Voici le premier chapitre d'un long article concernant le SQL et son implémentation dans les SGBD les plus courants (Paradox, Access, Oracle, SQL Server, Sybase). Introduction Most users at one time or another have dealt with hierarchical data in a SQL database and no doubt learned that the management of hierarchical data is.

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Managing Hierarchical Data in My. SQL — Mike Hillyer's Personal Webspace. Introduction. Most users at one time or another have dealt with hierarchical data in a SQL database and no doubt learned that the management of hierarchical data is not what a relational database is intended for. The tables of a relational database are not hierarchical (like XML), but are simply a flat list. Hierarchical data has a parent- child relationship that is not naturally represented in a relational database table. For our purposes, hierarchical data is a collection of data where each item has a single parent and zero or more children (with the exception of the root item, which has no parent).

Hierarchical data can be found in a variety of database applications, including forum and mailing list threads, business organization charts, content management categories, and product categories. For our purposes we will use the following product category hierarchy from an fictional electronics store: These categories form a hierarchy in much the same way as the other examples cited above. Harry Potter And The Prisoner Of Azkaban Extended Edition Subtitles.

In this article we will examine two models for dealing with hierarchical data in My. SQL, starting with the traditional adjacency list model.

The Adjacency List Model. Typically the example categories shown above will be stored in a table like the following (I’m including full CREATE and INSERT statements so you can follow along): CREATE TABLE category(.

The topmost element, in this case electronics, has a NULL value for its parent. The adjacency list model has the advantage of being quite simple, it is easy to see that. FLASH is a child ofmp. While the adjacency list model can be dealt with fairly easily in client- side code, working with the model can be more problematic in pure SQL. Retrieving a Full Tree. The first common task when dealing with hierarchical data is the display of the entire tree, usually with some form of indentation.

The most common way of doing this is in pure SQL is through the use of a self- join: SELECT t. AS lev. 1, t. 2. name as lev. FROM category AS t.

LEFT JOIN category AS t. ON t. 2. parent = t. Before being able to see the full path of a category we have to know the level at which it resides. In addition, special care must be taken when deleting nodes because of the potential for orphaning an entire sub- tree in the process (delete the portable electronics category and all of its children are orphaned). Some of these limitations can be addressed through the use of client- side code or stored procedures. E Tax Return 2012 Download. With a procedural language we can start at the bottom of the tree and iterate upwards to return the full tree or a single path.

We can also use procedural programming to delete nodes without orphaning entire sub- trees by promoting one child element and re- ordering the remaining children to point to the new parent. The Nested Set Model. What I would like to focus on in this article is a different approach, commonly referred to as the Nested Set Model. In the Nested Set Model, we can look at our hierarchy in a new way, not as nodes and lines, but as nested containers. Try picturing our electronics categories this way: Notice how our hierarchy is still maintained, as parent categories envelop their children.

We represent this form of hierarchy in a table through the use of left and right values to represent the nesting of our nodes: CREATE TABLE nested? We start numbering at the leftmost side of the outer node and continue to the right: This design can be applied to a typical tree as well: When working with a tree, we work from left to right, one layer at a time, descending to each node’s children before assigning a right- hand number and moving on to the right. This approach is called the modified preorder tree traversal algorithm. Retrieving a Full Tree. We can retrieve the full tree through the use of a self- join that links parents with nodes on the basis that a node’s lft value will always appear between its parent’s lft and rgt values: SELECT node.

FROM nested. We do not concern ourselves with the rgt value of the node in our BETWEEN clause because the rgt value will always fall within the same parent as the lft values. Finding all the Leaf Nodes. Finding all leaf nodes in the nested set model even simpler than the LEFT JOIN method used in the adjacency list model. If you look at the nested.

To find the leaf nodes, we look for nodes where rgt = lft + 1: SELECT name. FROM nested? This can be done by adding a COUNT function and a GROUP BY clause to our existing query for showing the entire tree: SELECT node. COUNT(parent. name) - 1) AS depth. FROM nested. Web developers could loop through the tree, adding < li> < /li> and < ul> < /ul> tags as the depth number increases and decreases. Depth of a Sub- Tree.

When we need depth information for a sub- tree, we cannot limit either the node or parent tables in our self- join because it will corrupt our results. Instead, we add a third self- join, along with a sub- query to determine the depth that will be the new starting point for our sub- tree: SELECT node. COUNT(parent. name) - (sub. The depth values are always relative to the named node. Find the Immediate Subordinates of a Node.

Imagine you are showing a category of electronics products on a retailer web site. When a user clicks on a category, you would want to show the products of that category, as well as list its immediate sub- categories, but not the entire tree of categories beneath it. For this, we need to show the node and its immediate sub- nodes, but no further down the tree. For example, when showing the PORTABLE ELECTRONICS category, we will want to show MP3 PLAYERS, CD PLAYERS, and 2 WAY RADIOS, but not FLASH. This can be easily accomplished by adding a HAVING clause to our previous query: SELECT node.

COUNT(parent. name) - (sub! As you can see, there is a count for each category and the count of subcategories is reflected in the parent categories. Adding New Nodes. Now that we have learned how to query our tree, we should take a look at how to update our tree by adding a new node. Let’s look at our nested set diagram again: If we wanted to add a new node between the TELEVISIONS and PORTABLE ELECTRONICS nodes, the new node would have lft and rgt values of 1. We would then add the new node with the appropriate lft and rgt values. While this can be done with a stored procedure in My.

SQL 5, I will assume for the moment that most readers are using 4. I will isolate my queries with a LOCK TABLES statement instead: LOCK TABLE nested. Let’s add a new FRS node below the 2 WAY RADIOS node: LOCK TABLE nested. As you can see, our new node is now properly nested: SELECT CONCAT( REPEAT( ' ', (COUNT(parent. AS name. FROM nested. The course of action you take when deleting a node depends on the node’s position in the hierarchy; deleting leaf nodes is easier than deleting nodes with children because we have to handle the orphaned nodes.

When deleting a leaf node, the process if just the opposite of adding a new node, we delete the node and its width from every node to its right: LOCK TABLE nested. In some cases you may wish to just change the name to a placeholder until a replacement is presented, such as when a supervisor is fired. In other cases, the child nodes should all be moved up to the level of the deleted parent: LOCK TABLE nested. Once again, we can confirm our elements have been promoted: SELECT CONCAT( REPEAT( ' ', (COUNT(parent. AS name. FROM nested. In my opinion the most comprehensive source of information on managing hierarchical information is a book called Joe Celko’s Trees and Hierarchies in SQL for Smarties, written by a very respected author in the field of advanced SQL, Joe Celko.

Joe Celko is often credited with the nested sets model and is by far the most prolific author on the subject. I have found Celko’s book to be an invaluable resource in my own studies and highly recommend it.