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Here is how the transaction log works, in simplified form. When a user executes a SQL statement that modifies the database, the DBMS automatically writes a record in the transaction log showing two copies of each row affected by the statement. One copy shows the row before the change, and the other copy shows the row after the change. Only after the log is written does the DBMS actually modify the row on the disk. If the user subsequently executes a COMMIT statement, the end-of-transaction is noted in the transaction log. If the user executes a ROLLBACK statement, the DBMS examines the log to find the "before" images of the rows that have been modified since the transaction began. Using these images, the DBMS restores the rows to their earlier state, effectively backing out all changes to the database that were made during the transaction. If a system failure occurs, the system operator typically recovers the database by running a special recovery utility supplied with the DBMS. The recovery utility examines the end of the transaction log, looking for transactions that were not committed before the failure. The utility rolls back each of these incomplete transactions, so that only committed transactions are reflected in the database; transactions in process at the time of the failure have been rolled back. The use of a transaction log obviously imposes an overhead on updates to the database. In practice, the mainstream commercial DBMS products use much more sophisticated logging techniques than the simple scheme described here to minimize this overhead. In addition, the transaction log is usually stored on a fast disk drive, different from the one that

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When a dimension table contains a type 1 attribute that is not fully dependent on the natural key, simple comparison to the existing dimension record is not sufficient to determine if an update is needed. For example, suppose the table in Figure 17-3 also contains a brand_name column. Brand_name is determined by brand_code. It is therefore only partially dependent on the product s natural key. Suppose that brand_name is designated as a type 1 attribute. If the brand_name of an existing product changes, this may or may not call for an overwrite. It is necessary to determine if the brand_code, a type 2 attribute, has also changed. If so, the product has been assigned a new brand, and a new row is required. There has been no type 1 change. If the brand_code has not changed, however, the new brand_name is indicative of a type 1 change. It is important that dimensional designers be cognizant of the increased ETL complexity that will be necessitated by attributes like this. In order to avoid this situation, designers may choose to forgo the use of type 1 attributes that do not correspond to the grain of the dimension. As noted in s 3 and 15, type 1 attributes can also increase the complexity involved in maintaining aggregates or cubes. If the use of such an attribute is necessary, design documentation must carefully spell out the dependency of the attribute so that ETL developers can process it correctly. Notice that in a snowflake design, the ETL challenge is slightly mitigated because the attributes of brand will be in a separate table.

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lers for this purpose. The important benefits of using RAID technology are that it can increase performance, provide for fault tolerance, and simplify administration. In this section, we look at each of these benefits in turn and determine how they pertain to the design of your SQL Server storage configuration. We ve already covered fault tolerance: When redundancy is built into a disk array, the failure of a single disk will not cause a loss of data. Although there may be a decrease in performance and a decrease in usable disk space, certain types of RAID solutions will allow you to quickly and easily replace a failed disk without stopping network service. This increases uptime and, ultimately, ensures that your users can work uninterrupted in the event of a failure. When data is retrieved from multiple physical disks in a disk subsystem, the results are faster data access and increased throughput both of which are benefits that can greatly increase database performance. Finally, as we ll see in the next section, configuring files and filegroups to specifically store information across physical devices involves extensive planning and understanding of the underlying data structure. For most environments, however, the costs associated with purchasing RAID solutions for database servers is a good investment.

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After type 1 changes have been identified and applied, the source record must be checked for type 2 changes In Figure 17-2, this takes place in step 17 This task can be accomplished by a third lookup, using the combination of the natural key and all type 2 attributes If a match is found in the dimension table, the incoming record is properly represented and processing is complete If it is not found, a type 2 change has occurred The prior record is no longer current, so step 18 is used to update its current_version to Not Current The new version is assigned a surrogate key (step 19) and is loaded into the warehouse table (step 110) TIP Type 2 changes can be identified by comparing a source record to dimension records that have the same natural key.

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NOTE: In 5, we discussed the issues related to using write-caching disk controllers. In some cases, a failure of a RAID controller or an unexpected restart of the server (due to, for example, a loss of power) can cause database files to be left in an inconsistent state. Be sure to review that information before implementing a hardware-based RAID solution.

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