

Each node x has its descendant nodes y bounded within the two-dimensional cone defined by the two inequalities x.preorder# ≤ y.preorder# and y.postorder# ≤ x.postorder#. For nested intervals we would additionally have preorder# < postorder# or in geometric terms have all the nodes above the diagonal preorder# = postorder#. If all the tree nodes are moved above the diagonal somehow, then the Dietz encoding would be transformed into nested intervals. Linear mapping

left = total#nodes - postorder# +1
right = 2 ?/span> total#nodes - preorder#

achieves that goal in Figure 5.13.

Pre-order - Depth Encoding

Yet another way to label the tree is storing the combination of the pre-order index number and level. Let's glance over the basic queries, though.

1      To find the node's parent, select all the nodes on the upper level, filter out all the nodes beneath, and choose the one with maximum preorder# among the rest. In the familiar example

59)  Smith's parent is searched for among the upper level nodes: Scott, Ford, Allen, Ward and Martin. Allen, Ward and Martin are rejected as they all have a preorder# greater than Smith. Among Scott and Ford the latter has a greater preorder#.

1        To find all the descendants, locate the next node on the same level and the next node on the parent level, choose the closest of the two, and select all the nodes between the given node and the chosen one.

60)  For Scott, pick up Ford and Blake, then select every node between Scott and Ford (exclusively).

1        Finding all the node's children is just filtering out of case #2 the nodes with the proper level.

2        Finding a path to the root is simply selecting all the predecessor nodes, grouping them by level, and extracting the maximum sequence numbers per each group.

This is, again, volatile encoding, which kills our further interest in detailed exploration.


This is an excerpt from the new book SQL Design Patterns: The Expert Guide to SQL Programming by Vadim Tropashko

You can buy it direct from the publisher for 30%-off.


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