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Before an aggregate can be used, it must be built. As with the derived tables of 14, this is the price we must pay for increased performance. Designed around the principle of performance, it makes sense to source aggregate tables from the original star. The occurrence of a type 1 change can make it quite difficult to maintain aggregates incrementally, leading many developers to adopt a drop-and-rebuild approach.
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Part V
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PART V
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The Source of an Aggregate
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By allowing the concept of conformance to guide aggregate design, we guarantee consistent results from the summary, while keeping the process of rewriting queries as simple as possible. This same guideline has one additional advantage: it helps keep the ETL process simple as well. Construction of conformed dimensions was discussed in 5. If a dimension table is shared by the base star and aggregate, only one version exists; no additional work is required at ETL time. If the aggregate uses an identical copy of the original dimension, this can be handled via simple replication. In the case of a conformed rollup, such as the month table in Figure 15-1, some additional processing will be needed. The rollup will summarize attributes of the base dimension table, but it has its own surrogate key. As you learned in 5, the best way to load a conformed rollup is to use the base dimension table as its source. This eliminates any redundant processing that would be required if the rollup dimension were loaded from the original source. More importantly, it eliminates the possibility that processing rules will be applied inconsistently. This guarantees consistent structure and content, the twin requirements of conformance. The process can be streamlined by adding housekeeping columns to the base dimension that indicate the date the row was created and the date the row was last modified. These dates can be used by the process that loads the conformed rollup to identify changes. The development of ETL routines for aggregate fact tables is guided by the same principle. Sourcing the aggregate from the base star guarantees the conformance that is required, both in terms of structure and in terms of content. TiP Source aggregate tables from the base schema to guarantee consistent structure and content. Following these guidelines, the overall dependency of table load routines during the ETL process might look like this: 1. Load base dimensions 2. Load conformed rollups 3. Load base fact table 4. Load aggregate fact table Actual dependencies are slightly more relaxed than this. For example, after base dimensions have been loaded, it is possible to load the base fact table. Unfortunately, there is a complication.
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One potential roadblock stands in the way of this simple workflow: the type 1 change. When base data and aggregates are loaded sequentially, the occurrence of a type 1 change can force the need to reload the aggregate completely, rather than update it incrementally. To understand why this happens, consider a very simple example. Suppose an aggregate of order_facts contains rows that summarize orders by product manager, which is a type 1 attribute. This aggregate will call for a simple conformed rollup of the product dimension, which includes only the column product_manager.
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15 Aggregates 355
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The product dimension has hundreds of products. Each is managed by one of three people: Richard Meyers, David Yu, and Robert Jones. The product_manager table has one row for each of these people. Orders for all the products are recorded in order_facts, and the aggregate fact table summarizes orders by product_manager. Subsequently, one of the products that Richard Meyers managed is reassigned to David Yu. If we try to follow the process flow outlined earlier, this type 1 change gets us into trouble. Step by step, here is what happens: 1. Load base dimensions The type 1 change calls for the product in question to be updated; its new manager is David Yu. 2. Load conformed rollups If there is a date_updated on the base dimension table, it tells us the product has changed. The manager on the changed record is David Yu. A record already exists for David Yu in the product_manager table since he managed other products as well, so nothing needs to happen here. 3. Load base fact table Any new orders are added to the base fact table. 4. Load aggregate fact table The new orders are added to the aggregate fact table, associated with the correct manager. The problem here is that we are not finished. Because of the change in product manager, some previous order summaries that are already in the aggregate fact table must be adjusted. Some orders previously attributed to Richard Meyers are now attributed to David Yu. If we have followed this four-step process, we might not have been aware of this by the time we reached step four, especially if the aggregates were to be sourced from the base tables. One way to avoid this breakdown is to note that a change occurred during step one. Then, when loading the aggregate, we can fix things. In this case, we might note that product A11 had its manager changed to David Yu. When we get to step four, we can go back and look for orders of product A11 and add their totals to the summary row for David Yu. We are still not done. We also need to deduct these orders from whoever managed the product before David did. This happens to be Richard Meyers, but we cannot tell that from looking at the base dimension table, since it was updated in step one! The moral of this story is simple: when an aggregate contains type 1 attributes, a type 1 change forces processing that requires access to the before and after values, and access to the granular data. This is enough additional work that many ETL developers opt for a simpler brute-force approach: after updating the base schema, drop and re-create the aggregate. Often, this proves to be less processing-intensive than the alternative. Some tools that automate the creation of aggregate tables or cubes operate in this manner; if the aggregate contains a type 1 attribute, the cube is destroyed and rebuilt each time a change occurs. TiP Type 1 changes can significantly complicate aggregate processing. If an aggregate star includes a type 1 attribute, it may be best to drop and rebuild the aggregate fact table. Another way to avoid this situation is to exclude type 1 attributes from aggregate designs. Type 2 changes don t pose the same problem, since they don t restate the context of previously recorded facts.
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