SQL tuning base

Information

  • Patent Grant
  • 8983934
  • Patent Number
    8,983,934
  • Date Filed
    Tuesday, September 7, 2004
    20 years ago
  • Date Issued
    Tuesday, March 17, 2015
    9 years ago
Abstract
A computer readable medium storing a database query language statement tuning base in a tuning base memory location is disclosed. The tuning base includes tuning information for one or more query language statements. The tuning information for each statement includes one or more tuning actions for the statement, and a signature to allow an optimizer to identify the one or more tuning actions for the statement.
Description
FIELD OF THE INVENTION

This invention is related to the field of electronic database management.


BACKGROUND

In a database management system, SQL statements are used to manipulate data and to retrieve data that matches certain selection criteria. A SQL statement is compiled in memory before being executed by a database engine. Though the compiled form of the SQL statement may be cached in memory for some amount of time for repeated executions, it is eventually discarded. Therefore SQL statements can be considered transient objects in a database system.


In practice, the set of SQL statements used by an application are repeatedly executed, and it is highly likely that the same SQL statements appear (are compiled into memory) with certain frequencies. The knowledge that certain SQL statements (especially those critical to application performance) reappear can be taken advantage of by placing special manual controls affecting the performance of the SQL statements.


However, selecting the proper control to insert is difficult or impossible, because execution data for the SQL statement is not collected and is therefore not used as feedback to select a control to influence future executions of a SQL statement. Even if an appropriate control is selected for targeting a SQL statement, the data (or metadata) cannot be associated with the SQL statement, because the SQL statement has no persistent representation. Thus, conventional methods place a control on a SQL statement by either modifying the text of the SQL statement, or by making modifications to the session context in which a SQL statement is executed. Both of these approaches require application changes, which can be difficult and sometimes impossible. Therefore, conventional methods of associating metadata with a SQL statement locate the metadata within the context of the executing SQL statement. If the metadata cannot be located in the execution context of SQL statement, the database system has no other way to deliver the metadata to the database engine when the SQL statement re-appears and is compiled into memory.


SUMMARY

A computer readable medium storing a database query language statement tuning base in a tuning base memory location is disclosed. The query language statement tuning base includes tuning information for one or more query language statements. The tuning information for each statement includes one or more tuning actions for the query language statement, and a signature to allow an optimizer to identify the one or more tuning actions for the query language statement.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 shows an example of a device that includes a SQL tuning base.



FIG. 2 shows an example of a method to perform accessing an object in a SQL tuning base.



FIG. 3 shows an example of a method of generating and persistently storing tracing information with the tuning base.



FIG. 4 shows an example of a method to use a profile in the tuning base to generate a well-tuned plan.



FIG. 5 is a block diagram of a computer system suitable for implementing an embodiment of coverage computation for verification.





DETAILED DESCRIPTION

Overview


The embodiments of the invention are described using the term “SQL”, however, the invention is not limited to just this exact database query language, and indeed may be used in conjunction with other database query languages and constructs.


The SQL tuning base (STB) provides a storage facility for a SQL statement and tuning information associated with the statement. The STB provides a mechanism for providing access to tuning information by automatically collecting, analyzing, and delivering the tuning information to an optimizer. This persistence and delivery mechanism can store a variety of tuning features in a location that is external to the SQL statement. The tuning features can persistently target a specific SQL statement, and can be delivered by the STB to the optimizer when the SQL statement is compiled.


An example of a device that includes a SQL tuning base is shown in FIG. 1. An application program 105 running on a computer processing system issues a SQL statement 110 to query optimizer 120. The optimizer sends a signature of the statement to lookup device 125 to determine if the tuning base 130 of database 180 has tuning information about the statement. The lookup device retrieves the information from a profile section 140, an outline section 150, or a tracing information section, 160, of tuning base 130. The optimizer uses the tuning information to generate an execution plan 185, which, when executed, selects query results 190 from the database 180, and returns the results to the application.


For example, a persistent representation of a SQL statement can be used by a lookup scheme to identify the tuning information for the SQL statement from the STB, and send it to the optimizer at SQL compilation time. The STB therefore allows a tuning control, such as historical feedback control for example, to target a specific SQL statement, while being stored outside of the SQL statement and the application program that stores the SQL statement. This ability to store tuning information independently of the SQL statement and the application program of the statement can be used to automatically manage the database.


The STB stores tuning information for SQL statements on a per statement basis. The STB stores multiple types of metadata objects related to tuning actions for individual SQL statements: tracing information, outlines, and profiles. The tracing information provides the ability to trace and debug individual SQL statements. The outline facility offers plan stability and plan editing capabilities. The profile facility provides plan tuning functions. The metadata in the STB can be represented as a set of dictionary tables. For example, the STB can include a SQL table that stores a set of SQL statements that are associated with one or more tuning actions. The STB can also include a profile table that stores SQL profiles, an outline table that stores outlines, and a tracing table that stores tracing information.


Access to Tuning Information


At SQL compilation time, a signature is generated from the SQL text, and is used as a lookup key for any SQL tuning data associated with this SQL statement. This signature can be extended to include other environmental data values known before SQL compilation, such as “parsing user name” for example.


Any STB data retrieved by the optimizer is either directly consumed by the optimizer in creating the execution plan, or is copied to the compiled SQL statement for use while the statement is executed. For example, in one embodiment, SQL profile data is copied into the compiled SQL statements. However, the SQL profiles and outlines can be used without copying their data. (The tracing and debugging data is copied though, since tracing and debugging are acted upon during SQL execution as well as during compilation.)


Because the SQL tuning base is expected to be sparsely populated (in other words, most SQL statements are usually not represented in the STB), the lookup mechanism can be designed to take advantage of the negative lookup case. In one embodiment, this is performed by hashing the signature into a bit-vector (cached in memory), where 0 represents the fact that no data exists for the given SQL statements that hash to this value, and 1 means that a lookup to the STB tables is performed to see if any data exists for it.



FIG. 2 shows an example of a method to perform accessing an object in a SQL tuning base. The statement is received by a compiler, 210. The signature of the statement is determined, 220, and is mapped to a portion of a lookup table, 230. If the information in the mapped portion of the lookup table indicates that tuning information is available, 240, then the tuning information for the SQL statement is retrieved from the tuning base, 250. The tuning information is then used by the compiler to tune the statement.


Tracing and Debugging


Tracing and debugging information from the tracing table can be used to identify a flaw in a SQL statement. For example, if a database administrator (DBA) determines that a certain statement is a high load statement, the DBA can cause the system to trace the execution of the statement. The tracing output for the statement can be persistently stored and used during a tuning process. The tracing information can be collected for a single SQL statement to be traced by setting a tracing parameter using the STB. Other parameters, such as enabling plan statistics and setting plan events, can also be set using the STB. An advantage of setting these features through the tuning base is being able to perform tracing functions without changing the software for the SQL statement in the application program or enabling tracing for the entire user session.


In one implementation, tracing of a SQL statement in the database causes every execution of that statement to be traced until the tracing is disabled for the SQL statement. Similarly, the plan statistics are traced as well. And thus statistics can be collected and used to improve performance of the SQL statement across different sessions and time periods.


A method of generating and persistently storing tracing information with the tuning base is shown in FIG. 3. A user sets a tracing parameter for the statement in the tuning base, 310. When the statement is received by a compiler, 320, the tracing parameter is retrieved from the tuning base, 330. When the statement is executed, 340, tracing information is collected and stored to disk, 350. The tracing information can then be used during a tuning process to tune the SQL statement, 360.


Outline


An outline is an abstraction of an execution plan generated by the optimizer. The stored outline, when used by an optimizer, causes the optimizer to use a specific plan, and, when stored in the tuning base, provides a persistent representation of the specific plan for the SQL statement. The outline can also be used to revert to a saved execution plan when a new plan generated by the optimizer is sub-optimal. Thus, an outline provides stability for an execution plan.


Profile


A SQL profile is a mechanism that is used to influence which plan is generated by the optimizer. The profile contains tuning information related to the statement, which is stored as a persistent database object, in a dictionary table, of the tuning base. A profile can have a name, and can be identified by its name, or by the signature of the corresponding SQL statement. The profile can be manually created by a database administrator (DBA), or automatically created by an auto-tune process. The profile can also be altered, cloned, deleted, and updated.


When the corresponding SQL statement is compiled (i.e., optimized), the query optimizer retrieves the SQL Profile from the tuning base. The tuning information from the SQL Profile is used by the optimizer, in conjunction with existing statistics, to produce a well-tuned plan for the corresponding SQL statement. The tuning information stored in the profile table can include a set of optimizer hints that target a particular SQL statement. Each hint can specify tuning actions such as setting parameters of the optimizer, adding or correcting statistics and estimates for the SQL statement, and modifying the execution behavior of the statement.


Statistics adjustment hints (e.g. TABLE_STATS( ), COLUMN_STATS( ), INDEX_STATS( ) hints) are used to adjust statistics of base objects accessed by the statement being compiled. For example, a NDV adjustment hint is used to correct the distinct cardinality, or the number of distinct values, estimate of a join key. A selectivity adjustment hint is used to correct the index selectivity of an index access path. A statistic adjustment hint contains adjustments to a stale statistic. A cardinality adjustment hint is used to correct the cardinality estimate of a result. An auto tuning hint can also specify correct optimization mode to use, such as FIRST_ROWS or ALL_ROWS.


The use of profile remains completely transparent to the end-user. For example, when a SQL statement is compiled, the optimizer searches the tuning base to determine if a SQL profile exists for that statement. If a profile exists, it is loaded into the optimizer, and information in the profile is used when building the execution plan for the statement. Because the profile is stored in the tuning base instead of being directly embedded in the text of the statement, the tuning base allows the hints in the profile to be fully separated from the statement.


An advantage of independently creating, storing, and accessing the profile with the SQL tuning base is that the SQL text is separated from the set of tuning hints and actions. Hence, the execution plan of a SQL statement can be tuned by the optimizer without changing the application source code of the statement. Therefore, with the tuning information stored in the tuning base, execution plans for SQL statements that are issued by packaged applications are tuned by gathering and storing related information for the SQL statement within the SQL tuning base of the database system itself.



FIG. 4 shows an example of a method to use a profile in the tuning base to generate a well-tuned plan. The statement is received by a compiler, 410. The profile for the statement is retrieved from the tuning base, 420. Statistic hints in the profile are used to adjust statistics related to the statement, 430. Estimate hints in the profile are used to adjust table estimates, 440. Optimizer settings are determined from tuning information in the profile, 450. Execution parameters for the statement are set based on the information in the profile, 460. A well-tuned execution plan for the statement is determined based on the hints, 470.


Automatic Creation of Tuning Information


The tuning information for a SQL statement can be automatically generated by the database system. For example, an auto tuning optimizer, when tuning a statement, can detect errors present in estimates related to the statement. After an error is detected, it can be removed or reduced by applying a hint, such as an adjustment factor, to it. By reducing or eliminating these errors, the optimizer can select a better execution plan. Hints can also be generated to adjust stale statistics or to supply missing statistics for tables and indexes. Further, hints can be generated to store and supply relevant information about the past execution history of the SQL statement. The execution history can then be sent from the STB to the optimizer, to set an appropriate optimization mode.


The auto-tuning hints for the SQL statement are grouped together in a SQL profile which is associated with the SQL statement and is stored persistently in the SQL tuning base. When the SQL statement is compiled by the optimizer under normal mode, the auto tuning hints from the corresponding SQL profile are retrieved from the SQL tuning base to help the optimizer produce a well-tuned plan. Hence, the tuning process can be performed only once, and the resulting hints can be reused many times.



FIG. 5 is a block diagram of a computer system 500 suitable for implementing an embodiment of verification based on coverage computation. Computer system 500 includes a bus 502 or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor 504, system memory 506 (e.g., RAM), static storage device 508 (e.g., ROM), disk drive 510 (e.g., magnetic or optical), communication interface 512 (e.g., modem or ethernet card), display 514 (e.g., CRT or LCD), input device 516 (e.g., keyboard), and cursor control 518 (e.g., mouse or trackball).


According to one embodiment of the invention, computer system 500 performs specific operations by processor 504 executing one or more sequences of one or more instructions contained in system memory 506. Such instructions may be read into system memory 506 from another computer readable medium, such as static storage device 508 or disk drive 510. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the invention.


The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to processor 504 for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as disk drive 510. Volatile media includes dynamic memory, such as system memory 506.


Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read.


In an embodiment of the invention, execution of the sequences of instructions to practice the invention is performed by a single computer system 500. According to other embodiments of the invention, two or more computer systems 500 coupled by communication link 520 (e.g., LAN, PTSN, or wireless network) may perform the sequence of instructions to practice the invention in coordination with one another. Computer system 500 may transmit and receive messages, data, and instructions, including program, i.e., application code, through communication link 520 and communication interface 512. Received program code may be executed by processor 504 as it is received, and/or stored in disk drive 510, or other non-volatile storage for later execution.


In the foregoing specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than restrictive sense.

Claims
  • 1. An article of manufacture comprising a volatile or non-volatile computer readable storage medium storing a system for tuning database query language statements, comprising: a tuning base containing tuning information for a database query language statement, the tuning information comprising: trace information for the database query language statement, wherein the trace information comprises statistics of a database object for the database query language statement, wherein the statistics in the trace information are adjusted to tune execution of the database query language statement when the database query language statement is compiled or executed; anda signature which is generated during compilation of the database query language statement and is used to identify the trace information for the database query language statement; anda query optimizer that tunes the database query language statement at least by retrieving at least the tuning information from the tuning base during compilation of the database query language statement, based at least in part the signature for the database query language statement, wherein the query optimizer tunes the database query language statement at least by modifying a compiled database query statement of the database query language statement to include at least a part of the tuning information for the execution of the compiled database query statement with an execution plan, andthe query optimizer adjusts the statistics in the trace information to tune the database query language statement, by using one or more statistics adjustment hints.
  • 2. The article of claim 1, the tuning information further comprising: tracing information collected during one or more previous executions of the statement.
  • 3. The article of claim 1, the tuning information further comprising: an outline to persistently represent a stored execution plan of the statement.
  • 4. The article of claim 1, the tuning information further comprising: a profile to persistently represent tuning hints to influence a generation of the execution plan of the statement.
  • 5. The article of claim 1, the computer readable storage medium further comprising: one or more SQL statements stored in a second memory location that is independent of a memory location storing the tuning base.
  • 6. The article of claim 1, wherein the signature for the statement is based on a normalized text of the statement.
  • 7. The article of claim 1, wherein the database query language statement is a SQL statement.
  • 8. A computer implemented method for retrieving tuning information for a database query language statement, the method comprising: receiving a signature, the signature being generated based on a database query language statement during compilation of the database query language statement, wherein the signature allows an optimizer at a database server to identify trace information for the database query language statement for retrieving the tuning information during the compilation of the database query language statement, andthe trace information comprises statistics of a database object for the database query language statement;determining, by using a processor, whether trace information for the database query language statement is available based at least in part upon the signature;tuning the database query language statement at least by retrieving the tuning information based at least in part upon the signature for the database query language statement from a tuning base, and further by modifying a compiled database query statement of the database query language statement to include at least a part of the tuning information for execution of the compiled database query statement with an execution plan; andadjusting the statistics in the trace information, which is used to further tune the database query language statement, by using one or more statistics adjustment hints.
  • 9. The method of claim 8, the tuning information further comprising: tracing information collected during one or more previous executions of the statement.
  • 10. The method of claim 8, the tuning information further comprising: an outline to persistently represent a stored execution plan of the statement.
  • 11. The method of claim 8, the tuning information further comprising: a profile to persistently represent tuning hints to influence a generation of the execution plan of the statement.
  • 12. The method of claim 8, wherein one or more SQL statements are stored in a memory location that is independent of a memory location storing the tuning base.
  • 13. The method of claim 8, wherein the signature for the statement is based on a normalized text of the statement.
  • 14. The method of claim 8, wherein the statement is an SQL statement.
  • 15. The method of claim 8, wherein determining whether trace information for the database query language statement is available comprises: mapping the signature to a lookup table.
  • 16. The computer implemented method of claim 8, wherein the act of determining whether the trace information is available is performed by using at least a negative identification case.
  • 17. The computer implemented method of claim 8, wherein at least a part of the tuning information is copied into the database query language statement.
  • 18. The computer implemented method of claim 8, wherein the tuning information comprises a profile which provides one or more tuning functions for tuning the database query statement.
  • 19. The computer implemented method of claim 8, wherein the tuning information comprises an outline which provides execution plan editing capability.
  • 20. A system for retrieving tuning information for a database query language statement, the system comprising: a hardware processor for performing: receiving a signature, the signature being generated based on a database query language statement during compilation of the database query language statement, wherein the signature allows an optimizer at a database server to identify trace information for the database query language statement for retrieving the tuning information during the compilation of the database query language statement, andthe trace information comprises statistics of a database object for the database query language statement;determining, by using a processor, whether trace information for the database query language statement is available based at least in part upon the signature;tuning the database query language statement at least by retrieving the tuning information based at least in part upon the signature for the database query language statement from a tuning base, and further by modifying a compiled database query statement of the database query language statement to include at least a part of the tuning information for execution of the compiled database query statement with an execution plan; andadjusting the statistics in the trace information, which is used to further tune the database query language statement, by using one or more statistics adjustment hints.
  • 21. The system of claim 20, the tuning information further comprising: tracing information collected during one or more previous executions of the statement.
  • 22. The system of claim 20, the tuning information further comprising: an outline to persistently represent a stored execution plan of the statement.
  • 23. The system of claim 20, the tuning information further comprising: a profile to persistently represent tuning hints to influence a generation of the execution plan of the statement.
  • 24. The system of claim 20, wherein one or more SQL statements are stored in a memory location that is independent of a memory location of the tuning base.
  • 25. The system of claim 20, wherein the signature for the statement is based on a normalized text of the statement.
  • 26. The system of claim 20, wherein the statement is an SQL statement.
  • 27. The system of claim 20, wherein the tuning information further comprising: means for mapping the signature to a lookup table.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 60/500,490, filed Sep. 6, 2003, which is incorporated herein by reference in its entirety. This application is related to co-pending applications “SQL TUNING SETS,” with U.S. application Ser. No. 10/936,449; “AUTO-TUNING SQL STATEMENTS,” with U.S. application Ser. No. 10/935,908; “SQL PROFILE,” with U.S. application Ser. No. 10/936,205; “GLOBAL HINTS,” with U.S. application Ser. No. 10/936,781; “HIGH LOAD SQL DRIVEN STATISTICS COLLECTION,” with U.S. application Ser. No. 10/936,427; “AUTOMATIC LEARNING OPTIMIZER,” with U.S. application Ser. No. 10/935,906; “AUTOMATIC PREVENTION OF RUN-AWAY QUERY EXECUTION,” with U.S. application Ser. No. 10/936,779; “METHOD FOR INDEX TUNING OF A SQL STATEMENT, AND INDEX MERGING FOR A MULTI-STATEMENT SQL WORKLOAD, USING A COST-BASED RELATIONAL QUERY OPTIMIZER,” with U.S. application Ser. No. 10/936,469; “SQL STRUCTURE ANALYZER,” with U.S. application Ser. No. 10/936,426; “AUTOMATIC SQL TUNING ADVISOR,” with U.S. application Ser. No. 10/936,778, all of which are filed Sep. 7, 2004 and are incorporated herein by reference in their entirety.

US Referenced Citations (134)
Number Name Date Kind
5140685 Sipple et al. Aug 1992 A
5260697 Barrett et al. Nov 1993 A
5398183 Elliott Mar 1995 A
5408653 Josten et al. Apr 1995 A
5481712 Silver et al. Jan 1996 A
5504917 Austin Apr 1996 A
5544355 Chaudhuri et al. Aug 1996 A
5577240 Demers et al. Nov 1996 A
5634134 Kumai et al. May 1997 A
5724569 Andres Mar 1998 A
5737601 Jain et al. Apr 1998 A
5761660 Josten et al. Jun 1998 A
5765159 Srinivasan Jun 1998 A
5781912 Demers et al. Jul 1998 A
5794227 Brown Aug 1998 A
5794229 French et al. Aug 1998 A
5806076 Ngai et al. Sep 1998 A
5860069 Wright Jan 1999 A
5870760 Demers et al. Feb 1999 A
5870761 Demers et al. Feb 1999 A
5940826 Heideman et al. Aug 1999 A
5963933 Cheng et al. Oct 1999 A
5963934 Cochrane et al. Oct 1999 A
5991765 Vethe Nov 1999 A
6052694 Bromberg Apr 2000 A
6122640 Pereira Sep 2000 A
6195653 Bleizeffer et al. Feb 2001 B1
6212514 Eberhard et al. Apr 2001 B1
6275818 Subramanian et al. Aug 2001 B1
6321218 Guay et al. Nov 2001 B1
6330552 Farrar et al. Dec 2001 B1
6349310 Klein et al. Feb 2002 B1
6353818 Carino, Jr. Mar 2002 B1
6356889 Lohman et al. Mar 2002 B1
6366901 Ellis Apr 2002 B1
6366903 Agrawal et al. Apr 2002 B1
6374257 Guay et al. Apr 2002 B1
6397207 Bleizeffer et al. May 2002 B1
6397227 Klein et al. May 2002 B1
6434545 MacLeod et al. Aug 2002 B1
6434568 Bowman-Amuah Aug 2002 B1
6442748 Bowman-Amuah Aug 2002 B1
6460027 Cochrane et al. Oct 2002 B1
6460043 Tabbara et al. Oct 2002 B1
6493701 Ponnekanti Dec 2002 B2
6496850 Bowman-Amuah Dec 2002 B1
6513029 Agrawal et al. Jan 2003 B1
6529901 Chaudhuri et al. Mar 2003 B1
6560606 Young May 2003 B1
6571233 Beavin et al. May 2003 B2
6594653 Colby et al. Jul 2003 B2
6598038 Guay et al. Jul 2003 B1
6615223 Shih et al. Sep 2003 B1
6701345 Carley et al. Mar 2004 B1
6714943 Ganesh et al. Mar 2004 B1
6721724 Galindo-Legaria et al. Apr 2004 B1
6728719 Ganesh et al. Apr 2004 B1
6728720 Lenzie Apr 2004 B1
6744449 MacLeod et al. Jun 2004 B2
6763353 Li et al. Jul 2004 B2
6804672 Klein et al. Oct 2004 B1
6816874 Cotner et al. Nov 2004 B1
6839713 Shi et al. Jan 2005 B1
6850925 Chaudhuri et al. Feb 2005 B2
6865567 Oommen et al. Mar 2005 B1
6910109 Holman et al. Jun 2005 B2
6912547 Chaudhuri et al. Jun 2005 B2
6915290 Bestgen et al. Jul 2005 B2
6931389 Bleizeffer et al. Aug 2005 B1
6934701 Hall, Jr. Aug 2005 B1
6947927 Chaudhuri et al. Sep 2005 B2
6961931 Fischer Nov 2005 B2
6999958 Carlson et al. Feb 2006 B2
7007013 Davis et al. Feb 2006 B2
7031958 Santosuosso Apr 2006 B2
7047231 Grasshoff et al. May 2006 B2
7058622 Tedesco Jun 2006 B1
7080062 Leung et al. Jul 2006 B1
7139749 Bossman et al. Nov 2006 B2
7146363 Waas et al. Dec 2006 B2
7155426 Al-Azzawe Dec 2006 B2
7155459 Chaudhuri et al. Dec 2006 B2
7174328 Stanoi et al. Feb 2007 B2
7272589 Guay et al. Sep 2007 B1
7302422 Bossman et al. Nov 2007 B2
7353219 Markl et al. Apr 2008 B2
7617201 Bedell et al. Nov 2009 B1
20020073086 Thompson et al. Jun 2002 A1
20020120617 Yoshiyama et al. Aug 2002 A1
20020198867 Lohman et al. Dec 2002 A1
20030018618 Bestgen et al. Jan 2003 A1
20030065648 Driesch et al. Apr 2003 A1
20030088541 Zilio et al. May 2003 A1
20030093408 Brown et al. May 2003 A1
20030110153 Shee Jun 2003 A1
20030115183 Abdo et al. Jun 2003 A1
20030126143 Roussopoulos et al. Jul 2003 A1
20030130985 Driesen et al. Jul 2003 A1
20030135478 Marshall et al. Jul 2003 A1
20030154216 Arnold et al. Aug 2003 A1
20030177137 MacLeod et al. Sep 2003 A1
20030182276 Bossman et al. Sep 2003 A1
20030187831 Bestgen et al. Oct 2003 A1
20030200204 Limoges et al. Oct 2003 A1
20030200537 Barsness et al. Oct 2003 A1
20030229621 Carlson et al. Dec 2003 A1
20030229639 Carlson et al. Dec 2003 A1
20030236782 Wong et al. Dec 2003 A1
20040002957 Chaudhuri et al. Jan 2004 A1
20040003004 Chaudhuri et al. Jan 2004 A1
20040010488 Chaudhuri et al. Jan 2004 A1
20040019587 Fuh et al. Jan 2004 A1
20040034643 Bonner et al. Feb 2004 A1
20040181521 Simmen et al. Sep 2004 A1
20040210563 Zait et al. Oct 2004 A1
20040215626 Colossi et al. Oct 2004 A1
20050033734 Chess et al. Feb 2005 A1
20050097078 Lohman et al. May 2005 A1
20050097091 Ramacher et al. May 2005 A1
20050102305 Chaudhuri et al. May 2005 A1
20050119999 Zait et al. Jun 2005 A1
20050120000 Ziauddin et al. Jun 2005 A1
20050120001 Yagoub et al. Jun 2005 A1
20050125393 Yagoub et al. Jun 2005 A1
20050125398 Das et al. Jun 2005 A1
20050125427 Dageville et al. Jun 2005 A1
20050125452 Ziauddin et al. Jun 2005 A1
20050138015 Dageville et al. Jun 2005 A1
20050177557 Ziauddin et al. Aug 2005 A1
20050187917 Lawande et al. Aug 2005 A1
20050251523 Rajamani et al. Nov 2005 A1
20060004828 Rajamani et al. Jan 2006 A1
20060167883 Boukobza Jul 2006 A1
20070038618 Kosciusko et al. Feb 2007 A1
Non-Patent Literature Citations (92)
Entry
Aboulnaga, A. et al. “Self-tuning Histograms: Building Histograms Without Looking at Data”, Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD'99, Philadelphia, PA, 1999, pp. 181-192.
Almeida, et al., “Panasync: Dependency tracking among file copies”, Proceedings of the 9th Workshop on ACM SIGOPS European Workshop: Beyond the PC: New Challenges for the Operating System, Kolding, Denmark, 2000, pp. 7-12.
Baldoni, et al., “A Communication-Induced Checkpointing Protocol that Ensures Rollback-Dependency Trackability”, 27th Annual International Symposium on Fault-Tolerant Computing, FTCS-27, IEEE, 1997, pp. 68-77.
Baldoni, et al., “Rollback-Dependency Trackability: Visible Characterizations”, Proceedings of the 18th Annual ACM Symposium on Principles of Distributed Computing, Atlanta, GA, 1999, pp. 33-42.
Damani, et al, “Optimistic Distributed Simulation Based on Transitive Dependency Tracking”, Proceedings of the 11th Workshop on Parellel and Distributed Simulation, IEEE, 1997, pp. 90-97.
Elnozahy, “On the Relevance of Communication Costs of Rollback-Recovery Protocols”, Proceedings of the 14th Annual ACM Symposium on Principles of Distributed Computing, Ottawa, Ontario, Canada, 1995, pp. 74-79.
Garcia, et al., “On the Minimal Characterization of the Rollback-Dependency Trackability Property”, 21st International Conference on Distributed Computing Systems, IEEE, Apr. 16-19, 2001, pp. 342-349.
Graefe, G. “Dynamic Query Evaluation Plans: Some Course Corrections?”, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, Jun. 2000, vol. 23, No. 2, pp. 3-6.
Hellerstein, J.M. et al. “Adaptive Query Processing: Technology in Evolution”, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, Jun. 2000, vol. 23, No. 2, pp. 7-18.
Kabra, N. et al. “Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans”, Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, SIGMOD'98, Seattle, WA, 1998, pp. 106-117.
Louboutin, et al., “Comprehensive Distributed Garbage Collection by Tracking Causal Dependencies of Relevant Mutator Events”, Proceedings of the 17th International Conference on Distributed Computing Systems, IEEE, May 27-30, 1997, pp. 516-525.
Perry, “Consumer Electronics”, IEEE Spectrum, Jan. 1997, vol. 34, No. 1, pp. 43-48.
Sadri, “Integrity Constraints in the Information Source Tracking Method”, IEEE Transactions on Knowledge and Data, Feb. 1995, vol. 7, Issue 1, pp. 106-119.
Sreenivas, et al., “Independent Global Snapshots in Large Distributed Systems”, Proceedings of the 4th International Conference on High Performance Computing, IEEE, Dec. 18-21, 1997, pp. 462-467.
Office Action dated Apr. 20, 2007 for U.S. Appl. No. 10/936,449.
Office Action dated Apr. 19, 2007 for U.S. Appl. No. 10/936,205.
Office Action dated Sep. 6, 2007 for U.S. Appl. No. 10/936,205.
Office Action dated Feb. 7, 2007 for U.S. Appl. No. 10/936,781.
Office Action dated Jul. 30, 2007 for U.S. Appl. No. 10/936,781.
Office Action dated Jan. 24, 2007 for U.S. Appl. No. 10/936,779.
Office Action dated Aug. 22, 2007 for U.S. Appl. No. 10/936,779.
Office Action dated Jan. 25, 2007 for U.S. Appl. No. 10/936,778.
Avnur, R. et al. “Eddies: Continuously Adaptive Query Processing” Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (SIGMOD '00), Dallas, TX, May 15-18, 2000, pp. 261-272.
Blakeley, J.A. et al. “Experiences Building the Open OODB Query Optimizer” Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (SIGMOD '93), Washington, DC, May 25-28, 1993, pp. 287-296.
Bruno, N. et al. “STHoles: A Multidimensional Workload-Aware Histogram” Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data (SIGMOD '01), Santa Barbara, CA, May 21-24, 2001, pp. 211-222.
Bruno, N. et al. “Exploiting Statistics on Query Expressions for Optimization” Proceedings of the 2002 ACM SIGMOD International Conference on Data Management (SIGMOD '02), Madison, WI, Jun. 4-6, 2002, pp. 263-274.
Chaudhuri, S. “An Overview of Query Optimization in Relational Systems” Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS '98), Seattle, WA, Jun. 1-4, 1998, pp. 34-43.
Chaudhuri, S. et al. “Rethinking Database System Architecture: Towards a Self-Tuning RISC-style Database System” Proceedings of the 26th International Conference on Very Large Databases (VLDB 2000), Cairo, Egypt, Sep. 10-14, 2000, pp. 1-10.
Chen, C.M. et al. “Adaptive Selectivity Estimation Using Query Feedback” Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data (SIGMOD '94), Minneapolis, MN, May 24-27, 1994, pp. 161-172.
Derr, M.A. “Adaptive Query Optimization in a Deductive Database System” Proceedings of the 2nd International Conference on Information and Knowledge Management (CIKM '93), Washington, DC, Nov. 1-5, 1993, pp. 206-215.
Ganek, A.G. et al. “The dawning of the autonomic computing era” IBM Systems Journal, 2003, vol. 42, No. 1, pp. 5-18.
Gassner, P. et al. “Query Optimization in the IBM DB2 Family” Data Engineering, Dec. 1993, vol. 16, No. 4, pp. 4-18.
Getoor, L. et al. “Selectivity Estimation using Probabilistic Models” Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data (SIGMOD '01), Santa Barbara, CA, May 21-24, 2001, pp. 461-472.
Gorman, T. “The Search for Intelligent Life in the Cost-Based Optimizer” Jul. 2001, v1.0, pp. 1-11.
IBM “DB2 Universal Database Slashes Administration Costs by Reducing Time Spent on Administrative Tasks by up to 65 Percent” MarketWire, Sep. 9, 2004, pp. 1-3, located at http://www.marketwire.com/mw/release—html-b1?release—id=72387.
Ives, Z.G. et al. “An Adaptive Query Execution System for Data Integration” Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data (SIGMOD '99), Philadelphia, PA, Jun. 1-3, 1999, pp. 299-310.
Lightstone, S.S. et al. “Toward Autonomic Computing with DB2 Universal Database” ACM SIGMOD Record, Sep. 2002, vol. 31, No. 3, pp. 55-61.
Markl, V. et al. “LEO: An autonomic query optimizer for DB2” IBM Systems Journal, 2003, vol. 42, No. 1, pp. 98-106.
Scheuermann, P. et al. “Adaptive Algorithms for Join Processing in Distributed Database Systems” Distributed and Parallel Databases, 1997, vol. 5, pp. 233-269.
Slivinskas, G. et al. “Adaptable Query Optimization and Evaluation in Temporal Middleware” Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data (SIGMOD '01), Santa Barbara, CA, May 21-24, 2001, pp. 127-138.
Valentin, G. et al. “DB2 Advisor: An Optimizer Smart Enough to Recommend Its Own Indexes” Proceedings of the 16th International Conference on Data Engineering, Feb. 29-Mar. 3, 2000, pp. 101-110.
Zilio, D. et al. “Self-Managing Technology in IBM DB2 Universal Database8” Proceedings of the 10th International Conference on Information and Knowledge Management (CIKM '01), Atlanta, GA, Nov. 5-10, 2001, pp. 541-543.
Ioannidis et al, “Parametric Query Optimization”, Proceedings of the 18 VLDB Conference, Vancouver, BC, Canada 1992, pp. 103-114.
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Office Action dated Jan. 22, 2009 for U.S. Appl. No. 10/936,778.
Markl et al. “Learning Table Access Cardinalities with LEO” SIGMOD '02, Jun. 3-6, 2002, p. 613.
Stillger et al. “LEO—DB2's Learning Optimizer” VLDB 2001.
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Related Publications (1)
Number Date Country
20050097091 A1 May 2005 US
Provisional Applications (1)
Number Date Country
60500490 Sep 2003 US