CN108170775A - A kind of database SQL indexes dynamic optimization method - Google Patents
A kind of database SQL indexes dynamic optimization method Download PDFInfo
- Publication number
- CN108170775A CN108170775A CN201711430676.4A CN201711430676A CN108170775A CN 108170775 A CN108170775 A CN 108170775A CN 201711430676 A CN201711430676 A CN 201711430676A CN 108170775 A CN108170775 A CN 108170775A
- Authority
- CN
- China
- Prior art keywords
- sql
- predicate
- index
- data
- optimization method
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
- G06F16/2246—Trees, e.g. B+trees
Abstract
The invention discloses a kind of database SQLs to index dynamic optimization method, includes the following steps：Step S1：By configuring timing tasks, timed collection SQL resource service conditions establish resource baseline；Step S2：To the SQL resource service conditions after acquisition, the index situation of executive plan and predicate conditions is analyzed；Step S3：To the data of analysis into line trace, the optimal enforcement indexed accordingly if optimization threshold values is reached.Database SQL provided by the invention indexes dynamic optimization method, by carrying out comprehensive monitoring and analysis to SQL service conditions, and carries out analysis according to predicate conditions and realizes optimization, greatly improves SQL execution efficiencys；Optimized at SQL generation performance issue initial stages by system intervention, reducing fault rate and failure influences；The index analysis of SQL predicates is all to be automatically completed, and without human intervention, reduces artificial dependence and maloperation.
Description
Technical field
The present invention relates to a kind of database SQL processing method more particularly to a kind of database SQL index dynamic optimization sides
Method.
Background technology
Information technology has become a kind of vital productivity of telecommunications industry, and the quality of operation system directly influences enterprise
The condition of production of industry.Database layer is a wherein the most key ring in traditional forms of enterprises's framework at present, and efficient in database
SQL execution efficiencys are by the handling capacity for the system that greatlys improve.
Current environment considers that unreasonable Index Design consumes the index maintenance for generating additional DML statement generation,
The Index Design of only arranged keyword section before business is reached the standard grade.In system operation often because business demand change from
And a large amount of mutation SQL is generated, how dynamically completing optimiged index in the operational process of system will be particularly important.
In the case, one or more of following several schemes are generally taken with reference to next more or less solution system
SQL optimiged index problems in system operational process.
Fig. 1 is referred to, the prior art mainly has the following two kinds process flow：
1), using Index Analyze class third party softwares
Third party software (Index Analyze), it provides some and indexes the monitoring scheme used and then judge index
Whether design is reasonable.According to manufacturer's design, more or less there are little bit differents with scheme for specific monitor control index.
When the operation of certain class index reaches default monitoring threshold values, seriously put level policy with reference to configuration and alerted, ensured
Problem responds promptness, and manual intervention is needed to handle.
2), manual inspection is handled
The inspection of the service conditions such as SQL calling amount, executive plan is periodically carried out, and carry out according to service condition by artificial
Manual intervention handle, ensure database stabilization and efficiently.
With the continuous development of operation system, the business throughput in the unit interval has large-scale promotion, also leads in this way
The calling amount of SQL in database has been caused to ramp, has also necessarily led to the promotion required SQL execution efficiencys.Therefore to data
It is particularly important that library SQL carries out dynamic index optimization.The prior art has the following disadvantages：
1), type is various, and safety is poor
All kinds of IndexAnalyze systems are similar on current internet, but the monitor control index preciseness of each manufacturer and
Shi Xing, accuracy still have a large amount of problems and to the supports of various database platforms nor very friendly.
2), only play the role of monitoring
IndexAnalyze only plays the role of the monitoring of relative index service condition, when the business SQL newly to reach the standard grade generation property
During energy problem, business has been affected, it is impossible to avoid the generation of failure.
3) experience, is relied on
It is very high that requirement in database D BA is checked by artificial nucleus.Database D BA experiences are abundanter, then more can guarantee
Accuracy.Change for database is very careful, once fault incidence will be led into one by generating artificial maloperation
Step expands.
More than technology due to the mechanism of its realization, all has same one, when problem occurs, needs manually to be situated between
Enter analysis and operate, this series of operation needs a large amount of time, it is also desirable to which the engineer with rich experiences is complete by hand
Into processing, to a certain extent the time of troubleshooting be highly dependent on the profile of engineer and environment be familiar with
Degree.
Invention content
The technical problems to be solved by the invention are to provide a kind of database SQL index dynamic optimization method, being capable of the period
Property is acquired analysis to the related resources such as the SQL statement amount of being called of data store internal, predicate conditions, IO, CPU；According to
SQL statement consumes the analysis result of resource, and system dynamic takes optimiged index change scheme, avoids the generation of failure.
The present invention is to solve above-mentioned technical problem and the technical solution adopted is that provide a kind of database SQL index dynamic excellent
Change method, includes the following steps：Step S1：By configuring timing tasks, timed collection SQL resource service conditions establish resource
Baseline；Step S2：To the SQL resource service conditions after acquisition, the index situation of executive plan and predicate conditions is analyzed；Step
S3：To the data of analysis into line trace, the optimal enforcement indexed accordingly if optimization threshold values is reached.
Above-mentioned database SQL index dynamic optimization method, wherein, the configuring timing tasks in the step S1 include：
Establishment, cancellation, update, deletion, inquiry and the backstage scheduling operation of task；Setting task is primary execution or repeatedly cycle is held
Row, and pass through filter kernel resource, key message is added in into list, is recorded in information collection library.
Above-mentioned database SQL index dynamic optimization method, wherein, the process that the step S1 collects SQL resources is as follows：
Step S11：SQL bases operation information is collected, calls, take resource, including：Calling amount performs duration, SQL versions, logic reading
And physical read；Step S12：Filter condition in SQL is split, associated predicate field is analyzed, including predicate amount,
Predicate selectivity, predicate result set and predicate data skewness；Step S13：Execution route acquires, and collects SQL in data scanning
During the data access mode that uses, the data access mode includes the unique scanning of full table scan, index or index model
Enclose scanning.
Above-mentioned database SQL index dynamic optimization method, wherein, the step S2 includes the access path of analysis SQL
With predicate field selectivity, the step S3 establishes predicate index according to the automation of the selectivity of predicate field and completes manipulative indexing
Optimization and removal will be indexed according to the data access mode of predicate field and selectivity in vain.
Above-mentioned database SQL index dynamic optimization method, wherein, the Predicate selectivity f values calculate as follows：
Wherein, c1, c2, cN represent the record number of the predicate conditions, again according to the record number duplicate removal, obtain non-duplicate
It is worth, and the selectivity of the predicate conditions is obtained using non-duplicate value divided by the total number of records；When f values reach 30%, then root is judged
The data result collection filtered according to the predicate conditions is smaller, and establishes common b-tree indexed for the predicate field.
Above-mentioned database SQL index dynamic optimization method, wherein, the predicate data skewness calculates as follows：
D=distinct (Col)
F=[d1 { count (col) }, d2 { count (col) }, dN { count (col) }]
Wherein, d is the uniqueness data of the predicate field, by the distribution situations of data in classified calculating d and is arranged
Sequence is it can be learnt that whether data distribution is uniform, if there is a situation where abnormal dip；When predicate data skewness is more than default threshold
During value, composite index is established according to other predicate fields.
The present invention comparison prior art has following advantageous effect：Database SQL provided by the invention indexes dynamic optimization
Method by carrying out comprehensive monitoring and analysis to SQL service conditions, and carries out analysis according to predicate conditions and realizes optimization, significantly
Improve SQL execution efficiencys；Optimized at SQL generation performance issue initial stages by system intervention, reduce fault rate and event
Barrier influences；The index analysis of SQL predicates is all to be automatically completed, and without human intervention, reduces artificial dependence and maloperation.
Description of the drawings
Fig. 1 is existing database SQL index dynamic optimization flow charts；
Fig. 2 indexes dynamic optimization flow chart for database SQL of the present invention；
Fig. 3 indexes dynamic optimization system configuration diagram for database SQL of the present invention.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and examples.
Fig. 2 indexes dynamic optimization flow chart for database SQL of the present invention.
Fig. 2 is referred to, database SQL provided by the invention indexes dynamic optimization method, includes the following steps：
Step S1：By configuring timing tasks, timed collection SQL resource service conditions establish resource baseline；
Step S2：To the SQL resource service conditions after acquisition, the index situation of executive plan and predicate conditions is analyzed；
Step S3：To the data of analysis into line trace, the optimization indexed accordingly if optimization threshold values is reached is real
It applies.
Fig. 3 indexes dynamic optimization system configuration diagram for database SQL of the present invention.
Continuing with referring to Fig. 3, the present invention is broadly divided into four layers to realize, each layer realization and function is described in detail below.
1. initialization layer：
This layer is mainly responsible for the management of task, and establishment, cancellation, update, deletion, inquiry and the backstage for mainly having task are dispatched
Deng operation.Monitor task is defined, primary execution is can define or repeatedly cycle performs, by filter kernel resource, by key message
List is added in, is recorded in information collection library.
2. information collection layer：
SQL is collected using resource in order to carry out predicate index analysis, data are mainly in terms of such as following 3：
1st, SQL acquires resource, collects SQL bases operation information, the related resources such as calling, time-consuming.
● calling amount
● perform duration
● SQL versions
● logic is read
● physical read
2nd, SQL conditions are split, and the filter condition in SQL is split, associated predicate field is analyzed.
● predicate amount
● Predicate selectivity
● predicate result set
● predicate data skew
3rd, execution route acquires, and collects the data access mode that SQL is used during data scanning, and such as full table is swept
It retouches, index unique scanning, index range scanning etc..
● full table scan
● the unique scanning of index
● index range scans
3. data analysis layer：
By analyzing access path, the predicate field selectivity of SQL, to generate corresponding optimiged index suggestion.
3.1 Predicate selectivities are analyzed
Wherein, c1, c2, cN represent the record number of the predicate conditions, and non-duplicate value is obtained again according to the record number duplicate removal,
And the selectivity of the predicate conditions is obtained using non-duplicate value divided by the total number of records.
When f values reach 30%, when, this means that, the predicate word smaller according to the data result collection that the predicate conditions filter
Duan Shihe establishes common b-tree indexed.
3.2 data skewness are analyzed
To avoid the situation for occurring a small amount of null values class Anomalies of Ground Tilt on the basis of most of data are selectively good special
Independent analysis data skewness, formula are as follows：
D=distinct (Col)
F=[d1 { coumt (col) }, d2 { count (col) }, dN { count (col) }]
Wherein, d is the uniqueness data of the predicate field, by the distribution situations of data in classified calculating d and is arranged
Sequence is it can be learnt that whether data distribution is uniform, if there is a situation where abnormal dip.It can when the more serious data skew of appearance
It carries out establishing composite index according to other predicate fields.
4. scheme implementation level：
When SQL calls situation and predicate field selectivity, after data distribution situation analysis, scheme implementation level is born
Duty：
1. establishing predicate index according to the automation of the selectivity of predicate field, improve executive plan, when improving sql responses
Between.
2. removal will be indexed in vain according to the data access mode of predicate field and selectivity, reduce DML statement and bring
Index maintenance problem and reduce disk space consumption.
Although the present invention is disclosed as above with preferred embodiment, however, it is not to limit the invention, any this field skill
Art personnel, without departing from the spirit and scope of the present invention, when can make a little modification and it is perfect, therefore the present invention protection model
It encloses to work as and is subject to what claims were defined.
Claims (6)
1. a kind of database SQL indexes dynamic optimization method, which is characterized in that includes the following steps：
Step S1：By configuring timing tasks, timed collection SQL resource service conditions establish resource baseline；
Step S2：To the SQL resource service conditions after acquisition, the index situation of executive plan and predicate conditions is analyzed；
Step S3：To the data of analysis into line trace, the optimal enforcement indexed accordingly if optimization threshold values is reached.
2. database SQL as described in claim 1 indexes dynamic optimization method, which is characterized in that matching in the step S1
Timed task is put to include：Establishment, cancellation, update, deletion, inquiry and the backstage scheduling operation of task；Setting task is once holds
Row or repeatedly cycle execution, and pass through filter kernel resource, add in list by key message, are recorded in information collection library.
3. database SQL as described in claim 1 indexes dynamic optimization method, which is characterized in that the step S1 collects SQL
The process of resource is as follows：
Step S11：SQL bases operation information is collected, calls, take resource, including：Calling amount performs duration, SQL versions, patrols
Collect reading and physical read；
Step S12：Filter condition in SQL is split, associated predicate field is analyzed, including predicate amount, predicate
Selectivity, predicate result set and predicate data skewness；
Step S13：Execution route acquires, and collects the data access mode that SQL is used during data scanning, the data
Access mode includes the unique scanning of full table scan, index or index range scanning.
4. database SQL as claimed in claim 3 indexes dynamic optimization method, which is characterized in that the step S2 includes dividing
The access path of SQL and predicate field selectivity are analysed, the step S3 establishes predicate according to the automation of the selectivity of predicate field
Index completes the optimization of manipulative indexing and is removed invalid index according to the data access mode and selectivity of predicate field
It removes.
5. database SQL as claimed in claim 4 indexes dynamic optimization method, which is characterized in that the Predicate selectivity f values
It calculates as follows：
Wherein, c1, c2, cN represent the record number of the predicate conditions, again according to the record number duplicate removal, obtain non-duplicate value, and
The selectivity of the predicate conditions is obtained using non-duplicate value divided by the total number of records；
When f values reach 30%, then judge that the data result collection filtered according to the predicate conditions is smaller, and build for the predicate field
Found common b-tree indexed.
6. database SQL as claimed in claim 4 indexes dynamic optimization method, which is characterized in that the predicate data skew
Degree calculates as follows：
D=distinct (Col)
Wherein, d is the uniqueness data of the predicate field, by the distribution situations of data in classified calculating d and is ranked up i.e.
It can be seen that whether data distribution is uniform, if there is a situation where abnormal dip；When predicate data skewness is more than predetermined threshold value,
Composite index is established according to other predicate fields.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711430676.4A CN108170775A (en) | 2017-12-26 | 2017-12-26 | A kind of database SQL indexes dynamic optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711430676.4A CN108170775A (en) | 2017-12-26 | 2017-12-26 | A kind of database SQL indexes dynamic optimization method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108170775A true CN108170775A (en) | 2018-06-15 |
Family
ID=62521133
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711430676.4A Pending CN108170775A (en) | 2017-12-26 | 2017-12-26 | A kind of database SQL indexes dynamic optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108170775A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287114A (en) * | 2019-06-26 | 2019-09-27 | 深圳前海微众银行股份有限公司 | A kind of method and device of database script performance test |
| CN110895529A (en) * | 2018-08-23 | 2020-03-20 | 马上消费金融股份有限公司 | Processing method of structured query language and related device |
| CN110968594A (en) * | 2018-09-30 | 2020-04-07 | 阿里巴巴集团控股有限公司 | Database query optimization method, engine and storage medium |
| CN111241059A (en) * | 2020-01-07 | 2020-06-05 | 广州虎牙科技有限公司 | Database optimization method and device based on database |
| CN111813803A (en) * | 2020-07-02 | 2020-10-23 | 上海达梦数据库有限公司 | Statement block execution plan generation method, device, equipment and storage medium |
| CN116483831A (en) * | 2023-04-12 | 2023-07-25 | 上海沄熹科技有限公司 | Recommendation index generation method for distributed database |
| CN116775621A (en) * | 2023-08-23 | 2023-09-19 | 北京遥感设备研究所 | Database intelligent index optimization method based on index selectivity |
| CN117093611A (en) * | 2023-10-16 | 2023-11-21 | 北京人大金仓信息技术股份有限公司 | Database combined index suggestion processing method, storage medium and computer device |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1825305A (en) * | 2005-10-31 | 2006-08-30 | 北京神舟航天软件技术有限公司 | Query plan caching method and system based on predicate criticality analysis |
| CN102479255A (en) * | 2010-11-19 | 2012-05-30 | 国际商业机器公司 | Optimizing database query |
| CN103390066A (en) * | 2013-08-08 | 2013-11-13 | 上海新炬网络技术有限公司 | Database overall automation optimizing early warning device and processing method thereof |
| CN103984726A (en) * | 2014-05-16 | 2014-08-13 | 上海新炬网络技术有限公司 | Local revision method for database execution plan |
| CN104714984A (en) * | 2013-12-17 | 2015-06-17 | 中国移动通信集团湖南有限公司 | Database optimization method and device |
| CN104866608A (en) * | 2015-06-05 | 2015-08-26 | 中国人民大学 | Query optimization method based on join index in data warehouse |
| US9122722B2 (en) * | 2010-05-27 | 2015-09-01 | Salesforce.Com, Inc. | Transforming queries in a multi-tenant database system |
| CN106599130A (en) * | 2016-12-02 | 2017-04-26 | 中国银联股份有限公司 | Method and device for selectively interfering with multiple indexes of relational database management system |
| CN106611044A (en) * | 2016-12-02 | 2017-05-03 | 星环信息科技（上海）有限公司 | SQL optimization method and device |
| CN106953929A (en) * | 2017-05-04 | 2017-07-14 | 郑州云海信息技术有限公司 | A kind of method of SmartRack servers high concurrent optimization |
-
2017
- 2017-12-26 CN CN201711430676.4A patent/CN108170775A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1825305A (en) * | 2005-10-31 | 2006-08-30 | 北京神舟航天软件技术有限公司 | Query plan caching method and system based on predicate criticality analysis |
| US9122722B2 (en) * | 2010-05-27 | 2015-09-01 | Salesforce.Com, Inc. | Transforming queries in a multi-tenant database system |
| CN102479255A (en) * | 2010-11-19 | 2012-05-30 | 国际商业机器公司 | Optimizing database query |
| CN103390066A (en) * | 2013-08-08 | 2013-11-13 | 上海新炬网络技术有限公司 | Database overall automation optimizing early warning device and processing method thereof |
| CN104714984A (en) * | 2013-12-17 | 2015-06-17 | 中国移动通信集团湖南有限公司 | Database optimization method and device |
| CN103984726A (en) * | 2014-05-16 | 2014-08-13 | 上海新炬网络技术有限公司 | Local revision method for database execution plan |
| CN104866608A (en) * | 2015-06-05 | 2015-08-26 | 中国人民大学 | Query optimization method based on join index in data warehouse |
| CN106599130A (en) * | 2016-12-02 | 2017-04-26 | 中国银联股份有限公司 | Method and device for selectively interfering with multiple indexes of relational database management system |
| CN106611044A (en) * | 2016-12-02 | 2017-05-03 | 星环信息科技（上海）有限公司 | SQL optimization method and device |
| CN106953929A (en) * | 2017-05-04 | 2017-07-14 | 郑州云海信息技术有限公司 | A kind of method of SmartRack servers high concurrent optimization |
Non-Patent Citations (1)
| Title |
|---|
| 李小华，周毅: "《医院信息系统数据库技术与应用》", 31 October 2015 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110895529A (en) * | 2018-08-23 | 2020-03-20 | 马上消费金融股份有限公司 | Processing method of structured query language and related device |
| CN110895529B (en) * | 2018-08-23 | 2021-03-30 | 马上消费金融股份有限公司 | Processing method of structured query language and related device |
| CN110968594A (en) * | 2018-09-30 | 2020-04-07 | 阿里巴巴集团控股有限公司 | Database query optimization method, engine and storage medium |
| CN110968594B (en) * | 2018-09-30 | 2023-04-07 | 阿里巴巴集团控股有限公司 | Database query optimization method, engine and storage medium |
| CN110287114B (en) * | 2019-06-26 | 2021-06-04 | 深圳前海微众银行股份有限公司 | Method and device for testing performance of database script |
| CN110287114A (en) * | 2019-06-26 | 2019-09-27 | 深圳前海微众银行股份有限公司 | A kind of method and device of database script performance test |
| CN111241059A (en) * | 2020-01-07 | 2020-06-05 | 广州虎牙科技有限公司 | Database optimization method and device based on database |
| CN111813803A (en) * | 2020-07-02 | 2020-10-23 | 上海达梦数据库有限公司 | Statement block execution plan generation method, device, equipment and storage medium |
| CN116483831A (en) * | 2023-04-12 | 2023-07-25 | 上海沄熹科技有限公司 | Recommendation index generation method for distributed database |
| CN116483831B (en) * | 2023-04-12 | 2024-01-30 | 上海沄熹科技有限公司 | Recommendation index generation method for distributed database |
| CN116775621A (en) * | 2023-08-23 | 2023-09-19 | 北京遥感设备研究所 | Database intelligent index optimization method based on index selectivity |
| CN116775621B (en) * | 2023-08-23 | 2024-01-02 | 北京遥感设备研究所 | Database intelligent index optimization method based on index selectivity |
| CN117093611A (en) * | 2023-10-16 | 2023-11-21 | 北京人大金仓信息技术股份有限公司 | Database combined index suggestion processing method, storage medium and computer device |
| CN117093611B (en) * | 2023-10-16 | 2024-03-19 | 北京人大金仓信息技术股份有限公司 | Database combined index suggestion processing method, storage medium and computer device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108170775A (en) | A kind of database SQL indexes dynamic optimization method | |
| US20180129579A1 (en) | Systems and Methods with a Realtime Log Analysis Framework | |
| WO2022083576A1 (en) | Analysis method and apparatus for operating data of network function virtualization device | |
| US8239369B2 (en) | Method and apparatus for enhancing performance of database and environment thereof | |
| CN100578498C (en) | Data integral service system and method | |
| CN106371986A (en) | Log treatment operation and maintenance monitoring system | |
| CN201993755U (en) | Data filtration, compression and storage system of real-time database | |
| US10970343B2 (en) | Adapting database queries for data virtualization over combined database stores | |
| CN108923993B (en) | Network alarm correlation method and device | |
| CN106951351A (en) | A kind of database loads tendency monitoring method | |
| WO2022116107A1 (en) | Data management platform, intelligent defect analysis system, intelligent defect analysis method, computer-program product, and method for defect analysis | |
| CN104392297A (en) | Method and system for realizing non-business process irregularity detection in large data environment | |
| CN109669975B (en) | Industrial big data processing system and method | |
| CN101478432A (en) | Network element state polling method based on storage process timed scheduling | |
| US20190102455A1 (en) | Text analysis of unstructured data | |
| CN106095659A (en) | The method for real-time monitoring of a kind of destructuring event log data and device | |
| US20230315733A1 (en) | Pre-checking method and pre-checking system based on the olap pre-calculation model | |
| CN108845915A (en) | A kind of database data monitoring method | |
| WO2021233160A1 (en) | Data presentation system, method and device, and computer-readable storage medium | |
| Liu et al. | Outlier detection data mining of tax based on cluster | |
| CN111352982A (en) | Manpower extraction analysis system based on big data | |
| CN105843961B (en) | A kind of information system database schema method that process is separated with back-end data | |
| CN206421382U (en) | A kind of data handling system | |
| US8862606B1 (en) | Executing correlated and multi-row subqueries in a MPP database | |
| Wu et al. | Toward fast theta‐join: A prefiltering and amalgamated partitioning approach |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180615 |
|
| WD01 | Invention patent application deemed withdrawn after publication |