Grouping hash implementation

 

This technique uses the base hash access method to perform grouping or summarization of the selected table rows. For each selected row, the specified grouping value is run through the hash function. The computed hash value and grouping value are used to quickly find the entry in the hash table corresponding to the grouping value.

If the current grouping value already has a row in the hash table, the hash table entry is retrieved and summarized (updated) with the current table row values based on the requested grouping column operations (such as SUM or COUNT). If a hash table entry is not found for the current grouping value, a new entry is inserted into the hash table and initialized with the current grouping value.

The time required to receive the first group result for this implementation will most likely be longer than other grouping implementations because the hash table must be built and populated first. Once the hash table is completely populated, the database manager uses the table to start returning the grouping results. Before returning any results, the database manager must apply any specified grouping selection criteria or ordering to the summary entries in the hash table.

 

Where the grouping hash method is most effective

The grouping hash method is most effective when the consolidation ratio is high. The consolidation ratio is the ratio of the selected table rows to the computed grouping results. If every database table row has its own unique grouping value, then the hash table will become too large. This in turn will slow down the hashing access method.

The optimizer estimates the consolidation ratio by first determining the number of unique values in the specified grouping columns (that is, the expected number of groups in the database table). The optimizer then examines the total number of rows in the table and the specified selection criteria and uses the result of this examination to estimate the consolidation ratio.

Indexes over the grouping columns can help make the optimizer's ratio estimate more accurate. Indexes improve the accuracy because they contain statistics that include the average number of duplicate values for the key columns.

The optimizer also uses the expected number of groups estimate to compute the number of partitions in the hash table. As mentioned earlier, the hashing access method is more effective when the hash table is well-balanced. The number of hash table partitions directly affects how entries are distributed across the hash table and the uniformity of this distribution.

The hash function performs better when the grouping values consist of columns that have non-numeric data types, with the exception of the integer (binary) data type. In addition, specifying grouping value columns that are not associated with the variable length and null column attributes allows the hash function to perform more effectively.

 

Parent topic:

Grouping optimization