DB2 10.5 for Linux, UNIX, and Windows

Query performance for common SQL statements

A number of performance improvements have been made to improve the speed of many queries. These improvements are automatic. There are no configuration settings or changes to the SQL statements required.

Partial early distinct (PED)

An efficient hashing function will now be used to partially remove duplicates early in the processing of the query. This may not remove all duplicates, but will reduce the amount of data that must be processed later in the query evaluation. Removing some of the initial duplicate rows will speed up the query, and reduce the chance that it will run out of sort heap memory, thereby eliminating the need to use relatively slow disk space for temporary storage in these cases. This improvement is termed partial early distinct (PED).

To determine if this improvement is being used for a particular query, activate the Explain facility and run the query. A new value in the EXPLAIN_ARGUMENT table indicates when this new functionality has been applied to a query:

ARGUMENT_TYPE column = UNIQUE
ARGUMENT_VALUE column can now also have the value: HASHED PARTIAL which indicates that the new feature has been used

The db2exfmt tool will also show HASHED PARTIAL in its output, as shown in the following example:

6) UNIQUE: (Unique)
      Cumulative Total Cost: 		132.519
      Cumulative CPU Cost: 		1.98997e+06
      ...
      ...
      Arguments:
      ---------
      JN INPUT: (Join input leg)
            INNER
      UNIQKEY : (Unique Key columns)
            1: Q1.C22
      UNIQKEY : (Unique Key columns)
            2: Q1.C21
      pUNIQUE  : (Uniqueness required flag)
            HASHED PARTIAL

Partial early aggregation (PEA)

Similar to partial early distinct (PED), partial early aggregation (PEA) is an attempt to do a partial aggregation of data early in the processing of the query. While it is unlikely that all aggregation can take place at this point, it will at least reduce the amount of data that must be processed later in the query evaluation.

To determine if partial early aggregation is being used for a particular query activate the Explain facility and run the query. A new value in the EXPLAIN_ARGUMENT table indicates when this new functionality has been applied to a query:

ARGUMENT_TYPE column = AGGMODE
ARGUMENT_VALUE column can now also have the value: HASHED PARTIAL which indicates that this new feature has been used

The db2exfmt tool will also show HASHED PARTIAL in its output for GRPBY sections, along with a pGRPBY in the tree view, if this new functionality has been applied within that part of the query.

Hash join now selected by the query optimizer for a wider range of SQL queries

The query optimizer chooses between three basic join strategies when determining how to run an SQL query that includes a join. In many cases a hash join is the most efficient method, and with this release it can be used in more situations.

Data type mismatches: A hash join will now be considered even if the two columns in the join are not the same data type. This is the case in all but the most extreme situations.
Expressions used in join predicate: Join predicates that contain an expression no longer restrict the join method to a nested loop join. In this release a hash join is considered in cases where the WHERE clause contains an expression, like: WHERE T1.C1 = UPPER(T1.C3)

In these cases the hash join is considered automatically. There is no need to change any existing SQL queries to take advantage of this improved functionality. Note that hash joins make use of sort heap memory.

Improved cost estimates of network communication traffic generated by a query

The query optimizer relies on a range of information to choose an access plan that is efficient as possible. The estimated communication costs of queries has now been improved, enabling the optimizer to more accurately consider and compare all of the CPU, IO, and communication costs. In many cases this will result in faster query performance.

The estimated per-node communication costs of a query, as returned by the explain elements COMM_COST and FIRST_COMM_COST, have been improved. They are now more consistent with the existing CPU and IO costs per-node calculations. This enables the query optimizer to effectively balance all three of these cost estimations when evaluating different access plans. It also helps increase parallelism when possible by enabling the network traffic to be spread more evenly across multiple network adapters. In particular:

If there is more than one network adapter involved, the cumulative communication cost for the adapter with the highest value is returned. In previous releases the total number of frames transmitted throughout the entire network was returned.
The values only include the costs of network traffic between physical machines. They do not include the virtual communication costs between node partitions on the same physical machine in a partitioned database environment.