CN110928836A - Data equalization optimization method based on HDFS, system terminal and storage medium - Google Patents
Data equalization optimization method based on HDFS, system terminal and storage medium Download PDFInfo
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- CN110928836A CN110928836A CN201910995380.XA CN201910995380A CN110928836A CN 110928836 A CN110928836 A CN 110928836A CN 201910995380 A CN201910995380 A CN 201910995380A CN 110928836 A CN110928836 A CN 110928836A
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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
- G06F16/11—File system administration, e.g. details of archiving or snapshots
- G06F16/119—Details of migration of file systems
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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
- G06F16/17—Details of further file system functions
- G06F16/1727—Details of free space management performed by the file system
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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
Abstract
The invention provides a data equalization optimization method based on an HDFS (Hadoop distributed File System), a system terminal and a storage medium, wherein the method comprises the following steps: after initializing node information, traversing each node, and calculating the residual space and the average residual space of the node; calculating the difference value between the average residual space and the residual space of each node; acquiring a rebalance threshold of the cluster, calculating a migration amount according to the difference and the threshold, and outputting the migration amount; and data equalization is carried out according to the migration quantity of each node, so that the data equalization of the residual space is realized. The invention improves the utilization rate of the cluster residual space, provides a selectable range for the migration amount of the nodes by rebalancing the threshold value, and avoids the condition that some data are used and cannot be migrated.
Description
Technical Field
The invention belongs to the technical field of big data service platforms, and particularly relates to a data balance optimization method, a system, a terminal and a storage medium based on an HDFS.
Background
Hadoop is an open source distributed platform under the Apache flag. Hadoop with a Hadoop file system (HDFS) and a MapReduce computing model as cores provides a distributed infrastructure with transparent system bottom details for users. HDFS in Hadoop has a high fault tolerance and is developed based on the Java language, which allows him to deploy in cheap clusters of computers. The data management capability of HDFS in Hadoop and the high efficiency of MapReduce in processing tasks make the HDFS popular in a distributed system.
The project structure of Hadoop contains many sub-projects, and the core contents are HDFS and MapReduce. After the HDFS cluster of Hadoop is used for a period of time, the phenomenon of unbalanced disk use occurs in each data node, that is, the data on the data volume layer is inclined, which causes the problem of unbalanced load between different nodes, thereby affecting the overall operating efficiency of the cluster. HDFS has a special tool to solve this type of problem, namely Balancer, for this phenomenon.
However, the operation effect of the Balancer tool in the current Hadoop version is not particularly high, and particularly under the conditions of large data scale and large disk capacity gap, the phenomenon of data imbalance is more obvious. But this does not address all situations, such as the presence of a cluster of heterogeneous nodes. For example, 2 nodes within a cluster: the disk capacity of the node A is 100T, the disk capacity of the node B is 10T, and if the utilization rate is 70% according to the balance strategy of a Balancer tool, the node A finally tends to use 70T of space, 30T of residual space, 7T of residual space and 3T of residual space. In this case, the remaining space of the nodes in the cluster is unbalanced, and when the number of nodes is large, for example, there are 10 a nodes and 10B nodes, we will only put data in the nodes with large remaining space, so that the remaining space of the 10B nodes is wasted. Based on the above situation, a data balance optimization method based on HDFS, a system terminal and a storage medium are needed, so that the final balance effect is changed into 97T for node a, 3T for idle, 7T for node B and 3T for idle, and data balance of the node residual space is achieved, rather than according to the usage ratio.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, the present invention provides a method, a system, a terminal and a storage medium for optimizing data equalization based on HDFS, so as to solve the above-mentioned technical problems.
In a first aspect, the present invention provides a data equalization optimization method based on an HDFS, including:
after initializing node information, traversing each node, and calculating the residual space and the average residual space of the node;
calculating the difference value between the average residual space and the residual space of each node;
acquiring a rebalance threshold of the cluster, calculating a migration amount according to the difference and the threshold, and outputting the migration amount;
and data equalization is carried out according to the migration quantity of each node, so that the data equalization of the residual space is realized.
Further, the computing node remaining space and average remaining space includes:
acquiring the total space of each node and the utilization rate of the cluster, and calculating the use space of each node;
calculating the difference between the total space of the nodes and the use space of the nodes to obtain the residual space of each node;
calculating the sum of the residual spaces of all the nodes to obtain the total residual space of the cluster;
and obtaining the average residual space according to the total residual space of the cluster and the number of the nodes.
Further, the obtaining a rebalance threshold of the cluster, calculating a migration amount according to the difference and the threshold, and outputting the migration amount includes:
acquiring a rebalance threshold of the cluster;
and calculating the difference between the absolute value of the difference value and the threshold value as the migration quantity of data equalization.
In a second aspect, the present invention provides a HDFS-based data equalization optimization system, including:
the data acquisition unit is configured for traversing each node after initializing the node information, and calculating the node residual space and the average residual space;
a difference value calculation unit configured to calculate a difference value between the average remaining space and the remaining space of each node;
the migration amount calculation unit is used for configuring a rebalance threshold value used for acquiring the cluster, calculating the migration amount according to the difference value and the threshold value and outputting the migration amount;
and the data migration unit is configured to perform data balance according to the migration volume of each node, so as to realize data balance of the residual space.
Further, the data acquisition unit includes:
the node use space calculation module is configured to acquire the total space of each node and the utilization rate of the cluster and calculate the use space of each node;
the node residual space calculation module is configured for calculating the difference between the total space of the nodes and the use space of the nodes to obtain the residual space of each node;
the cluster total residual space calculation module is configured to calculate the sum of the residual spaces of all the nodes to obtain the total residual space of the cluster;
and the average residual space calculation module is configured to obtain an average residual space according to the total residual space of the cluster and the number of the nodes.
Further, the migration amount calculation unit includes:
a threshold acquisition module configured to acquire a rebalance threshold of the cluster;
and the migration quantity calculation module is configured to calculate the difference between the absolute value of the difference value and the threshold value as the migration quantity of data balance.
In a third aspect, a terminal is provided, including:
a processor, a memory, wherein,
the memory is used for storing a computer program which,
the processor is used for calling and running the computer program from the memory so as to make the terminal execute the method of the terminal.
In a fourth aspect, a computer storage medium having stored therein instructions, which when run on a computer, cause the computer to perform the method of the above aspects.
The beneficial effect of the invention is that,
according to the data balance optimization method, system, terminal and storage medium based on the HDFS, the average residual space is calculated, the residual space of each node is equally divided instead of being balanced according to the occupation ratio, the residual space balance of each node in the same cluster is achieved, the utilization rate of the cluster residual space is improved, and the overall operation efficiency of the cluster is improved; meanwhile, the invention also provides a selectable range for the migration quantity of the node according to the rebalance threshold value, thereby avoiding the situation that some data are used and can not be migrated; partial performance of the blancer tool of the HDFS is optimized, and convenience is provided for related development of the HDFS.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a schematic block diagram of a system of one embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following explains key terms appearing in the present invention.
Cluster rebalance threshold: the system provides a balance width value of data between disks, the balance width value is used as a judgment standard for judging whether the data needs to be continuously balanced, a configuration item is dfs. The final data balance is achieved by the aim that the residual space of each node is equal, so that absolute balance can be achieved by the cluster, and the overall operation efficiency of the cluster is improved. The rebalancing threshold exists by providing a selectable range for the migration amount and migrating nodes with more space left to nodes with less space left until the difference is within the threshold.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention. The execution subject in fig. 1 may be an HDFS-based data equalization optimization system.
As shown in fig. 1, the method 100 includes:
and 140, performing data balance according to the migration volume of each node to realize data balance of the residual space.
Optionally, as an embodiment of the present invention, the calculating the remaining space and the average remaining space of the node includes:
acquiring the total space of each node and the utilization rate of the cluster, and calculating the use space of each node;
calculating the difference between the total space of the nodes and the use space of the nodes to obtain the residual space of each node;
calculating the sum of the residual spaces of all the nodes to obtain the total residual space of the cluster;
and obtaining the average residual space according to the total residual space of the cluster and the number of the nodes.
Optionally, as an embodiment of the present invention, the obtaining a rebalance threshold of the cluster, calculating a migration amount according to the difference and the threshold, and outputting the migration amount includes:
acquiring a rebalance threshold of the cluster;
and calculating the difference between the absolute value of the difference value and the threshold value as the migration quantity of data equalization.
In order to facilitate understanding of the present invention, the principle of the HDFS-based data equalization optimization method of the present invention is combined with the data equalization optimization process of the HDFS in the embodiment to further describe the HDFS-based data equalization optimization method of the present invention.
Example 1
This embodiment provides a cluster, where the cluster includes a node a and a node B, the total size of the space of the node a is 100T, the total size of the space of the node B is 10T, and the utilization rate of the cluster is 90%.
Specifically, the data equalization optimization method based on the HDFS includes:
s1, after initializing the node information, traversing each node, and calculating the residual space and the average residual space of the node;
initializing node information, obtaining the total space size of all nodes and the utilization rate of a cluster, and obtaining the use space of each node, namely the use space of a node A is 90T and the use space of a node B is 9T; calculating the difference between the total space of the nodes and the used space of the nodes to obtain the residual space of each node, namely the residual space of the node A is 10T and the residual space of the node B is 1T; calculating the sum of the residual spaces of all the nodes to obtain the total residual space 11T of the cluster; and obtaining an average residual space according to the total residual space of the cluster and the number of the nodes, namely obtaining a result of 5.5T of the average residual space, wherein the average residual space is the total residual space of the cluster divided by the number of the nodes.
S2, calculating the difference value between the average residual space and the residual space of each node;
the residual space of each node is greater than the average residual space or less than the average residual space, so the difference has positive or negative, and the average residual space is derived from the residual space of each node, so the absolute value of the difference between the average residual space and the residual space of each node is equal. In this embodiment, the difference between the residual space of the node a and the average residual space is 4.5T, the difference between the residual space of the node B and the average residual space is-4.5T, and the absolute value of the difference between the node a and the difference between the node B is the same.
S3, acquiring a rebalance threshold of the cluster, calculating a migration amount according to the difference and the threshold, and outputting the migration amount;
acquiring a rebalance threshold of the cluster, wherein the threshold is a numerical value set by a system and provides a selectable range for data equalization; and calculating the difference between the absolute value of the difference and the threshold value as the migration amount of data balance, wherein the residual space of each balanced node is not completely equal due to the existence of the rebalance threshold value. In this embodiment, if the threshold is 2T, the migration amount is 2.5T.
And S4, balancing data according to the migration volume of each node, and balancing the residual space.
And respectively increasing or decreasing the space size of the migration volume according to the migration volume in the residual space of each node. In this embodiment, node a moves out of the 2.5T space, and node B receives the 2.5T space.
Example 2
The HDFS-based data equalization optimization method described in this embodiment is the same as the step described in embodiment 1. But the data is slightly different. The node a moves out of the 2.5T space, and in the step of receiving the 2.5T space, the 2.5T space can be replaced by any data from 2.5T to 4.5T.
Example 3
The method for optimizing data equalization based on the HDFS in this embodiment is the same as the steps in embodiments 1 and 2. But the data is slightly different. The threshold is 2T, where 2T only applies to examples 1 and 2.
As shown in fig. 2, the system 200 includes:
the data obtaining unit 210 is configured to traverse each node after initializing the node information, and calculate a node residual space and an average residual space;
a difference calculation unit 220 configured to calculate a difference between the average remaining space and the remaining space of each node;
a migration amount calculation unit 230 configured to obtain a rebalance threshold of the cluster, calculate a migration amount according to the difference and the threshold, and output the migration amount;
and the data migration unit 240 is configured to perform data equalization according to the migration volume of each node, so as to implement data equalization of the remaining space.
Optionally, as an embodiment of the present invention, the data obtaining unit includes:
the node use space calculation module is configured to acquire the total space of each node and the utilization rate of the cluster and calculate the use space of each node;
the node residual space calculation module is configured for calculating the difference between the total space of the nodes and the use space of the nodes to obtain the residual space of each node;
the cluster total residual space calculation module is configured to calculate the sum of the residual spaces of all the nodes to obtain the total residual space of the cluster;
and the average residual space calculation module is configured to obtain an average residual space according to the total residual space of the cluster and the number of the nodes.
Optionally, as an embodiment of the present invention, the migration amount calculation unit includes:
a threshold acquisition module configured to acquire a rebalance threshold of the cluster;
and the migration quantity calculation module is configured to calculate the difference between the absolute value of the difference value and the threshold value as the migration quantity of data balance.
Fig. 3 is a schematic structural diagram of a terminal system 300 according to an embodiment of the present invention, where the terminal system 300 may be used to execute the HDFS-based data equalization optimization method according to the embodiment of the present invention.
The terminal system 300 may include: a processor 310, a memory 320, and a communication unit 330. The components communicate via one or more buses, and those skilled in the art will appreciate that the architecture of the servers shown in the figures is not intended to be limiting, and may be a bus architecture, a star architecture, a combination of more or less components than those shown, or a different arrangement of components.
The memory 320 may be used for storing instructions executed by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile storage terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The executable instructions in memory 320, when executed by processor 310, enable terminal 300 to perform some or all of the steps in the method embodiments described below.
The processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by operating or executing software programs and/or modules stored in the memory 320 and calling data stored in the memory. The processor may be composed of an Integrated Circuit (IC), for example, a single packaged IC, or a plurality of packaged ICs connected with the same or different functions. For example, the processor 310 may include only a Central Processing Unit (CPU). In the embodiment of the present invention, the CPU may be a single operation core, or may include multiple operation cores.
A communication unit 330, configured to establish a communication channel so that the storage terminal can communicate with other terminals. And receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Therefore, the data balance optimization method, the data balance optimization system, the data balance optimization terminal and the data balance optimization storage medium based on the HDFS calculate the average residual space, enable the residual space of each node to be equally divided instead of adopting the method of balancing the residual space of each node in the same cluster according to the occupation ratio, improve the utilization rate of the residual space of the cluster, and improve the overall operation efficiency of the cluster; meanwhile, the invention also provides a selectable range for the migration quantity of the node according to the rebalance threshold value, thereby avoiding the situation that some data are used and can not be migrated; partial performance of the blancer tool of the HDFS is optimized, and convenience is provided for related development of the HDFS. The technical effects achieved by the present embodiment can be referred to the above description, and are not described herein again.
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention.
The same and similar parts in the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
In the embodiments provided by the present invention, it should be understood that the disclosed system, system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A data equalization optimization method based on HDFS is characterized by comprising the following steps:
after initializing node information, traversing each node, and calculating the residual space and the average residual space of the node;
calculating the difference value between the average residual space and the residual space of each node;
acquiring a rebalance threshold of the cluster, calculating a migration amount according to the difference and the threshold, and outputting the migration amount;
and data equalization is carried out according to the migration quantity of each node, so that the data equalization of the residual space is realized.
2. The HDFS-based data equalization optimization method according to claim 1, wherein the computing node residual space and the average residual space comprises the following steps:
acquiring the total space of each node and the utilization rate of the cluster, and calculating the use space of each node;
calculating the difference between the total space of the nodes and the use space of the nodes to obtain the residual space of each node;
calculating the sum of the residual spaces of all the nodes to obtain the total residual space of the cluster;
and obtaining the average residual space according to the total residual space of the cluster and the number of the nodes.
3. The HDFS-based data equalization optimization method according to claim 1, wherein the obtaining a rebalance threshold of the cluster, calculating a migration amount according to the difference and the threshold, and outputting the migration amount includes:
acquiring a rebalance threshold of the cluster;
and calculating the difference between the absolute value of the difference value and the threshold value as the migration quantity of data equalization.
4. An HDFS-based data equalization optimization system, comprising:
the data acquisition unit is configured for traversing each node after initializing the node information, and calculating the node residual space and the average residual space;
a difference value calculation unit configured to calculate a difference value between the average remaining space and the remaining space of each node;
the migration amount calculation unit is used for configuring a rebalance threshold value used for acquiring the cluster, calculating the migration amount according to the difference value and the threshold value and outputting the migration amount;
and the data migration unit is configured to perform data balance according to the migration volume of each node, so as to realize data balance of the residual space.
5. The HDFS-based data equalization optimization system according to claim 4, wherein the data acquisition unit comprises
The node use space calculation module is configured to acquire the total space of each node and the utilization rate of the cluster and calculate the use space of each node;
the node residual space calculation module is configured for calculating the difference between the total space of the nodes and the use space of the nodes to obtain the residual space of each node;
the cluster total residual space calculation module is configured to calculate the sum of the residual spaces of all the nodes to obtain the total residual space of the cluster;
and the average residual space calculation module is configured to obtain an average residual space according to the total residual space of the cluster and the number of the nodes.
6. The HDFS-based data equalization optimization system according to claim 4, wherein the migration amount calculation unit includes:
a threshold acquisition module configured to acquire a rebalance threshold of the cluster;
and the migration quantity calculation module is configured to calculate the difference between the absolute value of the difference value and the threshold value as the migration quantity of data balance.
7. A terminal, comprising:
a processor;
a memory for storing instructions for execution by the processor;
wherein the processor is configured to perform the method of any one of claims 1-3.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-3.
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