Data structure virtualization is the constructing of a data structure utilizing pieces or fragments from other data structures. This article demonstrates how Data structure virtualization can be done in ANSI SQL utilizing hierarchical processing techniques to produce hierarchically structured virtualized output.
A recent article in this series of articles on ANSI SQL Hierarchical Processing described hierarchical data structure transformation in SQL. Data structuring transformation is the converting of a data structure into a different structure while retaining the original data unless it is not desired in the resulting structure. Data structure virtualization is the constructing of a data structure utilizing pieces or fragments from other data structures. This article will demonstrate how this can be done in ANSI SQL utilizing some of the hierarchical processing techniques covered in previous articles in this series used to produce hierarchically structured virtualized output.
Basic Hierarchical Structure Defining and Joining Review
In Figure 1 below, two structures are hierarchically combined using the Select statement shown. The basic structure definitions of views ABC and XYZ are modeled hierarchically using the hierarchically oriented LEFT Outer join operation, which preserves the left argument and not the right argument when there is no match found. In addition, the outer join uses the newer more specific ON clause at each join point to specify the node link points using join criteria. This can be seen in the ABC and XYZ view structures defined below. These two hierarchical structured views can also be joined using the LEFT Outer join operation as shown below. Normally the link point to the lower level structure is the root node as shown. The expanded ABC and XYZ views produce the internal LEFT Outer Join combined structure automatically. This combined view models the hierarchical structure shown below, which is processed by the SQL processor where the associated LEFT Outer Join semantics controls its hierarchical operation.
A Valid Semantics for Linking Below Root Review
In a previous article in this series, linking below the root was covered. This gives unlimited control of linking structures together. Using the query shown in Figure 2 below, the upper level structure is attached to the lower level structure at a node below the root node to the Z node. The data link points between the structures being combined are used to match up data relationships between the structures being combined. Linking below the lower level structure root node level requires that the lower level structure to be fully expanded before it can be joined to the upper level structure. This occurs automatically and can be seen in the combined view above in Figure 1. Because of the natural hierarchical nesting occurring in the expanded LEFT Outer Join, the two constructed structures have been kept separate. This is controlled by the final ON clause, which now performs the joining of the two fully expanded view structures, ABC over XYZ. This is actually a data filtering operation applied to the lower structure's link point (node Z in this case) and will filter the entire lower structure in isolation hierarchically from the link point outward in all path directions.
Because the lower level structure has its hierarchical structure established before the linking process it can be treated as a single fixed entity regardless of where it is linked or whether it is a logical or physical structure. Since node Z is already affected and controlled by other nodes above it (node X in this case), the hierarchical data modeling link point is always the root node of the lower structure. This is shown in the Modeled Structure of Figure 2 below while the data relationship data filtering occurs between nodes C and Z. This consistently preserves the data semantics of the structures involved and the hierarchically combined structure.
Also demonstrated in the example in Figure 2 is the powerful variable SQL SELECT list, which dynamically controls the range of processing necessary by specifying the specific data to be output. In this case it is data from nodes A, C and Y. Notice that node X on the pathway to selected node Y was not selected and this was automatically closed up by Node Promotion before output. This is a standard hierarchical operation. This node promotion operation also maps directly to the relational SELECT projection operation. This produces a nice natural hierarchically condensed result.
Extracting and Manipulating Multiple Fragments Review
The previous examples in this article only extracted a single data segment per data structure to contribute to the result structure being constructed from multiple structures. From a previous article in this series, the query in Figure 3 below demonstrates the technique for deriving and separately manipulating multiple fragments from a single structure to perform a data structure restructuring. It separates the original structure into two structures. One consisting of the Invoice node and the other consisting of the original structure minus the Invoice node and recombines them placing the Invoice node on top using an existing data relationship.
The Alias feature of SQL enables renaming a view or data structure so it can be separately identified by different names. This feature can be used to take apart a structure into multiple fragments and hierarchically reassembling them by manipulating each fragment separately. The separately identified fragments from the identified structure in the FROM statement above are identified by their correlation names of X and Y, which is used as a qualifier on data names. The LEFT Outer join in Figure 3 models the restructured data as Y over X since Y data is preserved when X data is not. This is why InvID is identified in the SELECT statement with a Y qualifier while EmpID, DndID and AddrID are identified with an X qualifier in the SELECT statement. This is because InvID is desired at a higher hierarchical level than the others. The higher level Y.InvID is used in the SELECT List and in the ON clause to reflect its proper position in the new hierarchy.
Virtualization Composed of Multiple Structures
For the following examples, the ABC view has been extended as shown below in Figure 4. In this example two structures are combined utilizing the LEFT Outer Join to hierarchically link below the lower level root in the query. This allows any-to any node linking between the structures. Only the desired data in the SELECT clause is output triggering node promotion. This aggregates the data nicely into a single condensed structure where nodes D and G are brought up and directly under node Z. This process is indicated below in Figure 4 where the left diagram shows how the structures are related. The middle diagram shows the modeled structure, the inactive pathways have dashed lines and unselected nodes have dashed boxes. The example on the right is the result structure. Also notice that node B was on an active path even though it was not selected for output. The path is active because it is needed to navigate to referenced node D.
Virtualization Manipulating Separate Structure Fragments
The query and example below in Figure 5 uses the same basic capabilities as the previous example, but in this example separate fragments of the lower structure ABC are separately moved to different locations under the XYZ structure. In this case, node D is placed under node Y while node G is placed under node Z. This is achieved by using the SQL Alias feature to enabled separate references to the ABC view structure using either ABC1 or ABC2 to qualify the different view references and their associated data parameters on the SELECT clause. This is indicated below with the two data fragments qualified using ABC1 and ABC2. Notice that each fragment is positioned separately by its own hierarchical LEFT Outer Join statement.
Association Table Usage
The previous data virtualization examples all relied on the use of existing data relationships in the data to recombine the data fragments. Without data relationships to use there is a problem for recombining the data fragments. An externally maintained relational association table of physically related sets of keys in rows can be used to make this association where there was no previous data relationships to use. This is shown below in Figure 6 using the association table shown.
The above SQL query in Figure 6 does not select any data from the association table making the association table operation seamless and transparent placing node C directly over node X in the output structure. These association tables can easily support many-to-many relationships which are modeled as one-to-many relationships when operating hierarchically in a top to bottom direction. The association table usage can also be easily reversed by swapping the association table key usage around. This places structure XYZ over structure ABC. This also produces a one-to many relationship and structure. Many-to-many relationships like Part and Supplier can contain intersecting data like Price for each Part and Supplier combination, which can be easily selected for output and automatically placed in a intersecting data node between nodes C and X by selecting Price for output.
Data virtualization is the ability to control, isolate and combine fragments from multiple data structures into a single meaningful structure. The data virtualization of data structures shown in this article utilized the capabilities used in hierarchically joining structures below their lower level root, selecting only the data desired for output, and the node promotion resulting from the eliminated nodes of the structures. The technique for isolating multiple segments and their separate manipulation was also demonstrated.
The SQL hierarchical examples and capabilities shown and demonstrated in this article can be hierarchically tested with XML using my company's online interactive ANSI SQL Transparent XML Hierarchical Processor Prototype at www.adatinc.com/demo.html.