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PRIMARY KEY,
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First, for our e-mail messages we're building a simple table that has a unique numerical key (which is important) and stores the whole of each XML instance in a CLOB field The other two columns in the e-mail message table, To and From, are numerical and refer to rows in the other table we're creating user table
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In using CLOBs, it is often desirable (depending on your database server) to isolate the CLOBs into their own table for performance reasons Applying this to the previous example, we get:
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CREATE TABLE message_content { message_id message_xml } CREATE TABLE message { message_id NOT NULL, message_to NOT NULL } INTEGER(16) REFERENCES user(user_id) INTEGER(16) PRIMARY KEY NOT NULL, REFERENCES user(user_id) message_from INTEGER(16) INTEGER(16) CLOB REFERENCES NOT NULL message(message_id) NOT NULL,
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In this case, we've stored everything about the message in the message table itself, but we put the content of the message to the side in a message_content table This approach is sometimes faster; the DB engine can cache more of the message_info table because it doesn't have to store the CLOBs in memory with the other data Hence, queries against the message_info table are faster than queries against your combined table In practice, a welltuned application would not even load the XML into memory until required The efficacy of all this depends on your database vendor and the application logic that you're connecting to it A skilled database administrator can help you determine which approach will make the most sense for your data and your application The mechanism of partial decomposition now kicks in: Whenever you insert a new e-mail message into the message table or update a message already there, you have to examine the XML instance, decompose the information you're interested in, and insert or update the corresponding rows in your relational schema This can happen in your application code as part of a transaction or (in databases that allow for complex coding within the database itself) as a trigger on the table If you violate an integrity constraint (for example, your e-mail message XML instance contains a From field with a user ID that doesn't correspond to a user in the relational user table), you roll back the whole transaction and kick back an error Relational integrity is maintained If you want a list of a user's email messages, a simple SQL query is all that is required:
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select * from message where message_to = 3
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To read the actual message, you pull out the XML and transform it as necessary for the display medium you're using Using Partial Decomposition as a Caching Strategy Another way to use the tool of partial decomposition is to extract bits of data you want close at hand in your relational table For instance, it is preferable to get the subject line of each message from the database, instead of having to retrieve each XML instance (that is, the e-mail message), parse it, and pull out each subject every time you have to display a list of messages Add the message subject line to our relational schema, like so:
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CREATE TABLE message ( message_id message_xml INTEGER(16) CLOB NOT NULL NOT NULL, PRIMARY KEY,
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message_subject VARCHAR(255) message_from NOT NULL, message_to NOT NULL, ); INTEGER(16) INTEGER(16)
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NOT NULL, REFERENCES user(user_id) REFERENCES user(user_id)
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In addition, adding subject line decomposition to our partial decomposition, we can do just that
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Whenever you have data mirrored in two places at once, the data could become corrupt, that is, the data in your relational table might not match the data in your XML file This can happen for any number of reasons; for instance, the routines you're using for partial decomposition could fail halfway through, leaving your data only partially decomposed Following are some strategies for keeping your data in sync:
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Remember to keep XML in the driver's seat Don't be tempted to update the rows of your table directly from your application code Let your partial decomposition routines continue to do this based on your XML By compartmentalizing the population of these tables in one segment of your code, the decomposition, you ensure that the data in the tables is always a mirror of the data in the XML itself The system is also easier to maintain; when you want to add something to your decomposition, all the code is in one place (either as a part of your application code or in the database itself)
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Use database transactions If your code first inserts or updates an XML instance in your database and then inserts or updates rows associated with this XML instance, make this part of one database transaction You can start a transaction, do all the inserts and updates you want, and then end the transaction, committing all changes at once That way, if anything goes wrong halfway through, the database automatically rolls back all your changes and kicks back an exception, which you can catch and do something intelligent with (such as writing an error log that can alert support staff of a potential problem before your users start complaining about it)
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Build data management utilities You should have a way to rebuild all the partial decomposition information from your XML, and you should do this on a regular basis (depending on how much data you have) Rebuilding the partial decomposition information from your XML continually maintains consistent data and alerts you to potential problems before they balloon into big cleanup jobs Your rebuild should be resilient enough to flag errors and then move on so that you can run it overnight and fix problems in the morning You also should build small utilities for bulk deletes and bulk updates
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In general, think of the data stored in the partial decomposition tables as an index, not as the actual data The data really is stored in only one place: the XML Problems with Partial Decomposition Partial decomposition is especially geared toward write-sometimes-read-mostly applications Whenever you make changes to your data, the data has to go through the partial decomposition step, which (depending on the complexity of your data) can be costly in terms of performance If you made only one change, it's also wasteful because all the characteristics from your XML instance are decomposed each time you change it Document management systems, such as the one we're describing in the CyberCinema example, are particularly suited to this approach because once a document is published, it basically sits there and is read repeatedly Another problem with partial decomposition is with schema evolution (using the word "schema" in a general sense nything relating to changing your data model) If your data must be changed and, therefore, your DTD must change, your schema for partial decomposition may also have to change The good news is that it doesn't always have to change, only when you want to decompose new elements And even then, it's an additive process It's important to keep these issues in mind when you're analyzing the performance characteristics of your application Of course, the subject of application performance is a more complex issue, and entire books are dedicated to the subject of performance analysis and writing "performant" code (like "performant" is a real word hey're not fooling anyone)
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