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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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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

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cheap and sell that which is dear. Charles Dow, one of the founding fathers of Dow Jones & Company and the first managing editor of the Wall Street Journal, might have put it this way: Buy a stock that has value in earnings and value in the dividend paid out. As this stock rises in price and the value of earnings and dividends declines, sell the stock. Although the methods of evaluating stocks of value may have changed, the basic idea is still sound. The anticipation of value and value growth are what make a stock price rise. The value is not the price alone. However, it is one of the factors in determining value. Another important factor of value is earnings past and earnings forecast. Stock prices move in anticipation of future earnings growth.

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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