CN104391918A - Method for achieving distributed database query priority management based on peer deployment - Google Patents
Method for achieving distributed database query priority management based on peer deployment Download PDFInfo
- Publication number
- CN104391918A CN104391918A CN201410663305.0A CN201410663305A CN104391918A CN 104391918 A CN104391918 A CN 104391918A CN 201410663305 A CN201410663305 A CN 201410663305A CN 104391918 A CN104391918 A CN 104391918A
- Authority
- CN
- China
- Prior art keywords
- task
- priority
- query
- queue
- resource
- 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.)
- Granted
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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2453—Query optimisation
- G06F16/24534—Query rewriting; Transformation
- G06F16/24549—Run-time optimisation
-
- 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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
Abstract
The invention provides a method for achieving distributed database query priority management based on peer deployment. The method comprises the steps of dividing resources of each query execution nodes in a distributed database according to certain proportion and the same established cluster priority definition; establishing consistent task queues on the execution nodes based on cluster priority, wherein each task queue can manage a certain number of query tasks, clusters provide global unique task IDs for the query tasks, and the query tasks are sorted in the task queues according to the task IDs; adopting the same dispatching mode for the execution nodes according to the task queues, wherein high-priority queued tasks are more than low-priority queued tasks. The method has the advantages that resource division is conducted on the execution nodes different in priority according to the unified proportion so as to ensure that more execution resources can be obtained through high-priority query; only the execution nodes are required to be deployed according to the same strategy, and a unified resource management center is not needed.
Description
Technical field
The invention belongs to distributed networks database query administrative skill field, especially relate to a kind of implementation method and device of the distributed networks database query priority management based on equity deployment.
Background technology
Along with the fast development of informationization technology, large-scale database system needs the data volume of process and storage increasing, calculating becomes increasingly complex, challenge for performance is also increasing, performance, reliability, the demand of extensibility will be more and more stronger, and this time one, centralized database obviously can not meet demand.In order to adapt to the development need of applied business, distributed data base system is by Data distribution8 on the different nodes of computer network, and these data logically belong to same system, and this system can be described as distributed experiment & measurement system.In distributed experiment & measurement system, also need the same from traditional database arranges different priority according to different user or user's group, the user of high priority can have more resource, to guarantee that it can obtain better service, and some tasks are for execution efficiency no requirement (NR), low priority user then can be used to perform, can prevent it from too much taking resource.
For distributed experiment & measurement system, because its resource distribution is at different node, and some inquiries also can be broken down into many steps, and be assigned to the execution of different node, so unified resource management and task scheduling center are then often needed for traditional priority implementation method, as shown in Figure 1, this situation can solve the realization of priority, but it realizes relative complex, and often consume some system resources, such as it will collect all node resource states to divide for different priorities, dispatching center then needs to dispatch all query tasks of whole cluster, execution efficiency can exist and higher realize difficulty.
In sum, existing distributed experiment & measurement system is difficult to realize priority management under the high efficiency prerequisite of guarantee.
Summary of the invention
For the problems referred to above, the object of this invention is to provide a kind of implementation method and device of the distributed networks database query priority management based on equity deployment, the management of high efficiency Query priority is realized, to be suitable for the demand that distributed data base sets up the other user of different priorities in distributed experiment & measurement system.
The design philosophy that the present invention adopts is: according to reciprocity deployment mode, the same principle of pressing cluster priority division resource and task scheduling strategy is adopted in each XM, and by the overall unique task ID of cluster to guarantee the order that query task performs.
For solving the problems of the technologies described above, one aspect of the present invention provides a kind of implementation method of the distributed networks database query priority management based on equity deployment, comprises:
For each query execution node of distributed data base, its resource divides according to certain ratio by the priority definition set according to identical cluster, and the resource ratio that priority is high is large;
Each XM is set up the consistent task queue based on cluster priority, the query task of each task queue ALARA Principle some, the task ID that cluster provides the overall situation unique for query task, query task sorts in task queue according to task ID;
Each XM, for task queue, adopts identical scheduling method, priority from high to low from task queue or the task of getting different number go to perform, the task that high priority is fallen out can more than low priority.
Preferably, for each query execution node of distributed data base, its resource carried out being divided into multiple resource management group according to certain ratio, the corresponding priority of each resource management group, the inquiry of different priorities can be articulated in corresponding resource management group.
Preferably, described task ID can create according to non recounting pattern, ensures that the inquiry arrived first can obtain less task ID.
Preferably, the task dispatching queue length of each priority allows cluster configuration as parameter, access task queue from high to low, obtains the task of setting, as number in task queue is less than task dispatching queue length, then all takes out.
Preferably, described task dispatching queue length is the length of the summation of the once task of falling out of all priority.
Preferably, described resource management group realization needs the setting of choosing of Ore-controlling Role resource and operation parameter thereof.
Another aspect of the present invention provides a kind of implement device of the distributed networks database query priority management based on equity deployment, comprises:
Resource control unit, for each query execution node for distributed data base, its resource divides according to certain ratio by the priority definition set according to identical cluster, and the resource ratio that priority is high is large;
Role management unit, for setting up the consistent task queue based on cluster priority in each XM, the query task of each task queue ALARA Principle some, the task ID that cluster provides the overall situation unique for query task, query task sorts in task queue according to task ID;
Task scheduling unit, realizes each XM for task queue, adopts identical scheduling method, priority from high to low from task queue or the task of getting different number go to perform, the task that high priority is fallen out can more than low priority.
Preferably, role management unit also realizes described task ID and creates according to non recounting pattern, ensures that the inquiry arrived first can obtain less task ID.
Preferably, the task dispatching queue length of each priority is allowed cluster configuration as parameter by task scheduling unit, access task queue from high to low, obtains the task of setting.
Preferably, described resource control unit realization needs the setting of choosing of Ore-controlling Role resource and operation parameter thereof.
The advantage that the present invention has and good effect are:
In proportion resource is not divided to different priorities, to guarantee that high priority inquiry can obtain more execution resources; Each XM is only needed to get final product according to same policy deployment and without the need to unified resource management center, realize structure relatively simple and easy;
Each XM is only needed to get final product according to the deployment of same task scheduling strategy and without the need to unified task scheduling center, realize structure relatively simple and easy;
Implementation method of the present invention and device and the implementation method of existing centralized management compare and to realize relative simple under in order to be effective prerequisite, effectively improve cluster to the use control of resource and search efficiency and have done effective management to the execution sequence of query task.
Accompanying drawing explanation
Fig. 1 is prior art distributed data base priority resources and task scheduling situation schematic diagram;
Fig. 2 is the process flow diagram of the implementation method of the distributed networks database query priority management that one embodiment of the invention is disposed based on equity;
Fig. 3 is distributed data base XM priority resources dividing condition schematic diagram in one embodiment of the invention;
Fig. 4 is that in one embodiment of the invention, the task queue of distributed data base medium priority enters to list intention;
Fig. 5 is that in one embodiment of the invention, the task queue of distributed data base medium priority is fallen out schematic diagram;
Fig. 6 is that one embodiment of the invention inquiry is sent node and issued querying flow figure;
Fig. 7 is that the queue of one embodiment of the invention XM priority tasks is fallen out process flow diagram.
Embodiment
Below in conjunction with accompanying drawing, specific embodiments of the invention are elaborated.
Fig. 2 is the process flow diagram of the implementation method of the distributed networks database query priority management that one embodiment of the invention is disposed based on equity, the present invention is based on the implementation method of the distributed networks database query priority management that equity is disposed, as shown in Figure 2, comprises following steps:
Step 1, each query execution node for distributed data base, its resource divides according to certain ratio by the priority definition set according to identical cluster, and the resource ratio that priority is high is large;
In step 1, for each query execution node of distributed data base, its resource carried out being divided into multiple resource management group according to certain ratio, the corresponding priority of each resource management group, the inquiry of different priorities can be articulated in corresponding resource management group;
Resource management group realization described in the embodiment of the present invention needs the setting of choosing of Ore-controlling Role resource and operation parameter thereof; Controling parameters such as CPU uses weight can be helped to realize by technology such as similar LINUX CGROUP;
Fig. 3 is distributed data base XM priority resources dividing condition schematic diagram in one embodiment of the invention, one embodiment of the invention is for the XM resource in Fig. 3, the resource category divided in the present embodiment has CPU and DISKIO, be not limited thereto when realizing, also can only control CPU separately, in the present embodiment, one has 8 CPU and DISK, the priority that cluster is specified is divided into 0-3 level, so the division of its priority resources group can with reference to figure 2, and each node all can according to this ratio cut partition resource.
Wherein, the name of node resource management group and division, name can be combined according to cluster Instance Name and tenant and priority level and be named, with careful difference different user.
Step 2, in each XM, set up the consistent task queue based on cluster priority, the query task of each task queue ALARA Principle some, the task ID that cluster provides the overall situation unique for query task, query task sorts in task queue according to task ID; Its task ID obtains by the ID generator of a cluster; Wherein task ID described in the embodiment of the present invention can create according to non recounting pattern, ensures that the inquiry arrived first can obtain less task ID;
Each priority tasks can be put into the task queue of its correspondence, and sorts according to its task ID, waits for the execution that is scheduled; Query task sorts in task queue according to this ID, to guarantee that first initiating of task first performs and each XM is unanimous on the whole for the position in its queue of the task from same inquiry, as far as possible synchronously completes;
Fig. 4 is that in one embodiment of the invention, distributed data base medium priority queue task is entered to list intention; The priority that the present embodiment is specified is divided into 0-3 level, in figure for priority be 1 and query task ID be a certain inquiry of the USER1 user of 3, the task of describing this inquiry can put the corresponding tagmeme of priority 1 queue into each XM.
Step 3, each XM, for task queue, adopt identical scheduling method, priority from high to low from task queue or the task of getting different number go to perform, the task that high priority is fallen out can more than low priority;
Fig. 5 is that in one embodiment of the invention, distributed data base medium priority queue task is fallen out schematic diagram, in implementation process, each XM has an execution send queue to be used for loading ready executing the task, each priority gets the task number difference of falling out in the process of task at a poll, high-priority queue can go out on missions number lower priority can be many, to ensure that high-priority task can be complete sooner.
The task dispatching queue length of each priority of the embodiment of the present invention allows cluster configuration as parameter, access task queue from high to low, obtains the task of setting, as number in task queue is less than task dispatching queue length, then all takes out; The length of task dispatching queue described in the present embodiment is the length of the summation of the once task of falling out of all priority.
Fig. 6 is that the present invention inquires about and sends node and issue querying flow figure, and in the inventive method implementation procedure, as shown in Figure 6, inquiry is sent node and issued inquiry and comprise the steps:
Step 501, for according to query statement task resolution and the plan of formulating and implementing, namely this inquiry divides how many step to perform and performs to those XM;
Step 502, obtains all tasks of overall sole task ID dispensing;
Step 503, transfers query task corresponding to this step to corresponding XM;
Step 504, wait task is complete;
Step 505, until query task all completes;
Wherein step 502 – 505 realizes issuing task step by step to XM, and waits for that it completes.
Fig. 7 is that query execution node priority queue task is fallen out process flow diagram, and as shown in Figure 7, step 601 – 604 is the process that all priority queries of traversal take out the tasks carrying of respective number.
The present invention is based on the implement device of the distributed networks database query priority management that equity is disposed, comprise:
Resource control unit, for each query execution node for distributed data base, its resource divides according to certain ratio by the priority definition set according to identical cluster, and the resource ratio that priority is high is large;
Role management unit, for setting up the consistent task queue based on cluster priority in each XM, the query task of each task queue ALARA Principle some, the task ID that cluster provides the overall situation unique for query task, query task sorts in task queue according to task ID;
Task scheduling unit, realizes each XM for task queue, adopts identical scheduling method, priority from high to low from task queue or the task of getting different number go to perform, the task that high priority is fallen out can more than low priority.
Role management unit of the present invention also realizes described task ID and creates according to non recounting pattern, ensures that the inquiry arrived first can obtain less task ID.
The task dispatching queue length of each priority is allowed cluster configuration as parameter by task scheduling unit of the present invention, access task queue from high to low, obtains the task of setting.
Resource control unit realization of the present invention needs the setting of choosing of Ore-controlling Role resource and operation parameter thereof.
Above one embodiment of the present of invention have been described in detail, but described content being only preferred embodiment of the present invention, can not being considered to for limiting practical range of the present invention.All equalizations done according to the present patent application scope change and improve, and all should still belong within patent covering scope of the present invention.
Claims (10)
1., based on the implementation method of the distributed networks database query priority management of equity deployment, it is characterized in that, comprise:
For each query execution node of distributed data base, its resource divides according to certain ratio by the priority definition set according to identical cluster, and the resource ratio that priority is high is large;
Each XM is set up the consistent task queue based on cluster priority, the query task of each task queue ALARA Principle some, the task ID that cluster provides the overall situation unique for query task, query task sorts in task queue according to task ID;
Each XM, for task queue, adopts identical scheduling method, priority from high to low from task queue or the task of getting different number go to perform, the task that high priority is fallen out can more than low priority.
2. the implementation method of the distributed networks database query priority management based on equity deployment according to claim 1, it is characterized in that: for each query execution node of distributed data base, its resource is carried out being divided into multiple resource management group according to certain ratio, the corresponding priority of each resource management group, the inquiry of different priorities can be articulated in corresponding resource management group.
3. the implementation method of the distributed networks database query priority management based on equity deployment according to claim 1, is characterized in that: described task ID can create according to non recounting pattern, ensures that the inquiry arrived first can obtain less task ID.
4. the implementation method of the distributed networks database query priority management based on equity deployment according to claim 1, it is characterized in that: the task dispatching queue length of each priority allows cluster configuration as parameter, access task queue from high to low, obtain the task of setting, as number in task queue is less than task dispatching queue length, then all take out.
5. the implementation method of the distributed networks database query priority management based on equity deployment according to claim 4, is characterized in that: described task dispatching queue length is the length of the summation of the once task of falling out of all priority.
6. the implementation method of the distributed networks database query priority management based on equity deployment according to claim 2, is characterized in that: described resource management group realization needs the setting of choosing of Ore-controlling Role resource and operation parameter thereof.
7., based on the implement device of the distributed networks database query priority management of equity deployment, it is characterized in that, comprise:
Resource control unit, for each query execution node for distributed data base, its resource divides according to certain ratio by the priority definition set according to identical cluster, and the resource ratio that priority is high is large;
Role management unit, for setting up the consistent task queue based on cluster priority in each XM, the query task of each task queue ALARA Principle some, the task ID that cluster provides the overall situation unique for query task, query task sorts in task queue according to task ID;
Task scheduling unit, realizes each XM for task queue, adopts identical scheduling method, priority from high to low from task queue or the task of getting different number go to perform, the task that high priority is fallen out can more than low priority.
8. the implement device of the distributed networks database query priority management based on equity deployment according to claim 7, it is characterized in that: role management unit also realizes described task ID and creates according to non recounting pattern, ensure that the inquiry arrived first can obtain less task ID.
9. the implement device of the distributed networks database query priority management based on equity deployment according to claim 7, it is characterized in that: the task dispatching queue length of each priority is allowed cluster configuration as parameter by task scheduling unit, access task queue from high to low, obtains the task of setting.
10. the implement device of the distributed networks database query priority management based on equity deployment according to claim 7, is characterized in that: described resource control unit realization needs the setting of choosing of Ore-controlling Role resource and operation parameter thereof.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410663305.0A CN104391918B (en) | 2014-11-19 | 2014-11-19 | The implementation method of distributed networks database query priority management based on equity deployment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410663305.0A CN104391918B (en) | 2014-11-19 | 2014-11-19 | The implementation method of distributed networks database query priority management based on equity deployment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104391918A true CN104391918A (en) | 2015-03-04 |
| CN104391918B CN104391918B (en) | 2018-01-19 |
Family
ID=52609822
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410663305.0A Active CN104391918B (en) | 2014-11-19 | 2014-11-19 | The implementation method of distributed networks database query priority management based on equity deployment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104391918B (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105511950A (en) * | 2015-12-10 | 2016-04-20 | 天津海量信息技术有限公司 | Dispatching management method for task queue priority of large data set |
| CN105550038A (en) * | 2015-12-12 | 2016-05-04 | 天津南大通用数据技术股份有限公司 | Equivalently deployed distributed database resource management and load adjustment method |
| CN107239342A (en) * | 2017-05-31 | 2017-10-10 | 郑州云海信息技术有限公司 | A kind of storage cluster task management method and device |
| CN108092917A (en) * | 2016-11-23 | 2018-05-29 | 通用汽车有限责任公司 | Timing message serializer |
| CN108156086A (en) * | 2017-12-19 | 2018-06-12 | 北京奇艺世纪科技有限公司 | A kind of strategy rule downloading method and device |
| WO2018176965A1 (en) * | 2017-03-31 | 2018-10-04 | 北京京东金融科技控股有限公司 | Financial data processing method and apparatus based on blockchain, and electronic device |
| CN109976910A (en) * | 2019-03-20 | 2019-07-05 | 跬云(上海)信息科技有限公司 | Querying method and device based on precomputation OLAP model |
| CN110750350A (en) * | 2019-10-29 | 2020-02-04 | 广东浪潮大数据研究有限公司 | Large resource scheduling method, system, device and readable storage medium |
| CN111190932A (en) * | 2019-12-16 | 2020-05-22 | 北京淇瑀信息科技有限公司 | Privacy cluster query method and device and electronic equipment |
| CN111736965A (en) * | 2019-12-11 | 2020-10-02 | 西安宇视信息科技有限公司 | Task scheduling method and device, scheduling server and machine-readable storage medium |
| CN113364825A (en) * | 2020-03-06 | 2021-09-07 | 联通系统集成有限公司 | Distributed resource integration system |
| CN113495923A (en) * | 2021-02-09 | 2021-10-12 | 深圳市云网万店科技有限公司 | Scheduling management method and system for distributed database executor |
| CN113781063A (en) * | 2020-12-30 | 2021-12-10 | 北京京东振世信息技术有限公司 | User resource processing method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002080026A1 (en) * | 2001-03-30 | 2002-10-10 | British Telecommunications Public Limited Company | Global database management system integrating heterogeneous data resources |
| US20110072008A1 (en) * | 2009-09-22 | 2011-03-24 | Sybase, Inc. | Query Optimization with Awareness of Limited Resource Usage |
| CN102243598A (en) * | 2010-05-14 | 2011-11-16 | 深圳市腾讯计算机系统有限公司 | Task scheduling method and system in distributed data warehouse |
| CN102387173A (en) * | 2010-09-01 | 2012-03-21 | 中国移动通信集团公司 | MapReduce system and method and device for scheduling tasks thereof |
| CN102567086A (en) * | 2010-12-30 | 2012-07-11 | 中国移动通信集团公司 | Task scheduling method, equipment and system |
| CN103123652A (en) * | 2013-03-14 | 2013-05-29 | 曙光信息产业(北京)有限公司 | Data query method and cluster database system |
| CN103902646A (en) * | 2013-12-27 | 2014-07-02 | 北京天融信软件有限公司 | Distributed task managing system and method |
-
2014
- 2014-11-19 CN CN201410663305.0A patent/CN104391918B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002080026A1 (en) * | 2001-03-30 | 2002-10-10 | British Telecommunications Public Limited Company | Global database management system integrating heterogeneous data resources |
| US20110072008A1 (en) * | 2009-09-22 | 2011-03-24 | Sybase, Inc. | Query Optimization with Awareness of Limited Resource Usage |
| CN102243598A (en) * | 2010-05-14 | 2011-11-16 | 深圳市腾讯计算机系统有限公司 | Task scheduling method and system in distributed data warehouse |
| CN102387173A (en) * | 2010-09-01 | 2012-03-21 | 中国移动通信集团公司 | MapReduce system and method and device for scheduling tasks thereof |
| CN102567086A (en) * | 2010-12-30 | 2012-07-11 | 中国移动通信集团公司 | Task scheduling method, equipment and system |
| CN103123652A (en) * | 2013-03-14 | 2013-05-29 | 曙光信息产业(北京)有限公司 | Data query method and cluster database system |
| CN103902646A (en) * | 2013-12-27 | 2014-07-02 | 北京天融信软件有限公司 | Distributed task managing system and method |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105511950A (en) * | 2015-12-10 | 2016-04-20 | 天津海量信息技术有限公司 | Dispatching management method for task queue priority of large data set |
| CN105550038A (en) * | 2015-12-12 | 2016-05-04 | 天津南大通用数据技术股份有限公司 | Equivalently deployed distributed database resource management and load adjustment method |
| CN108092917B (en) * | 2016-11-23 | 2021-11-09 | 通用汽车有限责任公司 | Time sequence message serializer |
| CN108092917A (en) * | 2016-11-23 | 2018-05-29 | 通用汽车有限责任公司 | Timing message serializer |
| WO2018176965A1 (en) * | 2017-03-31 | 2018-10-04 | 北京京东金融科技控股有限公司 | Financial data processing method and apparatus based on blockchain, and electronic device |
| CN107239342A (en) * | 2017-05-31 | 2017-10-10 | 郑州云海信息技术有限公司 | A kind of storage cluster task management method and device |
| CN108156086A (en) * | 2017-12-19 | 2018-06-12 | 北京奇艺世纪科技有限公司 | A kind of strategy rule downloading method and device |
| CN108156086B (en) * | 2017-12-19 | 2022-04-22 | 北京奇艺世纪科技有限公司 | Policy rule issuing method and device |
| CN109976910A (en) * | 2019-03-20 | 2019-07-05 | 跬云(上海)信息科技有限公司 | Querying method and device based on precomputation OLAP model |
| CN110750350A (en) * | 2019-10-29 | 2020-02-04 | 广东浪潮大数据研究有限公司 | Large resource scheduling method, system, device and readable storage medium |
| CN110750350B (en) * | 2019-10-29 | 2022-08-16 | 广东浪潮大数据研究有限公司 | Large resource scheduling method, system, device and readable storage medium |
| CN111736965A (en) * | 2019-12-11 | 2020-10-02 | 西安宇视信息科技有限公司 | Task scheduling method and device, scheduling server and machine-readable storage medium |
| CN111190932A (en) * | 2019-12-16 | 2020-05-22 | 北京淇瑀信息科技有限公司 | Privacy cluster query method and device and electronic equipment |
| CN111190932B (en) * | 2019-12-16 | 2023-08-18 | 北京淇瑀信息科技有限公司 | Privacy cluster query method and device and electronic equipment |
| CN113364825A (en) * | 2020-03-06 | 2021-09-07 | 联通系统集成有限公司 | Distributed resource integration system |
| CN113364825B (en) * | 2020-03-06 | 2022-12-06 | 联通系统集成有限公司 | Distributed resource integration system |
| CN113781063A (en) * | 2020-12-30 | 2021-12-10 | 北京京东振世信息技术有限公司 | User resource processing method and device |
| CN113495923A (en) * | 2021-02-09 | 2021-10-12 | 深圳市云网万店科技有限公司 | Scheduling management method and system for distributed database executor |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104391918B (en) | 2018-01-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104391918A (en) | Method for achieving distributed database query priority management based on peer deployment | |
| CN102387173B (en) | MapReduce system and method and device for scheduling tasks thereof | |
| CN103309738B (en) | User job dispatching method and device | |
| CN105900064B (en) | The method and apparatus for dispatching data flow task | |
| CN103516807B (en) | A kind of cloud computing platform server load balancing system and method | |
| Zheng et al. | An approach for cloud resource scheduling based on Parallel Genetic Algorithm | |
| CN102567086B (en) | Task scheduling method, equipment and system | |
| CN108762896A (en) | One kind being based on Hadoop cluster tasks dispatching method and computer equipment | |
| CN110383764A (en) | The system and method for usage history data processing event in serverless backup system | |
| CN103023980B (en) | A kind of method and system of cloud platform processes user service request | |
| CN105471985A (en) | Load balance method, cloud platform computing method and cloud platform | |
| CN105373426B (en) | A kind of car networking memory aware real time job dispatching method based on Hadoop | |
| CN109783225B (en) | Tenant priority management method and system of multi-tenant big data platform | |
| CN103593229A (en) | Integrating and uniform dispatching frame of heterogeneous cloud operation systems and dispatching method thereof | |
| CN105791371B (en) | A kind of cloud storage service system and method | |
| CN106790332B (en) | Resource scheduling method, system and main node | |
| WO2018126771A1 (en) | Storage controller and io request processing method | |
| CN105824686A (en) | Selecting method and selecting system of host machine of virtual machine | |
| CN104881322A (en) | Method and device for dispatching cluster resource based on packing model | |
| CN103391206A (en) | Method and device for task scheduling | |
| CN103116525A (en) | Map reduce computing method under internet environment | |
| WO2024021489A1 (en) | Task scheduling method and apparatus, and kubernetes scheduler | |
| CN103763174A (en) | Virtual network mapping method based on function block | |
| CN106201681B (en) | Method for scheduling task based on pre-release the Resources list under Hadoop platform | |
| Wen et al. | Load balancing job assignment for cluster-based cloud computing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |