Abstract:
Request processing is optimized. The request has elements. The method, computer program and database system includes selecting an access path for the request taking into consideration a stored actual request element cost, processing the request using the selected access path, producing one or more actual request element costs, and storing the one or more actual request element costs.

Description:
BACKGROUND 
     A database management system is accessed and managed through “requests.” A request can be a query, through which the database is accessed, or an instruction to execute a utility, such as a load command. Most database management systems use an optimizer to select an access plan, or query execution plan, for a database request and most optimizers rely on a cost model, where cost is associated with the amount of a resource, such as CPU or I/O, that must be devoted to a task, to choose the best access plan to execute any given request. Most cost models rely on a mathematical construct that uses estimates of the numbers of rows (cardinalities) which will result at each step of the access plan. The estimates are often based on assumptions regarding the contents of the database tables. 
     SUMMARY 
     In general, in one aspect, the invention features a method for optimizing processing of a request. The request has elements. The method includes selecting an access path for the request taking into consideration a stored actual request element cost, processing the request using the selected access path, producing one or more actual request element costs, and storing the one or more actual request element costs. 
     Implementations of the invention may include one or more of the following. The request may include one or more predicates. Each predicate may reference one or more relations. Selecting an access path may include identifying the relations in the request, identifying one or more access paths for each relation, and extracting the predicates from the request. For each predicate the method may include associating the predicate with the one or more access plans identified for the one or more relations referenced in the predicate and estimating the cost of one or more access paths associated with the predicate. For each access path the method may include selecting the cheaper of the estimated access path cost and an actual access path cost, if one exists. Estimating the cost of one or more access paths may include retrieving estimated costs stored in a data dictionary. Estimating the cost of one or more access paths may include performing selectivity costing based on one or more of selectivity, cardinality and statistics. Selecting an access path may further includes for each predicate, selecting the lowest cost access path from among the access paths associated with each predicate. 
     Requests may be sorted into workload groups. The method may include categorizing the actual request element costs according to the workload group to which the request belongs. Selecting an access path for the request taking into consideration a stored actual request element cost may include taking into consideration the categorized actual request element cost, if it exists, for the workload group to which the request belongs. Storing the one or more actual request element costs may include storing the one or more actual request element costs in a cache. Storing the one or more actual request element costs may include backing up the stored one or more actual request element costs from the cache to a query capture data base. The method may further include assigning the request to an one of a plurality of workload groups. Selecting an access path for the request may include taking into consideration a stored actual request element cost if the one of the plurality of workload groups is identified for such processing. Selecting an access path for the request may not include taking into consideration a stored actual request element cost if the one of the plurality of workload groups is not identified for such processing. Storing the one or more actual request element costs may include performing a workload analysis to associate each actual request element cost with the workload group to which the request is assigned. Performing the workload analysis may include performing at least a portion of the workload analysis off-line. Performing the workload analysis may include performing at least a portion of the workload analysis in real time. The method may further include creating one or more workload groups and assigning the request to one of the one or more workload groups. 
     In general, in another aspect, the invention features a method for optimizing processing of a workload group. The workload group includes one or more requests. Each request has elements. The method includes selecting an access path for a request taking into consideration a stored actual request element cost, categorized by workload group. The method further includes processing the request using the selected access path, producing one or more actual request element costs, and storing the one or more actual request element costs, categorized by workload group. 
     In general, in another aspect, the invention features a computer program, stored on a tangible storage medium, for use in optimizing processing of a request, the request having elements. The program includes executable instructions that cause a computer to select an access path for the request taking into consideration a stored actual request element cost, process the request using the selected access path, producing one or more actual request element costs, and store the one or more actual request element costs. 
     In general, in another aspect, the invention features a database system including a massively parallel processing system, which includes one or more nodes, a plurality of CPUs, each of the one or more nodes providing access to one or more CPUs, a plurality of data storage facilities each of the one or more CPUs providing access to one or more data storage facilities, and a process for execution on the massively parallel processing system for optimizing processing of a request. The request has elements. The process includes selecting an access path for the request taking into consideration a stored actual request element cost, processing the request using the selected access path, producing one or more actual request element costs, and storing the one or more actual request element costs. 
     Implementations of the invention may include one or more of the following. The database system may include a plurality of additional process for optimizing processing of a request. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a node of a database system. 
         FIG. 2  is a block diagram of a parsing engine. 
         FIG. 3  is a block diagram of a parser. 
         FIG. 4  is a block diagram of a system to capture information regarding request processing. 
         FIGS. 5-8  are flow charts for closed-loop processes to estimate the cost of executing requests. 
     
    
    
     DETAILED DESCRIPTION 
     The closed-loop technique for estimating the cost to execute requests disclosed herein has particular application, but is not limited, to large databases that might contain many millions or billions of records managed by a database system (“DBS”)  100 , such as a Teradata Active Data Warehousing System available from NCR Corporation.  FIG. 1  shows a sample architecture for one node  105   1  of the DBS  100 . The DBS node  105   1  includes one or more processing modules  110   1 . . . N , connected by a network  115 , that manage the storage and retrieval of data in data-storage facilities  120   1 . . . N . Each of the processing modules  110   1 . . . N  may be one or more physical processors or each may be a virtual processor, with one or more virtual processors running on one or more physical processors. 
     For the case in which one or more virtual processors are running on a single physical processor, the single physical processor swaps between the set of N virtual processors. 
     For the case in which N virtual processors are running on an M-processor node, the node&#39;s operating system schedules the N virtual processors to run on its set of M physical processors. If there are 4 virtual processors and 4 physical processors, then typically each virtual processor would run on its own physical processor. If there are 8 virtual processors and 4 physical processors, the operating system would schedule the 8 virtual processors against the 4 physical processors, in which case swapping of the virtual processors would occur. 
     Each of the processing modules  110   1 . . . N  manages a portion of a database that is stored in a corresponding one of the data-storage facilities  120   1 . . . N . Each of the data-storage facilities  120   1 . . . N  includes one or more disk drives. The DBS may include multiple nodes  105   2 . . . O  in addition to the illustrated node  105   1 , connected by extending the network  115 . 
     The system stores data in one or more tables in the data-storage facilities  120   1 . . . N . The rows  125   1 . . . Z  of the tables are stored across multiple data-storage facilities  120   1 . . . N  to ensure that the system workload is distributed evenly across the processing modules  110   1 . . . N . A parsing engine  130  organizes the storage of data and the distribution of table rows  125   1 . . . Z  among the processing modules  110   1 . . . N . The parsing engine  130  also coordinates the retrieval of data from the data-storage facilities  120   1 . . . N  in response to queries received from a user at a mainframe  135  or a client computer  140 . The DBS  100  usually receives queries and commands to build tables in a standard format, such as SQL. 
     In one implementation, the rows  125   1 . . . Z  are distributed across the data-storage facilities  120   1 . . . N  by the parsing engine  130  in accordance with their primary index. The primary index defines the columns of the rows that are used for calculating a hash value. The function that produces the hash value from the values in the columns specified by the primary index is called the hash function. Some portion, possibly the entirety, of the hash value is designated a “hash bucket”. The hash buckets are assigned to data-storage facilities  120   1 . . . N  and associated processing modules  110   1 . . . N  by a hash bucket map. The characteristics of the columns chosen for the primary index determine how evenly the rows are distributed. 
     In one example system, the parsing engine  130  is made up of three components: a session control  200 , a parser  205 , and a dispatcher  210 , as shown in  FIG. 2 . The session control  200  provides the logon and logoff function. It accepts a request for authorization to access the database, verifies it, and then either allows or disallows the access. 
     Once the session control  200  allows a session to begin, a user may submit a SQL request, which is routed to the parser  205 . As illustrated in  FIG. 3 , the parser  205  interprets the SQL request (block  300 ), checks it for proper SQL syntax (block  305 ), evaluates it semantically (block  310 ), and consults a data dictionary to ensure that all of the objects specified in the SQL request actually exist and that the user has the authority to perform the request (block  315 ). The parser  205  runs an optimizer (block  320 ), which develops the least expensive plan to perform the request. Finally, a generator (block  325 ) generates the steps to be executed. 
     As illustrated in  FIG. 4 , the steps generated for queries and other requests, and statistics regarding system performance for those steps, are stored in one or more database query logs  405 . The system may include a single database query log  405  or it may include multiple database query logs. For example, each user may be assigned his/her own database query log. Similarly, each workload group (defined below) may be assigned its own database query log. Further, even if the database query log is a single log, it may be organized by user or by workload groups. 
     Queries and other requests are sorted into sets (i.e., classes of queries) known as “workload groups” having, for example, similar performance expectations. For example, the system may define service level goals (SLGs) for each workload group which are used to monitor and adjust the performance of the system, as described in co-pending U.S. patent application Ser. No. 10/730,348, entitled Administering the Workload of a Database System Using Feedback, by Douglas P. Brown, Anita Richards, Bhashyam Ramesh, Caroline M. Ballinger, and Richard D. Glick, filed on Dec. 8, 2003, incorporated by reference. The service level goals, the association of requests with workloads, and other workload information, are stored in a workload definition database  410 . 
     A workload analysis process  415  retrieves performance information from the database query logs  405  and stores it in a query capture database (QCD)  420 . Alternatively, the system stores the performance information in a workload cache  425 . In one example system, the cache is sorted by workload groups, which means that performance information can be accessed quickly if an identifier for the workload group is known. The workload analysis process  415  may be a manual process or it may be automatic. The process may be performed off-line, e.g., at night, or it may be performed in real time. 
     The information from the workload cache  425  is backed up (block  430 ) to the QCD periodically or when the cache is full or when another condition related to the process about to be described occurs. The result is that the QCD  420  contains actual cost information for query steps that have been executed by the system. This workload analysis may be performed for all workload groups or for only selected workload groups. 
     Included in the actual cost information stored in the QCD is the cost of processing identified predicates using identified access paths to access relations referenced in a requests (e.g., a tables). An example of a predicate is found in the WHERE clause of a SQL statement. For example, in the SQL statement: 
     SELECT*WHERE table1.name=‘joe’; 
     the single predicate is “table1.name=‘joe’”. In this example, the relation referenced in the predicate is “table1”. A request may have multiple predicates and the predicates themselves can take a variety of forms, as is known in the art. 
     An access path to a relation is a method for retrieving information from the relation. Each relation may have several access paths. For example, a particular relation may have the following access paths:
         all_access_ok: a relation with this access path can be scanned using any of the access paths defined on the table, including those listed below.   nu2nd_access_ok: a relation with this access path can only be scanned using a non-unique secondary index.   pk_access_ok: a relation with this access path can only be scanned using a unique primary index.   seq_access_ok: a relation with this access path can only be scanned sequentially.
 
When planning access to a single relation as part of an overall plan for processing a request, the optimizer  320  costs all of the available access plans and identifies the index ID of the cheapest access path.
       

     A predicate that references a relation may be executed using any of the access paths for that relation. The database query logs store actual performance statistics (also called “actual costs”) for predicates that have been executed using the different access paths for the relations referenced in the predicates. Herein, costs refer to the resources that a request consumes. For example, a request may cost 100 CPU-seconds, 100 input/outputs, and 2 hours of actual time. 
     As illustrated in  FIG. 5 , the system uses the actual costs stored in the QCD  420  to improve the operation of the optimizer  320 . As shown in  FIG. 5 , a request or requests is presented to an access preparation block  510 , which identifies all of the predicates and associated access paths for each of the requests to be executed, along with the cost associated with the predicates and associated access paths. In making these identifications, for at least some workgroups, the access preparation block  510  takes into account the actual cost information stored in the QCD  420 . 
     The set of predicates and associated access plans is presented to an access plan processor (block  515 ) which uses predetermined criteria (such as total cost) to select an access plan for each predicate from among the available access plans for each predicate. The access path processor  515  then executes the predicates using the selected access plans and collects actual cost information as each of the predicates is executed. The actual cost information is stored (block  520 ) in the database query logs  405 . The actual cost information is analyzed (block  420 ) and stored in the QCD  420  or in the workload cache  425  shown in  FIG. 4 , as discussed above. Thereafter, actual cost information is available for use in determining the cost of performing identified predicates using identified access plans. In one example system, the actual cost information is only available for workload groups for which such analysis has been selected and is not available for other workload groups. 
     As shown by the arrowed circle in  FIG. 5 , the access path processing forms a feedback loop. The actual cost of processing different access paths is used to adjust the estimates of the same cost made by the optimizer. 
     The access plan preparation block, shown in more detail within the dotted lines in  FIG. 6 , begins with predicate analysis (block  605 ). Predicates are sometimes called conditions, or in this application, request elements. In one example system, the predicate analysis process  510  generates an ANDED list of predicates that have been specified in, for example, the WHERE clause of a SQL SELECT statement. The ANDED predicate list may be pruned, for example, to exclude predicates for which a cost cannot be estimated. The predicate analysis process is similar to that described in as described in co-pending U.S. patent application Ser. No. 09/977,038, entitled “Index Selection in a Database System,” by Douglas P. Brown, Jeetendra Chaware, and Manjula Koppuravuri, filed on Oct. 12, 2001, incorporated by reference. 
     The ANDED predicate list is used to build (block  610 ) a list of predicates and associated access paths  615 . A list of access paths for each relation and other information useful for this analysis is stored in a dictionary  618 . A cost estimate  625  is computed (block  620 ) for each access path for each of the predicates using information stored in the dictionary  618 . The estimated cost is adjusted (block  630 ) using the actual cost information stored in the QCD (or in the workload cache  425 ), if the actual cost information exists. Processing then continues as described above with respect to  FIG. 5 . 
     The estimate adjustment process  630  may be invoked for all predicates and access paths associated with all received requests or it may be invoked more selectively. In one example system, the estimate adjustment process is only invoked for access paths associated with requests belonging to a selected workload group or groups. Access paths associated with requests belonging to non-selected workload groups would not have their estimated access path costs adjusted and would instead use the access path costs stored in location  625 . 
     An example process for adjusting the cost estimates (block  620 ), illustrated in  FIG. 7 , compares the estimated access path costs  625  with the actual access path costs stored in the QCD  420  or workload cache  425 , if they exist (block  705 ). In one example system, the access path cost comparator (block  705 ) determines if an actual path for a particular access path is stored in the QCD. If so, the system uses the actual cost rather than the estimated cost. As shown in  FIG. 7 , if an actual cost exists it is retrieved from the QCD (block  710 ). Otherwise, the estimated cost is retrieved (block  715 ) from the list of estimated costs. The selection between the estimated access cost and the actual access cost is represented by a switch  720 , which is driven by a actual/estimated signal from the access path cost comparator (block  705 ). Otherwise, processing proceeds as described with respect to  FIGS. 5 and 6 . As mentioned above, the process shown within the dashed lines in  FIG. 7  may be performed for all of the access plans or it may only be performed with respect to certain access plans associated with predicates that are part of requests assigned to selected workload groups. 
     In operation, a request (or several requests  505 ) are analyzed to identify their associated predicates (block  805 ). The predicates are analyzed, using information stored in the dictionary  618  to identify access paths for each predicate (block  810 ). The identified access paths and their associated predicates are stored in a list  615 . A cost is estimated for each access path (block  815 ). The costs are stored in another list  625 . 
     The estimated costs are then adjusted. For each access path, the system determines if the QCD  415  contains an actual cost (block  820 ). If so, the actual cost is used (block  825 ). Otherwise, the estimated cost is used (block  830 ). The system then moves to the next access path (block  835 ), returning to block  820 . Once again, this process may be performed selectively, as described above. 
     Once the system has adjusted the cost for each of the access paths, it selects, using a predetermined criteria (such as lowest cost) a single access path for each predicate from among the access paths available for each predicate (block  840 ). It then processes and monitors the actual cost of each of the access paths (block  840 ). The system stores the actual costs in the database query logs  520  (block  845 ). Finally, the system analyzes the workloads  850  and stores the results in the QCD  415  (block  850 ). 
     As can be seen in  FIG. 8 , the system has two loops, one nested inside the other. The first loop, including blocks  820 ,  825 ,  830  and  835 , selects an actual cost or an estimated cost for each of the access paths. The second loop, which includes each of the elements of the first loop and elements  840 ,  845 ,  520 ,  850  and  415 , determines an adjusted estimated cost for each of the access paths. The second loop provides feedback which has the capability of improving the performance of the optimizer  320 . 
     The text above described one or more specific embodiments of a broader invention. The invention also is carried out in a variety of alternative embodiments and thus is not limited to those described here. For example, while the invention has been described here in terms of a DBMS that uses a massively parallel processing (MPP) architecture, other types of database systems, including those that use a symmetric multiprocessing (SMP) architecture, are also useful in carrying out the invention. The foregoing description of the preferred embodiment of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the invention be limited not by this detailed description, but rather by the claims appended hereto.