CN110781156A - Data node distribution method, equipment and medium - Google Patents
Data node distribution method, equipment and medium Download PDFInfo
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- CN110781156A CN110781156A CN201910893298.6A CN201910893298A CN110781156A CN 110781156 A CN110781156 A CN 110781156A CN 201910893298 A CN201910893298 A CN 201910893298A CN 110781156 A CN110781156 A CN 110781156A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/182—Distributed file systems
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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
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
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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
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
-
- 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
- G06F9/46—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
Abstract
The invention discloses a data node distribution method, which comprises the following steps: receiving a data uploading request of a client, and acquiring load use information of each data node; calculating a comprehensive evaluation value of the data node according to the load use information and the corresponding load balance coefficient of each index; and selecting the data node with the highest comprehensive evaluation value on the local rack as a first copy placing node, and selecting a plurality of data nodes with the highest comprehensive evaluation value on the non-local rack as other copy placing nodes. The invention also discloses a computer device and a readable storage medium. The data node distribution method, the device and the medium provided by the invention can select the optimal data node for storing the copy by comprehensively considering the load use information of each data node, thereby realizing load balance and improving the performance of a cluster system.
Description
Technical Field
The present invention relates to the field of distributed systems, and in particular, to a method, device and readable medium for allocating data nodes.
Background
The HDFS of the Hadoop distributed file system adopts a rack-aware multi-copy storage strategy to realize the integrity, consistency and reliability of super-large-scale data, in the current HDFS default copy placement strategy, data nodes stored in part of copies are randomly selected, the space utilization rate of the data nodes is not considered, cluster load imbalance is easily caused, a large amount of network transmission time and network bandwidth are wasted in the load balancing process, the hardware performance of a node server is not considered, the data reading and writing rates of heterogeneous machines are different, the real-time load of the data nodes and the network distance between the data nodes are not fully considered, the cluster system load imbalance is easily caused, the data transmission efficiency is reduced when the data nodes are far away, the operation performance is influenced, and the cluster system performance is reduced.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a method, a device, and a medium for allocating data nodes, which can select an optimal data node storing a copy by comprehensively considering load usage information of each data node, thereby implementing load balancing and improving performance of a cluster system.
Based on the above object, an aspect of the embodiments of the present invention provides a method for allocating data nodes, including the following steps: receiving a data uploading request of a client, and acquiring load use information of each data node; calculating a comprehensive evaluation value of the data node according to the load use information and the corresponding load balance coefficient of each index; and selecting the data node with the highest comprehensive evaluation value on the local rack as a first copy placing node, and selecting a plurality of data nodes with the highest comprehensive evaluation value on the non-local rack as other copy placing nodes.
In some embodiments, the obtaining the load usage information of each data node includes: judging whether the operation condition of each data node is normal or not; and responding to the normal running condition of each data node, and acquiring the load use information of each data node.
In some embodiments, the determining whether the operation condition of each data node is normal includes: and judging whether the load rate of each data node exceeds a threshold value.
In some embodiments, the load usage information comprises: disk usage, disk speed, CPU usage, CPU performance, memory usage, network distance, and network bandwidth usage.
In some embodiments, the selecting, as the first copy placement node, the data node with the highest comprehensive evaluation value on the local rack includes: and sequencing all the data nodes from large to small according to the comprehensive evaluation value.
In some embodiments, further comprising: and sending the information of the selected data node to a client, and writing data into the data node by the client.
In some embodiments, the other replica placement nodes include a second replica placement node and a third replica placement node.
In some embodiments, the second replica placement node and the third replica placement node belong to the same chassis.
In another aspect of the embodiments of the present invention, there is also provided a computer device, including: at least one processor; and a memory storing computer instructions executable on the processor, the instructions being executable by the processor to perform the steps of: receiving a data uploading request of a client, and acquiring load use information of each data node;
calculating a comprehensive evaluation value of the data node according to the load use information and the corresponding load balance coefficient of each index; and
and selecting the data node with the highest comprehensive evaluation value on the local rack as a first copy placing node, and selecting a plurality of data nodes with the highest comprehensive evaluation value on the non-local rack as other copy placing nodes.
In a further aspect of the embodiments of the present invention, a computer-readable storage medium is also provided, in which a computer program for implementing the above method steps is stored when the computer program is executed by a processor.
The invention has the following beneficial technical effects: the load use information of each data node is comprehensively considered, and the optimal data node for storing the copy is selected, so that load balance is realized, and the performance of the cluster system 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 used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by using the drawings without creative efforts.
Fig. 1 is a schematic diagram of an embodiment of a method for allocating data nodes according to the present invention;
fig. 2 is a flowchart of an embodiment of a method for allocating data nodes according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following embodiments of the present invention are described in further detail with reference to the accompanying drawings.
It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two entities with the same name but different names or different parameters, and it should be noted that "first" and "second" are merely for convenience of description and should not be construed as limitations of the embodiments of the present invention, and they are not described in any more detail in the following embodiments.
In view of the above object, a first aspect of the embodiments of the present invention provides an embodiment of a method for allocating data nodes. Fig. 1 is a schematic diagram illustrating an embodiment of a method for allocating data nodes according to the present invention. As shown in fig. 1, the embodiment of the present invention includes the following steps:
s1, receiving a data uploading request of the client, and acquiring load use information of each data node;
s2, calculating a comprehensive evaluation value of the data node according to the load use information and the corresponding load balance coefficient of each index; and
and S3, selecting the data node with the highest comprehensive evaluation value on the local rack as a first copy placement node, and selecting a plurality of data nodes with the highest comprehensive evaluation value on the non-local rack as other copy placement nodes.
When the client end needs to upload data, the client end submits a data request to the cluster NameNode. After the cluster NameNode receives a data uploading request sent by a client, the cluster state is detected, a rack sensing program is started, and the load use information of each data node is obtained.
In some embodiments, the obtaining the load usage information of each data node includes: judging whether the operation condition of each data node is normal or not; and responding to the normal running condition of each data node, and acquiring the load use information of each data node. In some embodiments, the determining whether the operation condition of each data node is normal includes: and judging whether the load rate of each data node exceeds a threshold value. In addition, in some other embodiments, whether the cluster is operating normally may be determined by determining whether the load rate of the entire cluster exceeds a threshold.
In some embodiments, the load usage information comprises: disk usage, disk speed, CPU usage, CPU performance, memory usage, network distance, and network bandwidth usage. And calculating a comprehensive evaluation value of each data node according to a preset load balance coefficient of each index of the load use information, wherein for example, the load balance coefficient allocated to the disk use rate can be 0.1, the load balance coefficient allocated to the CPU use rate can be 0.05, and the comprehensive evaluation value of each data node is calculated by comprehensively calculating all indexes and the corresponding load balance coefficients. And selecting the data node with the highest comprehensive evaluation value on the local rack as a first copy placing node. In some embodiments, the selecting, as the first copy placement node, the data node with the highest comprehensive evaluation value on the local rack includes: and sequencing all the data nodes from large to small according to the comprehensive evaluation value.
In some embodiments, after the first replica placement node is selected, the data node may be placed into the non-selectable set of data nodes, such that when other replica placement nodes are selected, the data node is not selected regardless of whether the selected location is local or non-local.
And selecting the data node with the highest comprehensive evaluation value on the non-local rack as the other copy placing node. In some embodiments, the other replica placement nodes include a second replica placement node and a third replica placement node. In some embodiments, the second replica placement node and the third replica placement node belong to the same chassis.
In some embodiments, further comprising: and sending the information of the selected data node to a client, and writing data into the data node by the client. And after the writing operation is finished, emptying the selected data node information and the unselected data node information.
The method for selecting the data nodes provided by the embodiment of the invention is characterized in that on the basis of randomly selecting three copy storage data nodes by the current HDFS, data node loads such as the disk utilization rate, the disk rotating speed, the CPU utilization rate, the CPU performance, the memory utilization rate, the network distance, the network bandwidth utilization rate and the like of the data nodes are comprehensively considered, a data node comprehensive evaluation value is established and ranked, a data node with a higher evaluation value is selected for copy placement, system load balancing and data transmission are considered, and the cluster system performance is improved as much as possible.
Fig. 2 is a flowchart illustrating an embodiment of a method for allocating data nodes according to the present invention. As shown in fig. 2, starting from block 101, and proceeding to block 102, a data uploading request of a client is received, and load usage information of each data node is obtained; proceeding to block 103, calculating a comprehensive evaluation value of the data node according to the load usage information and the corresponding load balancing coefficient of each index; then, the process proceeds to a block 104, and a data node with the highest comprehensive evaluation value on the local rack is selected as a first copy placing node; then, the process proceeds to block 105, the data node with the highest comprehensive evaluation value on the non-local rack is selected as the other copy placement node, and then the process proceeds to block 106, and the process ends.
It should be particularly noted that, the steps in the embodiments of the foregoing data node allocation method may be mutually intersected, replaced, added, or deleted, and therefore, the allocation method of the data node based on these reasonable permutation and combination transformations shall also belong to the scope of the present invention, and shall not limit the scope of the present invention to the embodiments.
In view of the above object, a second aspect of the embodiments of the present invention provides a computer device, including: at least one processor; and a memory storing computer instructions executable on the processor, the instructions being executable by the processor to perform the steps of: s1, receiving a data uploading request of the client, and acquiring load use information of each data node; s2, calculating a comprehensive evaluation value of the data node according to the load use information and the corresponding load balance coefficient of each index; and S3, selecting the data node with the highest comprehensive evaluation value on the local rack as a first copy placement node, and selecting a plurality of data nodes with the highest comprehensive evaluation value on the non-local rack as other copy placement nodes.
In some embodiments, the obtaining the load usage information of each data node includes: judging whether the operation condition of each data node is normal or not; and responding to the normal running condition of each data node, and acquiring the load use information of each data node.
In some embodiments, the determining whether the operation condition of each data node is normal includes: and judging whether the load rate of each data node exceeds a threshold value.
In some embodiments, the load usage information comprises: disk usage, disk speed, CPU usage, CPU performance, memory usage, network distance, and network bandwidth usage.
In some embodiments, the selecting, as the first copy placement node, the data node with the highest comprehensive evaluation value on the local rack includes: and sequencing all the data nodes from large to small according to the comprehensive evaluation value.
In some embodiments, further comprising: and sending the information of the selected data node to a client, and writing data into the data node by the client.
In some embodiments, the other replica placement nodes include a second replica placement node and a third replica placement node.
In some embodiments, the second replica placement node and the third replica placement node belong to the same chassis.
The invention also provides a computer readable storage medium storing a computer program which, when executed by a processor, performs the method as above.
Finally, it should be noted that, as one of ordinary skill in the art can appreciate that all or part of the processes of the methods of the above embodiments can be implemented by a computer program to instruct related hardware, and the program of the allocation method of the data node can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods as described above. The storage medium of the program may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like. The embodiments of the computer program may achieve the same or similar effects as any of the above-described method embodiments.
Furthermore, the methods disclosed according to embodiments of the present invention may also be implemented as a computer program executed by a processor, which may be stored in a computer-readable storage medium. Which when executed by a processor performs the above-described functions defined in the methods disclosed in embodiments of the invention.
Further, the above method steps and system elements may also be implemented using a controller and a computer readable storage medium for storing a computer program for causing the controller to implement the functions of the above steps or elements.
Further, it should be appreciated that the computer-readable storage media (e.g., memory) herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of example, and not limitation, nonvolatile memory can include Read Only Memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which can act as external cache memory. By way of example and not limitation, RAM is available in a variety of forms such as synchronous RAM (DRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The storage devices of the disclosed aspects are intended to comprise, without being limited to, these and other suitable types of memory.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as software or hardware depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein may be implemented or performed with the following components designed to perform the functions herein: a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP, and/or any other such configuration.
The steps of a method or algorithm described in connection with the disclosure herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary designs, the functions may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk, blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The foregoing is an exemplary embodiment of the present disclosure, but it should be noted that various changes and modifications could be made herein without departing from the scope of the present disclosure as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the disclosed embodiments described herein need not be performed in any particular order. Furthermore, although elements of the disclosed embodiments of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated.
It should be understood that, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly supports the exception. It should also be understood that "and/or" as used herein is meant to include any and all possible combinations of one or more of the associated listed items.
The numbers of the embodiments disclosed in the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, of embodiments of the invention is limited to these examples; within the idea of an embodiment of the invention, also technical features in the above embodiment or in different embodiments may be combined and there are many other variations of the different aspects of the embodiments of the invention as described above, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present invention are intended to be included within the scope of the embodiments of the present invention.
Claims (10)
1. A method for allocating data nodes, comprising:
receiving a data uploading request of a client, and acquiring load use information of each data node;
calculating a comprehensive evaluation value of the data node according to the load use information and the corresponding load balance coefficient of each index; and
and selecting the data node with the highest comprehensive evaluation value on the local rack as a first copy placing node, and selecting a plurality of data nodes with the highest comprehensive evaluation value on the non-local rack as other copy placing nodes.
2. The method of claim 1, wherein obtaining load usage information for each data node comprises:
judging whether the operation condition of each data node is normal or not;
and responding to the normal running condition of each data node, and acquiring the load use information of each data node.
3. The method according to claim 2, wherein the determining whether the operation condition of each data node is normal comprises:
and judging whether the load rate of each data node exceeds a threshold value.
4. The method of claim 1, wherein the load usage information comprises: disk usage, disk speed, CPU usage, CPU performance, memory usage, network distance, and network bandwidth usage.
5. The method according to claim 1, wherein the selecting the data node with the highest comprehensive evaluation value on the local rack as the first copy placement node comprises:
and sequencing all the data nodes from large to small according to the comprehensive evaluation value.
6. The method of claim 1, further comprising:
and sending the information of the selected data node to a client, and writing data into the data node by the client.
7. The method of claim 1, wherein the other replica placement nodes comprise a second replica placement node and a third replica placement node.
8. The method of claim 7, wherein the second replica placement node and the third replica placement node belong to a same chassis.
9. A computer device, comprising:
at least one processor; and
a memory storing computer instructions executable on the processor, the instructions when executed by the processor implementing the steps of:
receiving a data uploading request of a client, and acquiring load use information of each data node;
calculating a comprehensive evaluation value of the data node according to the load use information and the corresponding load balance coefficient of each index; and
and selecting the data node with the highest comprehensive evaluation value on the local rack as a first copy placing node, and selecting a plurality of data nodes with the highest comprehensive evaluation value on the non-local rack as other copy placing nodes.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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| CN201910893298.6A CN110781156A (en) | 2019-09-20 | 2019-09-20 | Data node distribution method, equipment and medium |
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| CN201910893298.6A CN110781156A (en) | 2019-09-20 | 2019-09-20 | Data node distribution method, equipment and medium |
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| CN110781156A true CN110781156A (en) | 2020-02-11 |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112416888A (en) * | 2020-10-16 | 2021-02-26 | 上海哔哩哔哩科技有限公司 | Dynamic load balancing method and system for distributed file system |
| CN112632621A (en) * | 2020-12-30 | 2021-04-09 | 中国移动通信集团江苏有限公司 | Data access method, device, equipment and computer storage medium |
| CN113778973A (en) * | 2021-01-21 | 2021-12-10 | 北京沃东天骏信息技术有限公司 | Data storage method and device |
| CN115167783A (en) * | 2022-08-03 | 2022-10-11 | 贵州同创科技有限公司 | Electric power material data multi-element storage method and system based on big data |
| CN116088763A (en) * | 2023-02-09 | 2023-05-09 | 北京志凌海纳科技有限公司 | Copy allocation strategy system and method for optimizing recovery rate |
-
2019
- 2019-09-20 CN CN201910893298.6A patent/CN110781156A/en not_active Withdrawn
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112416888A (en) * | 2020-10-16 | 2021-02-26 | 上海哔哩哔哩科技有限公司 | Dynamic load balancing method and system for distributed file system |
| CN112416888B (en) * | 2020-10-16 | 2024-03-12 | 上海哔哩哔哩科技有限公司 | Dynamic load balancing method and system for distributed file system |
| CN112632621A (en) * | 2020-12-30 | 2021-04-09 | 中国移动通信集团江苏有限公司 | Data access method, device, equipment and computer storage medium |
| CN113778973A (en) * | 2021-01-21 | 2021-12-10 | 北京沃东天骏信息技术有限公司 | Data storage method and device |
| CN113778973B (en) * | 2021-01-21 | 2024-04-05 | 北京沃东天骏信息技术有限公司 | Data storage method and device |
| CN115167783A (en) * | 2022-08-03 | 2022-10-11 | 贵州同创科技有限公司 | Electric power material data multi-element storage method and system based on big data |
| CN116088763A (en) * | 2023-02-09 | 2023-05-09 | 北京志凌海纳科技有限公司 | Copy allocation strategy system and method for optimizing recovery rate |
| CN116088763B (en) * | 2023-02-09 | 2023-07-18 | 北京志凌海纳科技有限公司 | Copy allocation strategy system and method for optimizing recovery rate |
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Application publication date: 20200211 |
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