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Simplifying Processing
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Another way to optimize slow change processing is to simplify it. As Figure 17-2 illustrated, as many as three different lookups or comparisons may be necessary for each source record: looking for new records (step 1.4), looking for type 1 changes (step 1.5), and looking for type 2 changes (step 1.7). Each of these lookups requires comparing one or more columns of a source record to corresponding records in the dimension table.
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Developers may be able to squeeze performance out of the process by eliminating one or more of these lookups. For example, new records are handled in much the same way as type 2 changes: by inserting data into the table. It is possible to forgo the first lookup, allowing new records to be processed through the same steps that identify type 2 changes. Similarly, developers may be able to save time by assuming that all source records contain type 1 changes, skipping the comparison. Issuing an update for each source record may save time if a large number of type 1 changes are expected, or if the database has an upsert capability that can combine this with processing new records.
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Most lookups cannot be eliminated, but they can be optimized. One way to optimize a lookup is to load the entire dimension into primary memory while the ETL process is running, or to load the columns involved in comparisons. This technique is variously called pinning or caching. It ensures that lookups will take place quickly. This benefit is offset by the amount of time it takes to load the data into primary memory. The time it takes depends both on the number of rows that must be cached, as well as the number of columns. In addition, dimension processing may result in new or changed rows; these must be maintained in the cache as well.
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The efficiency of a lookup can also be enhanced by reducing the number of columns that must be compared. This can be achieved by using a hash function. Determining whether a type 2 change has occurred normally requires comparing the type 2 attributes of a source record with the corresponding attributes in the dimension table. If a customer dimension table has 75 type 2 attributes, all 75 of them must be checked. This may require undue processing resources or make caching impractical. A hash function can reduce the number of columns compared to one. A hash function, when applied to a string of data, produces a comparatively compact string or number. This value is referred to as a hash value or checksum. The same hash function will always provide the same hash value when applied to the same string of data. This principle can be used to simplify lookup processing. The 75 type 2 attributes of each customer, for example, can be concatenated to a single string, to which a hash function is applied. The resulting hash values are stored with each customer row in the dimension table. An example involving 12 attributes is illustrated in Figure 17-7.
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Part VI
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PART VI
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CUSTOMER customer_key customer_id customer_name billing_address billing_city billing_state billing_zip phone customer_type income_band age_range census_group census_tract marital_status birth_date . . . type2_hash SK NK 2 2 2 2 2 2 2 2 2 2 2 2 1 HK
12 type 2 columns
1 checksum
Figure 17-7 A checksum for 12 type 2 attributes
With this solution in place, source records can be tested for changes without comparing all the type 2 attributes to content in the dimension table. Instead, the hash function is applied to the source record. The resulting hash value is compared to the one stored in the dimension table. This greatly simplifies the lookup process, requiring the comparison of a single value rather than all the type 2 attributes. This also facilitates caching when the number of type 2 columns would have rendered it impractical. It is not necessary to cache the entire dimension table; all that is needed are the natural key, hash value, and surrogate key. An additional hash value may be computed on the type 1 attributes. TIP Lookups involving large numbers of columns may be simplified using hash functions. A hash value is computed and stored for each dimension record. The same function can be applied to source data, so that lookups only involve the hash value. This benefit, of course, is not free. In exchange for forgoing comparison of a large number of attributes, it will be necessary to compute hash values for each dimension record, and for each source record being processed. In addition, there is the risk of collisions situations in which a hash function returns the same value for two different strings of data. A hash algorithm must be selected that renders this possibility sufficiently small.
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