Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
Greenplum
Gpdb
提交
596731dc
G
Gpdb
项目概览
Greenplum
/
Gpdb
通知
7
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
DevOps
流水线
流水线任务
计划
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
G
Gpdb
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
DevOps
DevOps
流水线
流水线任务
计划
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
流水线任务
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
596731dc
编写于
7月 29, 1998
作者:
T
Thomas G. Lockhart
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Information moved to doc/src/sgml/geqo.sgml.
上级
be8300b1
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
0 addition
and
219 deletion
+0
-219
doc/README.GEQO
doc/README.GEQO
+0
-160
doc/TODO.GEQO
doc/TODO.GEQO
+0
-59
未找到文件。
doc/README.GEQO
已删除
100644 → 0
浏览文件 @
be8300b1
=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*
Genetic Query Optimization in Database Systems
=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*
Martin Utesch
<utesch@aut.tu-freiberg.de>
Institute of Automatic Control
University of Mining and Technology
Freiberg, Germany
02/10/1997
1.) Query Handling as a Complex Optimization Problem
====================================================
Among all relational operators the most difficult one to process and
optimize is the JOIN. The number of alternative plans to answer a query
grows exponentially with the number of JOINs included in it. Further
optimization effort is caused by the support of a variety of *JOIN
methods* (e.g., nested loop, index scan, merge join in Postgres) to
process individual JOINs and a diversity of *indices* (e.g., r-tree,
b-tree, hash in Postgres) as access paths for relations.
The current Postgres optimizer implementation performs a *near-
exhaustive search* over the space of alternative strategies. This query
optimization technique is inadequate to support database application
domains that evolve the need for extensive queries, such as artifcial
intelligence.
The Institute of Automatic Control at the University of Mining and
Technology Freiberg, Germany encountered the described problems as its
folks wanted to take the Postgres DBMS as the backend for a decision
support knowledge based system for the maintenance of an electrical
power grid. The DBMS needed to handle large JOIN queries for the
inference machine of the knowledge based system.
Performance difficulties within exploring the space of possible query
plans arose the demand for a new optimization technique being developed.
In the following we propose the implementation of a *Genetic
Algorithm* as an option for the database query optimization problem.
2.) Genetic Algorithms (GA)
===========================
The GA is a heuristic optimization method which operates through
determined, randomized search. The set of possible solutions for the
optimization problem is considered as a *population* of *individuals*.
The degree of adaption of an individual to its environment is specified
by its *fitness*.
The coordinates of an individual in the search space are represented
by *chromosomes*, in essence a set of character strings. A *gene* is a
subsection of a chromosome which encodes the value of a single parameter
being optimized. Typical encodings for a gene could be *binary* or
*integer*.
Through simulation of the evolutionary operations *recombination*,
*mutation*, and *selection* new generations of search points are found
that show a higher average fitness than their ancestors.
According to the "comp.ai.genetic" FAQ it cannot be stressed too
strongly that a GA is not a pure random search for a solution to a
problem. A GA uses stochastic processes, but the result is distinctly
non-random (better than random).
Structured Diagram of a GA:
---------------------------
P(t) generation of ancestors at a time t
P''(t) generation of descendants at a time t
+=========================================+
|>>>>>>>>>>> Algorithm GA <<<<<<<<<<<<<<|
+=========================================+
| INITIALIZE t := 0 |
+=========================================+
| INITIALIZE P(t) |
+=========================================+
| evalute FITNESS of P(t) |
+=========================================+
| while not STOPPING CRITERION do |
| +-------------------------------------+
| | P'(t) := RECOMBINATION{P(t)} |
| +-------------------------------------+
| | P''(t) := MUTATION{P'(t)} |
| +-------------------------------------+
| | P(t+1) := SELECTION{P''(t) + P(t)} |
| +-------------------------------------+
| | evalute FITNESS of P''(t) |
| +-------------------------------------+
| | t := t + 1 |
+===+=====================================+
3.) Genetic Query Optimization (GEQO) in PostgreSQL
===================================================
The GEQO module is intended for the solution of the query
optimization problem similar to a traveling salesman problem (TSP).
Possible query plans are encoded as integer strings. Each string
represents the JOIN order from one relation of the query to the next.
E. g., the query tree /\
/\ 2
/\ 3
4 1 is encoded by the integer string '4-1-3-2',
which means, first join relation '4' and '1', then '3', and
then '2', where 1, 2, 3, 4 are relids in PostgreSQL.
Parts of the GEQO module are adapted from D. Whitley's Genitor
algorithm.
Specific characteristics of the GEQO implementation in PostgreSQL
are:
o usage of a *steady state* GA (replacement of the least fit
individuals in a population, not whole-generational replacement)
allows fast convergence towards improved query plans. This is
essential for query handling with reasonable time;
o usage of *edge recombination crossover* which is especially suited
to keep edge losses low for the solution of the TSP by means of a GA;
o mutation as genetic operator is deprecated so that no repair
mechanisms are needed to generate legal TSP tours.
The GEQO module gives the following benefits to the PostgreSQL DBMS
compared to the Postgres query optimizer implementation:
o handling of large JOIN queries through non-exhaustive search;
o improved cost size approximation of query plans since no longer
plan merging is needed (the GEQO module evaluates the cost for a
query plan as an individual).
References
==========
J. Heitk"otter, D. Beasley:
---------------------------
"The Hitch-Hicker's Guide to Evolutionary Computation",
FAQ in 'comp.ai.genetic',
'ftp://ftp.Germany.EU.net/pub/research/softcomp/EC/Welcome.html'
Z. Fong:
--------
"The Design and Implementation of the Postgres Query Optimizer",
file 'planner/Report.ps' in the 'postgres-papers' distribution
R. Elmasri, S. Navathe:
-----------------------
"Fundamentals of Database Systems",
The Benjamin/Cummings Pub., Inc.
doc/TODO.GEQO
已删除
100644 → 0
浏览文件 @
be8300b1
=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=
* Things left to done for the PostgreSQL *
= Genetic Query Optimization (GEQO) =
* module implementation *
=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=
* Martin Utesch * Institute of Automatic Control *
= = University of Mining and Technology =
* utesch@aut.tu-freiberg.de * Freiberg, Germany *
=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=
1.) Basic Improvements
===============================================================
a) improve freeing of memory when query is already processed:
-------------------------------------------------------------
with large JOIN queries the computing time spent for the genetic query
optimization seems to be a mere *fraction* of the time Postgres
needs for freeing memory via routine 'MemoryContextFree',
file 'backend/utils/mmgr/mcxt.c';
debugging showed that it get stucked in a loop of routine
'OrderedElemPop', file 'backend/utils/mmgr/oset.c';
the same problems arise with long queries when using the normal
Postgres query optimization algorithm;
b) improve genetic algorithm parameter settings:
------------------------------------------------
file 'backend/optimizer/geqo/geqo_params.c', routines
'gimme_pool_size' and 'gimme_number_generations';
we have to find a compromise for the parameter settings
to satisfy two competing demands:
1. optimality of the query plan
2. computing time
c) find better solution for integer overflow:
---------------------------------------------
file 'backend/optimizer/geqo/geqo_eval.c', routine
'geqo_joinrel_size';
the present hack for MAXINT overflow is to set the Postgres integer
value of 'rel->size' to its logarithm;
modifications of 'struct Rel' in 'backend/nodes/relation.h' will
surely have severe impacts on the whole PostgreSQL implementation.
d) find solution for exhausted memory:
--------------------------------------
that may occur with more than 10 relations involved in a query,
file 'backend/optimizer/geqo/geqo_eval.c', routine
'gimme_tree' which is recursively called;
maybe I forgot something to be freed correctly, but I dunno what;
of course the 'rel' data structure of the JOIN keeps growing and
growing the more relations are packed into it;
suggestions are welcome :-(
2.) Further Improvements
===============================================================
Enable bushy query tree processing within PostgreSQL;
that may improve the quality of query plans.
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录