CN116225690A - Memory multidimensional database calculation load balancing method and system based on docker - Google Patents
Memory multidimensional database calculation load balancing method and system based on docker Download PDFInfo
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/4555—Para-virtualisation, i.e. guest operating system has to be modified
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45562—Creating, deleting, cloning virtual machine instances
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
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Abstract
The application provides a memory multidimensional database computing load balancing method, system, equipment and computer readable storage medium based on a dock. The memory multidimensional database calculation load balancing method based on the dock comprises the following steps: defining complex computing tasks on a master multidimensional database service; constructing a memory multidimensional database dock mirror image; constructing multidimensional database template data based on a main multidimensional database; before an external application executes a computing task, determining a designated dock instance; and executing a calculation task based on the dock instance to obtain calculation result data. According to the embodiment of the application, the load of the main multidimensional database can be reduced, and the usability of the main multidimensional database is improved.
Description
Technical Field
The application belongs to the technical field of databases, and particularly relates to a memory multidimensional database computing load balancing method, system, equipment and computer readable storage medium based on a dock.
Background
The memory multidimensional database stores data in a cube composed of a plurality of dimensions, the data stored in the cube are all stored in a memory, and one memory multidimensional database can be provided with a plurality of cubes. By utilizing the characteristic of quick access of the memory, the multidimensional database based on the memory completely reserves data in the memory, and the data reading behavior of a user can be quickly converted into memory reading and memory calculation, thereby realizing quick response to the data reading request of the user.
A multidimensional database service typically has two types of data: one type is metadata, such as: dimension, member, cube, process, calculation formula, etc., and the other type is data, generally refers to actual business data stored in cube.
Processes in a multidimensional database service: the process is similar to a script in a general sense, and can be divided into two links of creating and executing, wherein the creating can be understood as writing a script text and storing the script text on a current service, and the executing is actually executed on the text content in the process. The external application may pass in parameters required by the process and execute the process in a certain manner.
View in a multidimensional database service: one view is bound with one cube, one view is slice data in one cube, one cube is composed of multiple dimensions, one or more members are arranged on each dimension, and the view is composed of a plurality of members selected from each dimension of the cube to form a subset of cube data.
When the memory data reading action occurs, the memory data reading action is divided into common memory reading and memory computing. Cube cells are generally classified into 3 types, common entry cells, aggregation cells, and computation cells. The common reading of the input cells is converted into memory reading, the aggregation cells rely on the input cells to perform aggregation calculation, and the calculation cells are calculated based on some other cells through calculation formulas on the cube, so that the reading of the aggregation cells and the calculation cells is converted into real-time calculation based on the memory. In addition, the memory multidimensional database only stores the input cells, and the aggregation and calculation cells are not stored, and are calculated in real time when the cell data are read.
In order to improve the rapid reading and writing of data, the memory multidimensional database stores the data in the memory, so that the problem that the response is slow due to the interaction of hardware or a network layer is avoided, but another problem is brought at the same time, the hardware resources of a single-node machine, particularly the memory and a CPU (Central processing Unit) are limited, and in the actual use process, if a plurality of tasks of complex calculation are performed simultaneously, the resources of the current node are possibly insufficient, so that the response of the database service is slow and even downtime occurs.
Therefore, how to reduce the load of the main multidimensional database and improve the usability of the main multidimensional database is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The embodiment of the application provides a memory multidimensional database calculation load balancing method, a system, equipment and a computer readable storage medium based on a dock, which can reduce the load of a main multidimensional database and improve the usability of the main multidimensional database.
In a first aspect, an embodiment of the present application provides a method for computing load balancing of a memory multidimensional database based on a dock, including:
defining complex computing tasks on a master multidimensional database service;
constructing a memory multidimensional database dock mirror image;
constructing multidimensional database template data based on a main multidimensional database;
before an external application executes a computing task, determining a designated dock instance;
and executing a calculation task based on the dock instance to obtain calculation result data.
Further, after performing the computing task based on the dock instance to obtain the computing result data, the method further includes:
and the external application reads the calculation result data in the dock instance for display.
Further, defining complex computing tasks on the primary multidimensional database service includes:
creating a process for a data export function on a primary multidimensional database service;
creating a process on the primary multidimensional database service for executing the designation in the docker instance;
a process for data importation is created on a primary multidimensional database service.
Further, constructing multidimensional database template data based on the master multidimensional database, comprising:
determining a data directory required by starting a main multidimensional database;
packaging the metadata files and part of the data files in the data catalogue to obtain a template file;
wherein, the template file comprises: a process for a data import function, a process for complex computation, and a process for a data export function.
Further, before the external application performs the computing task, determining a designated dock instance includes:
after a part of docker examples are started in advance, acquiring idle docker examples;
the idle docker instance is determined to be the designated docker instance.
Further, before the external application performs the computing task, determining a designated dock instance includes:
starting a new dock instance in real time;
the new dock instance is determined to be the designated dock instance.
Further, based on the dock instance, performing a computing task to obtain computing result data, including:
the process for the data export function, the process for executing the process specified in the docker instance, and the process for data import are executed separately.
In a second aspect, an embodiment of the present application provides a dock-based memory multidimensional database computing load balancing system, including:
the computing task definition module is used for defining complex computing tasks on the main multidimensional database service;
the dock mirror image construction module is used for constructing a dock mirror image of the memory multidimensional database;
the template data construction module is used for constructing multi-dimensional database template data based on the main multi-dimensional database;
the dock instance determining module is used for determining a designated dock instance before the external application executes the computing task;
and the computing task execution module is used for executing the computing task based on the dock instance to obtain computing result data.
Further, the system further comprises:
and the result data display module is used for reading the calculation result data in the dock instance by an external application to display.
Further, a computing task definition module is configured to:
creating a process for a data export function on a primary multidimensional database service;
creating a process on the primary multidimensional database service for executing the designation in the docker instance;
a process for data importation is created on a primary multidimensional database service.
Further, the template data construction module is used for:
determining a data directory required by starting a main multidimensional database;
packaging the metadata files and part of the data files in the data catalogue to obtain a template file;
wherein, the template file comprises: a process for a data import function, a process for complex computation, and a process for a data export function.
Further, a dock instance determination module is configured to:
after a part of docker examples are started in advance, acquiring idle docker examples;
the idle docker instance is determined to be the designated docker instance.
Further, a dock instance determination module is configured to:
starting a new dock instance in real time;
the new dock instance is determined to be the designated dock instance.
Further, a computing task execution module for:
the process for the data export function, the process for executing the process specified in the docker instance, and the process for data import are executed separately.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;
the processor executes the computer program instructions to implement a method for computing load balancing based on the memory multidimensional database of the dock as shown in the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where computer program instructions are stored, where the computer program instructions, when executed by a processor, implement a method for computing load balancing in a multi-dimensional database based on a dock as shown in the first aspect.
According to the method, the system, the equipment and the computer-readable storage medium for balancing the computing load of the memory multidimensional database based on the dock, the load of the main multidimensional database can be reduced, and the availability of the main multidimensional database is improved.
The memory multidimensional database calculation load balancing method based on the dock comprises the following steps: defining complex computing tasks on a master multidimensional database service; constructing a memory multidimensional database dock mirror image; constructing multidimensional database template data based on a main multidimensional database; before an external application executes a computing task, determining a designated dock instance; and executing a calculation task based on the dock instance to obtain calculation result data.
Therefore, when the main multidimensional database needs to carry out complex calculation tasks, the method transfers the tasks to a dock instance for calculation, and a calculation result can select whether calculation result data is returned to the main multidimensional database according to actual needs. Based on the scheme, the main multidimensional database is generally only responsible for the reading and writing of the input cells and some simple calculations, meanwhile, the main multidimensional database only needs to introduce a part of calculation dependent cells into the docker instance, the docker instance carries out complex calculation tasks, and then some calculation result cells in the docker instance are returned to the main service, so that the load of the main multidimensional database is greatly reduced, and the usability of the main multidimensional database is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described below, it will be obvious that the drawings in the description below are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for balancing computational load of a memory multidimensional database based on a dock according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a dock-based memory multidimensional database computing load balancing system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application are described in detail below to make the objects, technical solutions and advantages of the present application more apparent, and to further describe the present application in conjunction with the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative of the application and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
In order to solve the problems in the prior art, embodiments of the present application provide a method, a system, a device, and a computer readable storage medium for computing load balancing of a memory multidimensional database based on a dock. The following first describes a method for computing load balancing of a memory multidimensional database based on a docker provided in an embodiment of the present application.
Fig. 1 is a schematic flow chart of a method for balancing computing load of a memory multidimensional database based on a dock according to an embodiment of the present application. As shown in fig. 1, the method for balancing the computational load of the memory multidimensional database based on the dock comprises the following steps:
s1, defining complex computing tasks on a main multidimensional database service;
in particular, complex computing tasks are defined on the primary multidimensional database service. Defining complex computing tasks by way of creating processes. The execution of the general process involves reading some complex computing cells, and because the computing formulas on the cube are triggered to perform real-time computation, the step occupies hardware resources, and the execution of the process containing the step is put into a dock instance for execution. In this application, a multidimensional database service based on a dock initiation is simply referred to as a dock instance.
In one embodiment, defining complex computing tasks on a master multidimensional database service includes:
creating a process for a data export function on a primary multidimensional database service;
creating a process on the primary multidimensional database service for executing the designation in the docker instance;
a process for data importation is created on a primary multidimensional database service.
Specifically, this embodiment includes:
(11) A process for data export functionality is created on the master multidimensional database service. A view of the data export is constructed. And constructing the data which needs to be imported into the docker instance into views, and constructing a plurality of views if the data of a plurality of cube needs to be exported. The data in these views is typically based on the entered cell data.
(12) And creating a process for executing the specified process in the specified docker instance on the main multidimensional database service, and executing the process specified in the specified docker instance through the ip in the incoming parameters of the process and the multidimensional database service name.
In actual use, the process of the (12) link can be executed by the process of the (11) link, or the two processes of the (12) link and the (11) link can be combined into one process.
(13) And creating a process for data import on the main multidimensional database service, wherein ip and multidimensional database service names in the import parameters of the process acquire one or more view data appointed in an appointed dock instance and write the view data into the current multidimensional database service. The data in these views is typically based on the entered cell data.
S2, constructing a memory multidimensional database dock mirror image;
specifically, a memory multidimensional database dock mirror image is constructed. And packaging the installation files of the memory multidimensional database into a docker mirror image, and starting the installation files in the docker.
S3, constructing multidimensional database template data based on a main multidimensional database;
in one embodiment, building multidimensional database template data based on a master multidimensional database includes:
determining a data directory required by starting a main multidimensional database;
packaging the metadata files and part of the data files in the data catalogue to obtain a template file;
wherein, the template file comprises: a process for a data import function, a process for complex computation, and a process for a data export function.
And constructing multi-dimensional database template data based on the main multi-dimensional database. The starting of the multidimensional database needs to specify a data directory, files for recording information such as metadata and data exist in the data directory, and the metadata files and part of the data files of the data directory of the main multidimensional database are packaged to be template files. The template requires a process file having the following functions.
(31) And the process for the data import function acquires the ip and the multidimensional database service name of the main service through the process import parameter, and acquires the specified one or more view data from the main service.
(32) A process for complex computation. And reading some calculation cell data of the current service, thereby triggering a calculation formula on the cube to perform real-time calculation, and generating more calculation intermediate data in the real-time calculation process, wherein the step occupies hardware resources comparatively. The calculation dependent data of this step is typically part of the underlying data in the template itself and data imported from the host service by the process in the link (31).
(33) A process for a data export function. And constructing a view of the data export, wherein the view contains (32) calculation result data executed by the link process and is used for importing the calculation result data into the main multidimensional database service.
S4, determining a designated dock instance before the external application executes the computing task;
in one embodiment, determining a specified dock instance before an external application performs a computing task includes: after a part of docker examples are started in advance, acquiring idle docker examples; the idle docker instance is determined to be the designated docker instance.
In one embodiment, determining a specified dock instance before an external application performs a computing task includes: starting a new dock instance in real time; the new dock instance is determined to be the designated dock instance.
Specifically, a specific dock instance is required before the external application needs to make complex calculations. The designated docker instance requires the use of a template packet for startup. The designated docker instances may be allocated in two ways as desired:
(41) And starting a part of the docker examples in advance, acquiring an idle docker example when complex calculation is needed, and designating the complex calculation to the idle docker example.
(42) When complex calculation is needed, a new instance is started in real time, and the complex calculation is assigned to the newly created dock instance.
And S5, executing a calculation task based on the dock instance to obtain calculation result data.
In one embodiment, based on the dock instance, a computing task is performed, resulting in computing result data, comprising: the process for the data export function, the process for executing the process specified in the docker instance, and the process for data import are executed separately.
Specifically, this embodiment includes:
(51) When the external application obtains a dock instance which can be used for complex computation when executing the flow of complex computation, executing the process created in the step (11) to create a data view for export.
(52) And executing the process of creating the link (12), transmitting the ip and the multidimensional database service name of the docker instance distributed in the links (41) and (42) to the process, firstly executing the process of creating the link in the docker instance (31), triggering the action of acquiring data from the main service by the docker instance, then executing the process of creating the link in the docker instance (32), and triggering the action of complex calculation in the docker instance.
In one embodiment, after performing the computing task based on the dock instance to obtain the computing result data, the method further includes: and the external application reads the calculation result data in the dock instance for display.
Specifically, the external application can read and display the calculation result data in the dock instance, the dock instance also has the capability of providing service externally, when the user confirms that the calculation result data needs to be reserved, the user can select to execute the process created in the step (13), trigger the action of acquiring the data from the dock instance by the main service, and import the calculation result data in the dock instance into the main service. The step is not executed every time, and in some scenes with complex calculation, such as price sensitivity analysis, a user may modify part of calculation dependent data (such as price) to perform multiple calculation, and only one calculation result is reserved finally.
In summary, the present application may have the following technical effects:
the distributed computation breaks through the limitation of single-node computation performance of the memory multidimensional database, multiple nodes can perform parallel computation, and the overall computation time can be greatly reduced when multiple complex computation tasks are simultaneously executed.
And the calculation and storage decoupling is carried out, so that the resource utilization rate is improved. The main multidimensional database service is mainly used for reading and writing of input data, recording and simple calculation, the docker instance is mainly used for complex calculation, the main multidimensional database service node does not need high resource redundancy compared with the original single-node deployment, the docker instance can be dynamically created or destroyed according to the requirement, and the utilization rate of resources can be improved to a great extent.
The expansibility is stronger. Compared with the original single-node deployment, the upper limit of the hardware resource of the server can be improved only by upgrading the hardware when the hardware resource is insufficient, a master multi-slave structure is adopted, the docker instance of the slave node can be uniformly managed by using k8s, the docker instance can be created and destroyed in real time according to the requirement, when the docker instance is created more, the machine can be dynamically added into the cluster when the whole load of the machine cluster is higher, the expansion mode is very flexible and the existing instance cannot be influenced, and therefore the expansibility is stronger.
The system is more robust, and fault loss is reduced. The docker instance also has the capability to provide services, and may also provide part of the services when the primary node fails.
In the application, the storage and calculation decoupling mode is mainly used for the main multidimensional database service to store and simply calculate, and higher memory resources are required; the dock instance performs complex computation, requires higher cpu resources, and completes the transmission of computation dependent data and computation result data and the transfer of complex computation tasks by executing processes according to a certain sequence.
The above technical solution is described below with a specific embodiment. This example is a price-selling price sensitivity analysis of a property company.
The company performs measurement and calculation of land price-selling price sensitivity analysis based on the memory multidimensional data.
1. Template files are created periodically based on the master multidimensional database service.
2. Before the calculation is needed, a dock instance for the calculation is created based on a certain template file.
3. The user page is related to the description of the application and comprises a drop-down frame, two buttons and 2 forms, the drop-down frame is used for selecting one docker instance, after one instance is selected, one of the two forms is used for inputting the form, the input data can be input into the docker instance, the display result data form displays the calculated result data in the instance, the two buttons are respectively a measuring button and a storage button, the measuring button is used for completing the calculation of the main application and inputting the calculation dependent data into the docker instance and triggering the calculation of the docker instance, and the other button is used for transmitting the calculated result data and the data input into the docker instance back into the main multidimensional database service.
4. The use flow of the user is as follows:
(1) A page selection instance;
(2) The user inputs a small amount of data and clicks a measuring button to measure;
(3) Checking the measuring and calculating result by the user, if the measuring and calculating result does not accord with the expectation, executing the step (2) again, and if the measuring and calculating result accords with the expectation, executing the next step;
(4) Clicking the save button and transmitting the calculation result data and a small amount of data entered by the instance back to the main application.
Fig. 2 is a schematic structural diagram of a dock-based memory multidimensional database computing load balancing system according to an embodiment of the present application, as shown in fig. 2, where the dock-based memory multidimensional database computing load balancing system includes:
a computing task definition module 201, configured to define complex computing tasks on a main multidimensional database service;
the dock mirror image construction module 202 is configured to construct a dock mirror image of the memory multidimensional database;
a template data construction module 203, configured to construct multi-dimensional database template data based on the main multi-dimensional database;
a dock instance determination module 204, configured to determine a designated dock instance before the external application performs the computing task;
the computing task execution module 205 is configured to execute a computing task based on the dock instance, to obtain computing result data.
In one embodiment, the system further comprises:
and the result data display module is used for reading the calculation result data in the dock instance by an external application to display.
In one embodiment, the computing task definition module 201 is configured to:
creating a process for a data export function on a primary multidimensional database service;
creating a process on the primary multidimensional database service for executing the designation in the docker instance;
a process for data importation is created on a primary multidimensional database service.
In one embodiment, the template data construction module 203 is configured to:
determining a data directory required by starting a main multidimensional database;
packaging the metadata files and part of the data files in the data catalogue to obtain a template file;
wherein, the template file comprises: a process for a data import function, a process for complex computation, and a process for a data export function.
In one embodiment, a docker instance determination module 204 is configured to:
after a part of docker examples are started in advance, acquiring idle docker examples;
the idle docker instance is determined to be the designated docker instance.
In one embodiment, a docker instance determination module 204 is configured to:
starting a new dock instance in real time;
the new dock instance is determined to be the designated dock instance.
In one embodiment, the computing task execution module 205 is configured to:
the process for the data export function, the process for executing the process specified in the docker instance, and the process for data import are executed separately.
Each module in the system shown in fig. 2 has a function of implementing each step in fig. 1, and can achieve a corresponding technical effect, which is not described herein for brevity.
Fig. 3 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
The electronic device may comprise a processor 301 and a memory 302 storing computer program instructions.
In particular, the processor 301 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
In one embodiment, memory 302 may be Read Only Memory (ROM). In one embodiment, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
In one example, the electronic device may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected to each other by a bus 310 and perform communication with each other.
The communication interface 303 is mainly used to implement communication between each module, system, unit and/or device in the embodiments of the present application.
In addition, in combination with the dock-based memory multidimensional database computing load balancing method in the above embodiment, the embodiments of the present application may provide a computer readable storage medium for implementation. The computer readable storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement any of the method embodiments described above for dock-based memory multidimensional database computation load balancing.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or systems. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing system to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing system, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, which are intended to be included in the scope of the present application.
Claims (10)
1. The method for balancing the computational load of the memory multidimensional database based on the dock is characterized by comprising the following steps of:
defining complex computing tasks on a master multidimensional database service;
constructing a memory multidimensional database dock mirror image;
constructing multidimensional database template data based on a main multidimensional database;
before an external application executes the computing task, determining a designated dock instance;
and executing the calculation task based on the dock instance to obtain calculation result data.
2. The dock-based memory multidimensional database computing load balancing method of claim 1, wherein after the computing task is performed based on the dock instance to obtain computing result data, the method further comprises:
and the external application reads the calculation result data in the dock instance for display.
3. The dock-based memory multidimensional database computing load balancing method of claim 1, wherein defining complex computing tasks on a primary multidimensional database service comprises:
creating a process for a data export function on the primary multidimensional database service;
creating a process on the primary multidimensional database service for executing the process specified in the docker instance;
a process for data importation is created on the primary multidimensional database service.
4. The dock-based memory multidimensional database computing load balancing method of claim 1, wherein the constructing multidimensional database template data based on the master multidimensional database comprises:
determining a data directory required by starting a main multidimensional database;
packaging the metadata files and part of the data files in the data catalogue to obtain a template file;
wherein, the template file comprises: a process for a data import function, a process for complex computation, and a process for a data export function.
5. The method for computing load balancing of a dock-based memory multidimensional database according to claim 1, wherein determining a designated dock instance before the external application performs the computing task comprises:
after a part of docker examples are started in advance, acquiring idle docker examples;
the idle docker instance is determined to be the designated docker instance.
6. The method for computing load balancing of a dock-based memory multidimensional database according to claim 1, wherein determining a designated dock instance before the external application performs the computing task comprises:
starting a new dock instance in real time;
the new dock instance is determined to be the designated dock instance.
7. The method for balancing computational load of a multi-dimensional database based on a memory of claim 3, wherein the performing the computational task based on the dock instance to obtain computational result data comprises:
the process for the data export function, the process for executing the process specified in the docker instance, and the process for data import are executed separately.
8. A dock-based memory multidimensional database computing load balancing system, comprising:
the computing task definition module is used for defining complex computing tasks on the main multidimensional database service;
the dock mirror image construction module is used for constructing a dock mirror image of the memory multidimensional database;
the template data construction module is used for constructing multi-dimensional database template data based on the main multi-dimensional database;
the dock instance determining module is used for determining a designated dock instance before the external application executes the computing task;
and the calculation task execution module is used for executing the calculation task based on the dock instance to obtain calculation result data.
9. An electronic device, the electronic device comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a dock-based memory multidimensional database computational load balancing method as recited in any one of claims 1-7.
10. A computer readable storage medium, wherein computer program instructions are stored on the computer readable storage medium, which when executed by a processor implement a dock-based memory multidimensional database computation load balancing method according to any one of claims 1-7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116501505A (en) * | 2023-06-27 | 2023-07-28 | 上海燧原科技有限公司 | Method, device, equipment and medium for generating data stream of load task |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116501505A (en) * | 2023-06-27 | 2023-07-28 | 上海燧原科技有限公司 | Method, device, equipment and medium for generating data stream of load task |
| CN116501505B (en) * | 2023-06-27 | 2023-09-12 | 上海燧原科技有限公司 | Method, device, equipment and medium for generating data stream of load task |
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