CN100397396C - Query plan caching method and system based on predicate criticality analysis - Google Patents
Query plan caching method and system based on predicate criticality analysis Download PDFInfo
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Abstract
The invention provides a query plan caching method and a query plan caching system based on predicate criticality analysis. In the invention, key conditions appearing in a query are divided into predicates, a predicate analyzer determines the influence of each predicate on a query plan according to query statistical information, analyzes the weight of the predicate analyzer and finally determines whether the predicates influence the query plan, and if the predicates do not influence the query plan, even if the query statement input by a user is inconsistent with the existing statement in a query cache region in the number of the predicates, the predicate analyzer considers that the query statement can be matched with the existing cache plan, thereby improving the cache hit rate.
Description
Technical field
The present invention relates to a kind of method and system thereof of inquiry plan buffer memory, relate in particular to a kind of inquiry plan caching method and system thereof, belong to database technical field based on the predicate key degree analyzing.
Background technology
A major function of Database Systems is to carry out the user fast to operations such as the inquiry of data and renewals, yet from the SQL statement of user's input must just can become can be directly to the visit on the physical store through transforming.Therefore, query optimizer is introduced into to finish this process.Yet, for a complicated query, various inquiry executive mode often being arranged, this can cause query optimizer to expend the too much time and be used for SQL statement optimization, under opposite extreme situations, even might surpass time of inquiry execution itself.Therefore, query caching is widely used in each database as a technology, in order to reduce the time of query optimization, its basic thought be former used query caching in internal memory, only 2 in the constant (as shown in the formula i) wherein replaced to variable element), when new inquiry input, if energy and original match query (only variable element is different), so just do not need, and reuse original inquiry plan for it regenerates inquiry plan.Can use the inquiry of original inquiry plan to be called as and hit, hit rate is high more, means that the efficient of inquiry plan caching system is high more.
Yet there are a lot of weak point defectives in existing query caching technology, and two main problems are wherein arranged.
Problem a) can only support can carry out fully those statements of parameter matching, such as following three simple SQL statement formulas:
select?*from?T?where?T.cl＝2 i)
select?*from?T?where?T.cl＝3 ii)
select?*from?T?where?T.cl＝3?and?T.c2＝3 iii)
Wherein, i) and ii) can mate, but at structure and statement inequality, i for example) and iii) but can not mate, although in cache pool, there has been i before) plan, but because i) with iii) between predicate number and different, so the i that preserves) inquiry plan can not must regenerate for iii) used.
In traditional OLTP (Transaction Processing) used, the difference between the query statement is the difference of constant parameter often, i.e. similar the and i of the difference of statement) and ii), use traditional program cache mode, can reach very high hit rate; But use the similar often i of the difference of query statement for emerging OLAP (on line data analysis)) formula and the iii) difference of formula, in this class is used, the hit rate of program cache system will descend rapidly, even approach 0.
Problem b) in some cases, even two inquiries are mated fully, can not guarantee that the inquiry plan in the buffer memory can be applied to a back inquiry.With i) be example, suppose on the c1 of T, to have an index T_C1, and the row of the C1=2 that satisfies condition among the table T is few, the plan of Sheng Chenging will be an index scan operation so:
Index?Scan?using?T_C1?on?T?Index?Cond：(C1＝2)
When the row among the table T satisfies C1=3 in a large number, for the plan that ii) generates should be a common following table scan.
Seq?Scan?on?T Filter：(C1＝3)
But program cache tends to produce wrong coupling, causes generating unexcellent inquiry plan.
By a) and b) two problems can see, even seem similar condition, can not guarantee that one mates original plan surely, and seem different conditions that original plan can not match.Therefore, existing algorithm based on constant parameter can not guarantee best query caching matching effect, causes the efficient of inquiry plan buffer memory not high, even the efficient that wrong coupling influence inquiry is carried out occurs.
Summary of the invention
The objective of the invention is at the deficiencies in the prior art, a kind of new inquiry plan caching method and system thereof based on the predicate key degree analyzing are provided.This method and system thereof be the hit rate of promote query program cache significantly.
For realizing above-mentioned goal of the invention, the present invention adopts following technical scheme:
A kind of inquiry plan caching method based on the predicate key degree analyzing is characterized in that comprising the steps:
(1) table that uses in the analysis and consult;
(2) take out the inquiry plan buffer memory of showing;
(3) take out the inquiry plan buffer memory that can mate;
(4) difference of analysis and consult predicate;
(5) differ predicate?
(6) if judge whether further that then whether the predicate of influential index and crucial degree are greater than K; If not, then analyze existing predicate, judge that whether crucial degree is greater than K;
(7) the selectance difference of these predicates of comparison;
(8) do not influence under the prerequisite of inquiry plan return results in difference.
Before inquiry, at first grammatical analysis is carried out in inquiry, generative grammar is set, and its predicate that uses and tabulation are separated out.
Described predicate is divided into KEY and FILTER.
A kind of inquiry plan caching system based on the predicate key degree analyzing is characterized in that comprising:
Syntax analyzer: handle the SQL query statement of input, finish syntax check, generation can supply the syntax tree structure of the standard of inter-process, with the inquiry of expression user input;
Query caching device: be responsible for the whole inquiry plan of buffer memory, and it is stored in the internal memory of system, by how much the managing and organizing that relates to physical arrangements such as table, index;
Query optimizer: according to the query statement of an input,, and actuator is exported in this plan carried out, output to simultaneously in the query caching device and store for it generates optimum plan;
The predicative analysis device: be responsible for according to query statistic information, the weight of the predicate in analysis and consult plan and the query statement determines whether plan depends on fixedly predicate;
Query executor: carry out inquiry plan, and carry out the feedback renewal of statistical information in the process of implementation;
Statistical information processor: be responsible for maintenance, renewal, the use of statistical information;
Parse tree after wherein the SQL statement of transmission user input is changed between syntax analyzer and the predicative analysis device; Transmit the inquiry plan that generates between query optimizer and the query caching device; Transmit the inquiry plan that generates between query optimizer and the predicative analysis device; Transmit the inquiry plan that generates between query optimizer and the query executor; Real-time statistics information when transmitting the inquiry execution between query executor and the statistical information processor; Transmit statistical information between statistical information processor and the predicative analysis device; Transmit the inquiry plan of finishing by coupling between predicative analysis device and the query optimizer; Transmit the inquiry plan of coupling between query caching device and the predicative analysis device.
Determine the degree of influencing of predicate in the predicative analysis device in the following sequence to inquiry plan:
1. whether be the Major key that optimizer is determined the unique index of use;
2. whether be the key assignments that optimizer is determined the simple index of use;
3. whether be the part that optimizer is determined unique many-valued index of use;
4. whether be the part that optimizer is determined the common many-valued index of use.
Inquiry plan caching method and system thereof based on the predicate key degree analyzing provided by the present invention has the following advantages:
1. promoted the hit rate of inquiry plan buffer memory, overcome technology in the past and can not support the phenomenon that can't mate actual available inquiry plan buffer memory that causes under the predicate number different situations in the query statement, particularly being fit to new O LAP uses, thereby improved the speed of query processing, promoted query performance.
2. by the process of predicative analysis, having removed those does not have influence or influence less than the predicate of a threshold value for the crucial degree of inquiry influence, has reduced unnecessary inquiry plan cache match predicate, has accelerated matching speed.
3. pass through managing of inquiry plan buffer memory, improved the speed of inquiry plan buffer memory visit itself based on the involved table sort of inquiry.
Description of drawings
The present invention is further illustrated below in conjunction with the drawings and specific embodiments.
Fig. 1 is the synoptic diagram of the inquiry plan caching system based on the predicate key degree analyzing of the present invention.
Fig. 2 is the synoptic diagram of inquiry plan.
Fig. 3 is the process flow diagram of the inquiry plan caching method based on the predicate key degree analyzing of the present invention.
Fig. 4 is the interface synoptic diagram as certain women of child-bearing age's information database of specific embodiment.
Embodiment
Core technology thought of the present invention is by the key condition that occurs in the inquiry being divided into predicate one by one, determine of the influence of each predicate by the predicative analysis device according to query statistic information to inquiry plan, analyze its weight, whether these predicates of final decision can influence inquiry plan, if do not influence, even the query statement of user input and the existing statement that is positioned at the query caching district are inconsistent on the predicate number so, also think and to mate existing buffer memory plan, thereby promote cache hit rate.
Realize above-mentioned technological thought, the present invention adopts query caching disposal system as shown in Figure 1 for this reason.This query caching disposal system is made of following key subsystem, and they all finish specific function:
1. syntax analyzer: handle the SQL query statement of input, finish syntax check, generation can be for the syntax tree structure of the standard of inter-process, with the inquiry of expression user input.
2. query caching device: be responsible for the whole inquiry plan of buffer memory, and it is stored in the internal memory of system, by how much the managing and organizing that relates to physical arrangements such as table, index.
3. query optimizer: according to the query statement of an input, be that it generates optimum plan, and actuator exported in this plan carried out, output to simultaneously in the query caching device and store.
4. predicative analysis device: be responsible for according to query statistic information, the weight of the predicate in analysis and consult plan and the query statement determines whether plan depends on fixedly predicate.
5. query executor: carry out inquiry plan, and the feedback of carrying out statistical information is in the process of implementation upgraded.
6. statistical information processor: be responsible for maintenance, renewal, the use of statistical information.
The information flow that transmits between each subsystem is as shown in table 1:
| The subsystem name | The subsystem name | Request and the information transmitted |
| Syntax analyzer | The predicative analysis device | Parse tree after the SQL statement conversion of user's input |
| Query optimizer | The query caching device | The inquiry plan that generates |
| Query optimizer | The predicative analysis device | The inquiry plan that generates |
| Query optimizer | Query executor | The inquiry plan that generates |
| Query executor | The statistical information processor | Real-time statistics information when inquiry is carried out |
| The statistical information processor | The predicative analysis device | Statistical information |
| The predicative analysis device | Query optimizer | The inquiry plan of finishing by coupling |
| The query caching device | The predicative analysis device | The inquiry plan of coupling |
Table 1
In this process, the weight of predicate was estimated during the part of most critical was to inquire about.Weight estimates that being divided into two parts carries out, and the first step is the carrying out when the plan of optimizer generated query, the secondth, when the matching inquiry plan, carry out.
The key of query caching be for new inquiry in existing inquiry plan buffer memory, find one the most suitable, traditional way is simple matched character string, and in our invention, then uses the mode of predicate coupling.At first grammatical analysis is carried out in inquiry, generative grammar is set, and its predicate that uses and tabulation are separated out.
When the plan of optimizer generated query, optimizer can use the some of them predicate, predicate is divided into KEY and FILTER, so-called KEY is meant those predicates that can be used for index, such as table T (I int, J int) have index T_I to be based upon on the T (I), so for example the expression formula of predicate (T.i＞5) is called as KEY, the such predicate in predicate T.K＞5 FITER that then is known as.KEY is bigger to the influence of inquiry plan, and FILTER then takes second place.For KEY, because the difference of index type, there is different ranks again in KEY, determines its degree of influence to inquiry plan in the predicative analysis device in the following sequence.
A) whether be the Major key that optimizer is determined the unique index of use.
B) whether be the key assignments that optimizer is determined the simple index of use.
C) whether be the part that optimizer is determined unique many-valued index of use.
D) whether be the part that optimizer is determined the common many-valued index of use.
For FILTER, mainly be the size that is embodied in selection rate (selectivity) to the influence of inquiry plan, i.e. comparison by remaining tuple behind this FILTER predicate and total input tuple.The Filter that selection rate is more little is big more to the influence of inquiry plan, otherwise then influence is more little.Because inquiry plan is a dendrogram as shown in Figure 2, inquiry at the bottom of setting is used for the upper strata owing to the result, therefore its KEY and FILTER are bigger to the influence of inquiry, just are greater than Filterll as Filter21 among Fig. 2 and KEY31 for the influence of inquiring about cost.Query optimizer is when calculating inquiry plan for the first time, the position that is positioned at according to selection rate and FILTER just, (this is the operation that the Cost-based query optimizer of standard can carry out for the influence function of inquiry plan cost can to define them, do not describe in detail), this is called the crucial degree of predicate.Then these predicates and crucial degree thereof are placed in the query caching device.
We are provided with the threshold k of an inquiry plan cost according to the concrete condition of system, and determine whether can both whether can mate, promptly whether can hit by following algorithm:
1) checks whether the table that relates in the inquiry plan in the table that relates in the query statement and the buffer memory is consistent, as inconsistent, can not mate so.
2) check to need the query statement and the inquiry plan in the buffer memory of coupling whether to differ one or several predicates,, to jump to the 6th so as not differing) step.If differ, enter next step so.
3) if these predicates are KEY, confirm so whether they have had influence on the selection of index, if having influence on, both can not hit these so.If do not influence, be equal to FILTER so and handle, enter next step.
4) for the KEY that does not influence and all FILTER, calculate their crucial degree, i.e. the extent of deviation that the inquiry cost is caused for inquiry plan.
5), mean that the two can mate, and does not handle for the predicate of crucial degree＜K.If all differ predicate key degree all＜K, just mean that this inquiry can mate, enter next step.Otherwise think to mate.
6) under all situations about can hit, because the constant of each predicate can change, need to check of the influence of its each predicate to inquiry plan, if crucial degree＞K's, the selectance when needing to calculate predicate so and using new constant and the difference of the selectance of original program cache, if selectance is as broad as long, just mean that this predicate mates, if it is bigger that selectance differs, just mean that the inquiry plan that calculates based on this predicate must replan, promptly can not mate.
Fig. 3 is the basic flow sheet of the explanation inquiry plan caching method based on the predicate key degree analyzing provided by the present invention.About each step of this flow process, detailed explanation is all arranged in the above description, do not illustrated one by one at this.
Being example with a concrete data base querying is below further specified the effect of this method and system thereof.This database is certain women of child-bearing age's information database, the women of child-bearing age have been deposited in the database for information about, and provide the function of carrying out custom-built query according to different dimensions by the user, promptly all SQL statement all are can be increased different querying conditions as required and carried out arbitrary combination and finish its inquiry by the user, as the date of birth, parent information condition, inside the plan birth index, tens conditions in city, place or the like, as shown in Figure 4.
Before using the present invention, because the predicate number of each statement is all unequal, the statement of generation is similar as follows:
Select distinctbasetable.bt_code, basetable.bt_bname, basetable.bt_birth_date, husband.hb_husband_name, husband.hb_birth_date, nursing.nu_sex, nursing.nu_birth_date, nursing.nu_report_date, nursing.nu_procreate, nursing.nu_attribute from (select * from basetable where basetable.bt_codelike ' 330605% ') as basetable left join (select * from nursing) as nursing on (nursing.nu_code=basetable.bt_code) and (nursing.nu_code like ' 330605% ') left join (select * from husband) as husband on (husband.hb_code=basetable.bt_code) and (husband.hb_code like ' 330605% ') where ((nursing.nu_report_date＞=' 20050101 ' and nursing.nu_report_date＜=' 20050531 ') and (nursing.nu_birth_date＜' 20050101 ') and (nursing.nu_is_last=' 1 ' or nu_code is null) andbasetable.bt_code like ' 330605% ' order by basetable.bt_code and (((the and not of nursing.nu_report_date＞=basetable.bt_indate) (basetable.bt_indate=")) or (basetable.bt_indate="))); Wherein the condition that occurs after where all is selectable, as " (nursing.nu_report_date＞=' 20050101 ' and nursing.nu_report_date＜=' 20050531 ') " (reporting the date between on January 1st, 2005 and 31 days Mays in 2005); " nursing.nu_birth_date＜' 20050101 " ' (date of birth was less than on January 1st, 2005); therefore nearly all query statement all can not hit in the inquiry buffering; unless all use identical predicate combination at every turn, its hit rate in the practical application test less than 10%.
Adopt after method of the present invention and the system thereof, through the practical application test, these inquiries can all cover by enough about 100 inquiry plans basically, and after operation a period of time, all inquiry plan cache hit rates have reached more than 90%.
More than the specific embodiment of the present invention has been carried out detailed explanation.For the those skilled in the art in present technique field, the various conspicuous change of under the situation of spirit that does not deviate from the method for the invention and claim scope it being carried out is all within protection scope of the present invention.
Claims (6)
1. the inquiry plan caching method based on the predicate key degree analyzing is characterized in that comprising the steps:
(1) table that uses in the analysis and consult;
(2) the inquiry plan buffer memory of the described table of taking-up;
(3) take out the inquiry plan buffer memory that can mate;
(4) difference of analysis and consult predicate;
(5) differ predicate?
(6) if judge whether further that then whether the predicate of influential index and crucial degree are greater than K; If not, then analyze existing predicate, judge that whether crucial degree is greater than K;
(7) the selectance difference of these predicates of comparison;
(8) do not influence under the prerequisite of inquiry plan return results in difference.
2. the inquiry plan caching method based on the predicate key degree analyzing as claimed in claim 1 is characterized in that:
Before inquiry, at first grammatical analysis is carried out in inquiry, generative grammar is set, and its predicate that uses and tabulation are separated out.
3. the inquiry plan caching method based on the predicate key degree analyzing as claimed in claim 1 is characterized in that:
Described predicate is divided into KEY and FILTER.
4. the inquiry plan caching method based on the predicate key degree analyzing as claimed in claim 1 is characterized in that further comprising the steps: in the described step (4)
(41) check whether the table that relates in the inquiry plan in the table that relates in the query statement and the buffer memory is consistent, as inconsistent, can not mate so;
(42) check to need the query statement and the inquiry plan in the buffer memory of coupling whether to differ one or several predicates,, to jump to (46) step so as not differing; If differ, enter (43) step so;
(43) if these predicates are KEY, confirm so whether they have had influence on the selection of index, if having influence on, both can not hit these so; If do not influence, be equal to FILTER so and handle, enter (44) step;
(44) for the KEY that does not influence and all FILTER, calculate their crucial degree, i.e. the extent of deviation that the inquiry cost is caused for inquiry plan;
(45) for the predicate of crucial degree＜K, do not handle; If all differ predicate key degree all＜K, enter (46) step; Otherwise think to mate;
(46) under all situations about can hit, check of the influence of its each predicate to inquiry plan, if crucial degree＞K's, the selectance when calculating predicate is used new constant and the difference of the selectance of original program cache, if selectance is as broad as long, mean that this predicate mates, bigger if selectance differs, just mean that the inquiry plan that calculates based on this predicate must replan, promptly can not mate.
5. inquiry plan caching system based on the predicate key degree analyzing is characterized in that comprising:
Syntax analyzer: handle the SQL query statement of input, finish syntax check, generation can supply the syntax tree structure of the standard of inter-process, with the inquiry of expression user input;
Query caching device: be responsible for the whole inquiry plan of buffer memory, and it is stored in the internal memory of system, by how much the managing and organizing that relates to physical arrangements such as table, index;
Query optimizer: according to the query statement of an input,, and actuator is exported in this plan carried out, output to simultaneously in the query caching device and store for it generates optimum plan;
The predicative analysis device: be responsible for according to query statistic information, the weight of the predicate in analysis and consult plan and the query statement determines whether plan depends on fixedly predicate;
Query executor: carry out inquiry plan, and carry out the feedback renewal of statistical information in the process of implementation;
Statistical information processor: be responsible for maintenance, renewal, the use of statistical information;
Parse tree after wherein the SQL statement of transmission user input is changed between syntax analyzer and the predicative analysis device; Transmit the inquiry plan that generates between query optimizer and the query caching device; Transmit the inquiry plan that generates between query optimizer and the predicative analysis device; Transmit the inquiry plan that generates between query optimizer and the query executor; Real-time statistics information when transmitting the inquiry execution between query executor and the statistical information processor; Transmit statistical information between statistical information processor and the predicative analysis device; Transmit the inquiry plan of finishing by coupling between predicative analysis device and the query optimizer; Transmit the inquiry plan of coupling between query caching device and the predicative analysis device.
6. the inquiry plan caching system based on the predicate key degree analyzing as claimed in claim 5 is characterized in that determining in the following sequence the degree of influencing of predicate to inquiry plan in the predicative analysis device:
(61) whether be the Major key that optimizer is determined the unique index of use;
(62) whether be the key assignments that optimizer is determined the simple index of use;
(63) whether be the part that optimizer is determined unique many-valued index of use;
(64) whether be the part that optimizer is determined the common many-valued index of use.
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| CN101059958B (en) * | 2007-03-15 | 2010-12-15 | 上海交通大学 | Coding and decoding method for speedup the quantitative data with the buffer memory |
| US8818991B2 (en) * | 2009-12-23 | 2014-08-26 | Pivotal Software, Inc. | Apparatus and method for analyzing query optimizer performance |
| CN101872325B (en) * | 2010-06-25 | 2012-08-29 | 北京航空航天大学 | Method for dynamically locating software bugs on basis of predicate execution sequences |
| US10810200B2 (en) | 2015-01-07 | 2020-10-20 | International Business Machines Corporation | Technology for join processing |
| CN105718593B (en) * | 2016-01-28 | 2019-04-16 | 长春师范大学 | A kind of database inquiry optimization method and system |
| CN107045542A (en) * | 2017-02-23 | 2017-08-15 | 上海赛可电子商务有限公司 | Data query system and querying method |
| CN108804473B (en) * | 2017-05-04 | 2022-02-11 | 华为技术有限公司 | Data query method, device and database system |
| CN108170775A (en) * | 2017-12-26 | 2018-06-15 | 上海新炬网络技术有限公司 | A kind of database SQL indexes dynamic optimization method |
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