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message_id) The underlying assumption is that something is creating these unique IDs This
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capacity is built in to some databases; others require that you write a trigger to assign the ID when a new row is inserted Either way, the ID usually comes from a sequence produced within the database, thereby guaranteeing uniqueness Great! We're done! Wait a second, hold the phone! We're not leveraging the power of our RDBMS What fools these mortals be! What we have from the previous example is great as long as the database doesn't have to be able to answer the request "list the e-mail messages I've received" n essential request in any email system Databases are smart, but they can't tell you what they don't know If you want your relational database to answer requests like this o "know" about your data instead of simply storing it ou need to tell it about your data in the first place In XML terminology, you need to decompose the XML into your relational tables
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SQL and XML: The Joys of Partial Decomposition
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XML is great at structuring data Relational databases are great at storing and relating data How do you get the best out of these two different ways of looking at data You can create a table that contains a row for each part of your XML document For example, take the following XML fragment:
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The <b>quick</b> brown <i>fox</i> jumped over the <u>lazy</u> dog
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Some database packages implement a "persistent parse tree" database schema to store XML What this means is that the entire XML document is decomposed, element by element, into individual nodes (see Figure 2-2 from 2), and each node is stored as a separate row in a database table, a table not specific to any particular XML language but able to accept any XML data With this scheme, you need up to 13 rows of a table just to represent the preceding line of XML code Each node of the tree is put in its own row, with relations built to the other nodes to reflect the original structure and order of the XML The original XML document is essentially disintegrated, broken into its constituent parts and stored atomically Yes, you could answer the question "Which words in all of my documents are bold " But in order to do so, your database has to select across all these rows In addition, such database schemas don't lend themselves well to indexing And what about relational integrity In the following fragment, how do you relate the reference to Charade back to a relational table of movie titles kept elsewhere in your database
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<movie id="3234">Charade</movie> is one of Cary Grant's later and least well known films
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Another method for integrating XML with a SQL database is XML decomposition An XML decomposition database schema takes the opposite approach to persistent parse trees by creating a schema that entirely encompasses your XML documents Every element, as well as every single attribute of each element, has a separate table Rigid relational integrity is maintained, but at the price of flexibility Also, unstructured data (such as a flow of text with tags embedded at various points ike our CyberCinema reviews) is very difficult to support With full decomposition, you throw away the XML when you decompose it into the SQL database because you don't need it any more You've fully encoded your information in the database, and you should be able to recreate your XML instance if needed from that stored information However, XML schema evolution (such as adding to your DTD) means SQL schema evolution, which can then lead to changing application code Therefore decomposition is useful only when you have a rigidly defined XML schema that you know will never change, which is never Both persistent parse trees and decomposition are misguided attempts to integrate XML and SQL tightly Enter Partial Decomposition
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In contrast to the methods for XML/SQL integration described previously, partial decomposition is a simple method that can help manage some of the complexity inherent in a mixed XML/SQL-based system The components of partial decomposition are the following:
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Store the entire XML instance in the database Keep the XML instance as the primary source of data Extract selected data from the XML, and place it in relational tables for quick access and relational constraint management
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Partial decomposition really struts its stuff in the application of these rules With partial decomposition, we take a different approach: Let XML do what it's good at (structuring content), and let SQL do what it's good at (enforcing relational constraints and organizing data) Partial decomposition is a loose integration of XML and SQL You can still use persistent parse trees with partial decomposition In fact, it doesn't matter how you store the XML itself The key to partial decomposition is figuring out what this selected data that you want to store in the database is The selected data depends on the purpose of the application and the data Let's go back to two strengths of SQL databases Enforcing Relational Constraints With e-mail messages, you want your database to ensure that the To and From fields refer to real users Relational databases can do this for you if you design your partial decomposition schema to include a user table and constraints that the To and From fields must refer to rows in this user table Hence, we need To and From fields (that is, columns) in our e-mail table Organizing Data for Easy Recall Organizing data really means answering questions about your data The question you need to ask yourself is "What questions is my application going to ask the database " In this case, we want to be able to answer the request: "List the e-mail messages a particular user has received" The To and From columns already solve this problem for e-mail messages We end up with the following schema, represented by SQL create statements:
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CREATE TABLE message ( message_id INTEGER(16) NOT NULL PRIMARY KEY,
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message_xml message_from NOT NULL, message_to NOT NULL, ); CREATE TABLE user ( user_id user_name );
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CLOB INTEGER(16) INTEGER(16)
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NOT NULL, REFERENCES user(user_id) REFERENCES user(user_id)
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INTEGER(16) VARCHAR(64)
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