A performance data cache management method, device, equipment and storage medium

Abstract

The present application discloses a performance data cache management method, apparatus, device and storage medium, which are applied to a cluster, wherein the cluster is used to run distributed storage management software, and relates to the field of distributed storage management software and performance information cache. The method comprises: determining the master node in the current cluster and creating a database table on the master node; classifying the performance data to obtain a performance index of the database table; wherein the performance data is data for monitoring the running status of the distributed storage management software; assigning different slave nodes to the performance index, and synchronizing the performance data of the same group of slave nodes corresponding to the same performance index; when the master node receives a query request related to a performance data type, it determines the corresponding slave node in the database table according to the performance index and returns the corresponding performance data. The technical solution of the present application can avoid the problem of excessive performance data in large-scale clusters, which occupies a large amount of system space.

Description

Technical Field

[0001] The present invention relates to the fields of distributed storage management software and performance information cache, and in particular to a performance data cache management method, device, equipment and storage medium. Background Art

[0002] In distributed storage management software, in order to monitor the real-time and historical operating status of the cluster, it is usually necessary to cache various types of system performance data. To ensure the accuracy and timeliness of the data, the performance cache data needs to be regularly updated through scheduled tasks. Usually, all cached data is stored and updated on the master node and synchronized to other nodes. When the cache scheduled update task is executed, a large amount of performance data will be queried and stored on the master node at the same time, which will cause a sudden increase in the node's CPU and memory pressure, affecting node performance. At the same time, when the cluster scale is large, the cache of historical performance data will take up a large amount of storage space, which may lead to insufficient system disk space and prevent the system from operating normally. In addition, the synchronization of large amounts of performance data between nodes will also occupy a large amount of bandwidth, affecting the efficiency of software operation.

[0003] In summary, how to disperse the pressure on the master node, reduce the pressure on cluster performance data updates and the storage space occupied by historical performance data, and avoid excessive performance data in large-scale clusters that takes up a large amount of system space are currently unresolved issues. Summary of the Invention

[0004] In view of this, the present invention aims to provide a performance data cache management method, apparatus, device, and storage medium that can distribute the pressure on the master node, reduce the pressure on cluster performance data updates and the storage space occupied by historical performance data, and avoid the excessive performance data in large clusters that consumes a large amount of system space. The specific solution is as follows:

[0005] In a first aspect, the present application discloses a performance data cache management method, which is applied to a cluster, wherein the cluster is used to run distributed storage management software, comprising:

[0006] Determine the master node in the current cluster and create a database table on the master node;

[0007] Classifying the performance data to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software;

[0008] Allocating different slave nodes to the performance indexes and synchronizing performance data of the same group of slave nodes corresponding to the same performance index; wherein the slave nodes in the same performance index are divided into primary and backup, and the slave nodes can be reused in other performance indexes;

[0009] When the master node receives a request for querying data related to the performance data type, it determines the corresponding slave node in the database table according to the performance index and returns the corresponding performance data.

[0010] Optionally, classifying the performance data to obtain the performance index of the database table includes:

[0011] Determining a performance data time type according to the time of the performance data;

[0012] Classifying different performance indicators in the performance data based on the performance data time type to obtain a performance data indicator type corresponding to the performance data time type;

[0013] The performance data indicator type is added to the database table to obtain a performance index of the database table.

[0014] Optionally, allocating different slave nodes to the performance indexes and synchronizing performance data of the same group of slave nodes corresponding to the same performance indexes includes:

[0015] Allocate different slave nodes to the performance index through a preset configuration interface;

[0016] The data change monitoring script is used to monitor the data changes in the cluster, and the performance data of the same group of slave nodes corresponding to the same performance index are synchronized.

[0017] Optionally, the step of monitoring data changes in the cluster using a data change monitoring script and synchronizing performance data of a same group of slave nodes corresponding to the same performance index includes:

[0018] When the performance data in the same group of slave nodes is modified and / or the cluster is expanded or reduced in capacity, the corresponding performance data in the database table is modified and synchronized.

[0019] Optionally, the performance data cache management method further includes:

[0020] The load of all nodes in the cluster is monitored regularly, and when the load meets a preset condition, a target node corresponding to the load is determined.

[0021] Optionally, the periodically monitoring the load of all nodes in the cluster includes:

[0022] Regularly monitor the CPU, memory, and bandwidth of all nodes in the cluster.

[0023] Optionally, when the load meets a preset condition, after determining the target node corresponding to the load, the method further includes:

[0024] The performance data in the target node is cached to the node with the lowest load in the same group of slave nodes corresponding to the same performance index.

[0025] In a second aspect, the present application discloses a performance data cache management device, which is applied to a cluster, wherein the cluster is used to run distributed storage management software, including:

[0026] A database table creation module is used to determine the master node in the current cluster and create a database table on the master node;

[0027] A performance index determination module, configured to classify performance data to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software;

[0028] A data synchronization module is used to assign different slave nodes to the performance index and synchronize the performance data of the same group of slave nodes corresponding to the same performance index; wherein the slave nodes in the same performance index are divided into primary and backup, and the slave nodes can be reused in other performance indexes;

[0029] The performance data query module is used to determine the corresponding slave node in the database table according to the performance index when the master node receives a request for querying the performance data type, and return the corresponding performance data.

[0030] In a third aspect, the present application discloses an electronic device comprising a processor and a memory; wherein the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the performance data cache management method as described above.

[0031] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the performance data cache management method as described above.

[0032] In this application, the cluster is used to run distributed storage management software. First, the master node in the current cluster is determined and a database table is created on the master node. Then, performance data is classified to obtain a performance index of the database table. The performance data is data used to monitor the running status of the distributed storage management software. Different slave nodes are assigned to the performance indexes, and the performance data of the same group of slave nodes corresponding to the same performance index are synchronized. The slave nodes in the same performance index are divided into master and backup, and the slave nodes can be reused in other performance indexes. When the master node receives a query request related to the performance data type, it determines the corresponding slave node in the database table based on the performance index and returns the corresponding performance data. It can be seen that the cache of performance data of the distributed storage management software running in the cluster is no longer entirely the responsibility of the master node. Instead, a database table is created on the master node. By classifying the performance data and generating performance indexes, the performance data is distributed to different slave nodes, each of which is responsible for different performance data. The master node allocates the caching task of the performance data through the database and plans which nodes are responsible for managing different categories of performance data. When querying performance data, the master node summarizes it according to the index. This distributes the pressure on the master node, reducing the cluster's performance data update pressure and the storage space occupied by historical performance data. This avoids the problem of excessive performance data in large clusters, which consumes a large amount of system space, and improves system stability. Furthermore, since each type of performance data is managed by a reusable group of nodes to ensure disaster recovery, different nodes in the same group are assigned to primary and backup roles, and performance data is synchronized across the group, preventing performance data loss due to a single node failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0034] Figure 1 This is a flow chart of a performance data cache management method disclosed in this application;

[0035] Figure 2 This is a flow chart of a specific performance data cache management method disclosed in this application;

[0036] Figure 3 This is a schematic diagram of the structure of a performance data cache management device disclosed in this application;

[0037] Figure 4 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Currently, distributed storage management software typically requires caching various system performance data to monitor the real-time and historical status of a cluster. The master node is solely responsible for collecting and storing this performance data, which can lead to significant CPU and memory pressure on the node, impacting performance. Furthermore, large clusters can lead to insufficient system disk space, preventing proper system operation.

[0040] To this end, this application provides a performance data cache management solution that can disperse the pressure on the master node, reduce the update pressure of the cluster's performance data and the storage space occupied by historical performance data, and avoid the occurrence of excessive performance data in large-scale clusters, which occupies a large amount of system space.

[0041] The embodiment of the present invention discloses a performance data cache management method, see Figure 1 As shown, the method is applied to a cluster, the cluster is used to run distributed storage management software, and includes:

[0042] Step S11: Determine the current master node in the cluster and create a database table on the master node.

[0043] In an embodiment of the present application, when a cluster is deployed, the master node in the current cluster is determined, and a database table is created on the master node, so that the master node can find the corresponding data and return information based on the content of the records in the database table after receiving a request to query performance data.

[0044] Step S12: Classify the performance data to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software.

[0045] In the embodiment of the present application, during cluster operation, the collection and storage of performance data is no longer solely the responsibility of the master node, but different nodes are responsible for different performance data. Therefore, the performance data is classified to obtain the performance index of the database table.

[0046] In the embodiment of the present application, the performance data is classified based on the type and time of the performance data. Specifically, the performance data time type is determined based on the time of the performance data; based on the performance data time type, different performance indicators in the performance data are classified to obtain performance data indicator types corresponding to the performance data time type; and the performance data indicator types are added to the database table to obtain a performance index for the database table.

[0047] It is understood that performance data time types can be applied to different scenarios. For example, real-time performance data can be used for fault location or short-term performance monitoring, while historical performance data can be used to analyze the network's medium- and long-term operational trends. By categorizing different performance indicators based on the performance data time type, such as IO (Input / Output), bandwidth, and capacity, the performance data indicator type corresponding to the performance data time type can be obtained. In other words, the performance data indicator type corresponding to the performance data time type is the performance index in the database table.

[0048] Step S13: assign different slave nodes to the performance index, and synchronize the performance data of the same group of slave nodes corresponding to the same performance index; wherein, the slave nodes in the same performance index are divided into master and backup, and the slave nodes can be reused in other performance indexes.

[0049] In an embodiment of the present application, the index table is manually configured during cluster deployment. Specifically, different slave nodes are assigned to the performance index through a preset configuration interface. Different types of performance data correspond to a node list, which is responsible for updating and storing such data. In order to save nodes, each node is reusable and can belong to different groups. At the same time, different nodes in the same group are divided into primary and backup roles, so that each type of performance data is responsible for disaster recovery by a group of reusable nodes.

[0050] Furthermore, a data change monitoring script is used to monitor data changes in the cluster and synchronize the performance data of the same group of slave nodes corresponding to the same performance index. In an embodiment of the present application, when the performance data in the same group of slave nodes is modified and / or the cluster is expanded or reduced in capacity, this database table needs to be modified synchronously. Table 1 shows an exemplary index structure table.

[0051] Table 1

[0052] Performance data node Cluster IO node1,node2 Storage pool capacity node3,node4 Average CPU utilization node1,node3 ··· ···

[0053] Table 1 shows that the performance data columns in the performance index correspondence table can be categorized based on cluster IO, storage pool capacity, and average CPU (Central Processing Unit) utilization. When adding slave nodes, node 1 appears in the cluster IO and average CPU utilization rows, and node 3 appears in the storage pool capacity and average CPU utilization rows, indicating that all nodes are reusable. Nodes in the same group are divided into primary and secondary nodes, and performance data is synchronized across the group. If one node fails, the remaining nodes can be used for disaster recovery, preventing performance data loss due to node failure.

[0054] Step S14: When the master node receives a request for querying the performance data type, it determines the corresponding slave node in the database table according to the performance index and returns the corresponding performance data.

[0055] In the embodiment of the present application, different types of performance data (such as IO, bandwidth, capacity, etc.) are assigned to different groups of nodes for updating and storage, and then managed uniformly by the master node. When querying performance data, the master node only needs to find the corresponding node that updates and stores the performance data based on the performance index recorded in the database, then query the specific node for information and return the information.

[0056] In this application, the cluster is used to run distributed storage management software. First, the master node in the current cluster is determined and a database table is created on the master node. Then, performance data is classified to obtain a performance index of the database table. The performance data is data used to monitor the running status of the distributed storage management software. Different slave nodes are assigned to the performance indexes, and the performance data of the same group of slave nodes corresponding to the same performance index are synchronized. The slave nodes in the same performance index are divided into master and backup, and the slave nodes can be reused in other performance indexes. When the master node receives a query request related to the performance data type, it determines the corresponding slave node in the database table based on the performance index and returns the corresponding performance data. It can be seen that the cache of performance data of the distributed storage management software running in the cluster is no longer entirely the responsibility of the master node. Instead, a database table is created on the master node. By classifying the performance data and generating performance indexes, the performance data is distributed to different slave nodes, each of which is responsible for different performance data. The master node allocates the caching task of the performance data through the database and plans which nodes are responsible for managing different categories of performance data. When querying performance data, the master node summarizes it according to the index. This distributes the pressure on the master node, reducing the cluster's performance data update pressure and the storage space occupied by historical performance data. This avoids the problem of excessive performance data in large clusters, which consumes a large amount of system space, and improves system stability. Furthermore, since each type of performance data is managed by a reusable group of nodes to ensure disaster recovery, different nodes in the same group are assigned to primary and backup roles, and performance data is synchronized across the group, preventing performance data loss due to a single node failure.

[0057] The present application discloses a specific performance data cache management method, see Figure 2 As shown, the method includes:

[0058] Step S21: Determine the master node in the current cluster and create a database table on the master node.

[0059] Step S22: Classify the performance data to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software.

[0060] Step S23: assign different slave nodes to the performance index, and synchronize the performance data of the same group of slave nodes corresponding to the same performance index; wherein, the slave nodes in the same performance index are divided into master and backup, and the slave nodes can be reused in other performance indexes.

[0061] For more specific processing procedures of the above-mentioned steps S21, S22, and S23, reference may be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be elaborated here.

[0062] Step S24: regularly monitoring the load of all nodes in the cluster, and when the load meets a preset condition, determining the target node corresponding to the load.

[0063] In an embodiment of the present application, the cluster periodically monitors load and switches performance cache nodes during operation, promptly switching performance cache tasks from high-load nodes to low-load nodes. Specifically, when periodically monitoring the load of all nodes in the cluster, CPU, memory, and bandwidth are monitored. If a node currently updating and storing cached data is detected to have a high load, that is, if the load meets a preset condition, the target node corresponding to the load is determined.

[0064] Step S25: Cache the performance data in the target node to the node with the lowest load in the same group of slave nodes corresponding to the same performance index.

[0065] In an embodiment of the present application, if it is found that the load of the node currently performing cache data update and storage is high, the node responsible for caching performance data is promptly cached to the node with the lowest load in the same group, and the task is switched to the low-load node, thereby reducing the load of the node and reducing the system disk space occupied by the storage of historical performance data.

[0066] Step S26: When the master node receives a request for querying the performance data type, it determines the corresponding slave node in the database table according to the performance index and returns the corresponding performance data.

[0067] For a more specific processing procedure of the above step S26, reference may be made to the corresponding contents disclosed in the above embodiments, which will not be described again here.

[0068] In the present application, it is applied to a cluster, which is used to run distributed storage management software. First, the master node in the current cluster is determined, and a database table is created on the master node; then the performance data is classified to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software; different slave nodes are assigned to the performance index, and the performance data of the same group of slave nodes corresponding to the same performance index are synchronized; wherein the slave nodes in the same performance index are divided into master and backup, and the slave nodes can be reused in other performance indexes; the load of all nodes in the cluster is monitored regularly, and when the load meets the preset conditions, the target node corresponding to the load is determined; the performance data in the target node is cached to the node with the lowest load in the same group of slave nodes corresponding to the same performance index; when the master node receives a query request related to the performance data type, it determines the corresponding slave node in the database table according to the performance index and returns the corresponding performance data. As can be seen, the distributed storage management software running in the cluster no longer solely caches performance data on the master node. Instead, a database table is created on the master node. By categorizing performance data and generating performance indexes, the data is distributed to different slave nodes, each responsible for different performance data types. The master node then uses the database to allocate performance data caching tasks and determine which nodes are responsible for managing different types of performance data. When querying performance data, the master node aggregates the data based on the indexes. This distributes the pressure on the master node, reducing the cluster's performance data update pressure and the storage space occupied by historical performance data. This prevents the problem of excessive performance data in large clusters, which consumes a large amount of system space, and improves system stability. Furthermore, since each type of performance data is managed by a reusable group of nodes to ensure disaster recovery, different nodes in the same group are assigned primary and backup roles, and performance data is synchronized across the group, preventing performance data loss caused by a single node failure. Furthermore, since node load is regularly monitored during cluster operation, performance data from nodes with the highest current load is promptly cached on nodes with the lowest load in the group, reducing node load.

[0069] Accordingly, the embodiment of the present application also discloses a performance data cache management device, which is applied to a cluster, wherein the cluster is used to run distributed storage management software, see Figure 3 As shown, the device includes:

[0070] A database table creation module 11 is used to determine the master node in the current cluster and create a database table on the master node;

[0071] A performance index determination module 12 is configured to classify the performance data to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software;

[0072] A data synchronization module 13 is configured to allocate different slave nodes to the performance index and synchronize performance data of the same group of slave nodes corresponding to the same performance index; wherein the slave nodes in the same performance index are divided into primary and backup, and the slave nodes can be reused in other performance indexes;

[0073] The performance data query module 14 is configured to determine the corresponding slave node in the database table according to the performance index when the master node receives a request for querying the performance data type, and return the corresponding performance data.

[0074] Among them, for more specific working processes of the above modules, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0075] Thus, it can be seen that the above scheme of this embodiment is applied to a cluster, and the cluster is used to run distributed storage management software. First, the master node in the current cluster is determined, and a database table is created on the master node; then, performance data is classified to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software; different slave nodes are assigned to the performance index, and the performance data of the same group of slave nodes corresponding to the same performance index are synchronized; wherein the slave nodes in the same performance index are divided into master and backup, and the slave nodes can be reused in other performance indexes; when the master node receives a query request related to the performance data type, it determines the corresponding slave node in the database table according to the performance index and returns the corresponding performance data. It can be seen that the cache of performance data of the distributed storage management software running in the cluster is no longer entirely the responsibility of the master node, but a database table is created on the master node, and by classifying the performance data and generating performance indexes, different slave nodes are assigned to be responsible for different performance data. The master node allocates the cache task of the performance data through the database and plans which nodes are responsible for managing different categories of performance data. When querying performance data, the master node summarizes it according to the index. This distributes the pressure on the master node, reducing the cluster's performance data update pressure and the storage space occupied by historical performance data. This avoids the problem of excessive performance data in large clusters, which consumes a large amount of system space, and improves system stability. Furthermore, since each type of performance data is managed by a reusable group of nodes to ensure disaster recovery, different nodes in the same group are assigned to primary and backup roles, and performance data is synchronized across the group, preventing performance data loss due to a single node failure.

[0076] In a specific implementation, the performance index determination module 12 includes:

[0077] a performance data time type determining unit, configured to determine a performance data time type according to the time of the performance data;

[0078] a performance data indicator type determining unit, configured to classify different performance indicators in the performance data based on the performance data time type, to obtain a performance data indicator type corresponding to the performance data time type;

[0079] A performance index determining unit is configured to add the performance data indicator type to the database table to obtain a performance index of the database table.

[0080] In a specific implementation, the data synchronization module 13 includes:

[0081] A slave node allocation unit, configured to allocate different slave nodes to the performance index through a preset configuration interface;

[0082] The performance data synchronization unit is used to monitor the data changes in the cluster using a data change monitoring script, and synchronize the performance data of the same group of slave nodes corresponding to the same performance index.

[0083] In a specific embodiment, the performance data synchronization unit is used to modify and synchronize the corresponding performance data in the database table when the performance data in the same group of slave nodes is modified and / or the cluster is expanded or reduced in capacity.

[0084] In a specific embodiment, the performance data cache management device further includes:

[0085] The timing monitoring module is used to regularly monitor the load of all nodes in the cluster, and when the load meets a preset condition, determine the target node corresponding to the load.

[0086] In a specific implementation, the timing monitoring module is used to regularly monitor the CPU, memory, and bandwidth of all nodes in the cluster.

[0087] In a specific embodiment, the timing monitoring module further includes:

[0088] The load switching module is used to cache the performance data in the target node to the node with the lowest load in the same group of slave nodes corresponding to the same performance index.

[0089] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 4This is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the content in the diagram cannot be considered as any limitation to the scope of use of the present application.

[0090] Figure 4 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the performance data cache management method disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be a server.

[0091] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0092] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, a magnetic disk, or an optical disk. The resources stored thereon may include an operating system 221, a computer program 222, and data 223. The data 223 may include various data. The storage method can be temporary storage or permanent storage.

[0093] The operating system 221 is used to manage and control the hardware devices on the electronic device 20 and the computer program 222, and can be Windows Server, NetWare, Unix, Linux, etc. In addition to including a computer program capable of implementing the performance data cache management method disclosed in any of the aforementioned embodiments and executed by the electronic device 20, the computer program 222 can further include a computer program capable of implementing other specific tasks.

[0094] Furthermore, the embodiments of the present application also disclose a computer-readable storage medium, where the computer-readable storage medium includes a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a magnetic disk or an optical disk, or any other form of storage medium known in the technical field. Wherein, when the computer program is executed by the processor, the aforementioned performance data cache management method is implemented. For the specific steps of the method, reference can be made to the corresponding content disclosed in the aforementioned embodiments, and no further details will be given here.

[0095] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0096] The performance data cache management or algorithm steps described in conjunction with the embodiments disclosed herein can be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0097] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0098] The above is a detailed introduction to the performance data cache management method, device, equipment and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for general technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A performance data cache management method, characterized in that: Applied to a cluster that runs distributed storage management software, including: Determine the master node in the current cluster and create a database table on the master node; Classifying the performance data to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software; Allocating different slave nodes to the performance indexes and synchronizing performance data of the same group of slave nodes corresponding to the same performance index; wherein the slave nodes in the same performance index are divided into primary and backup, and the slave nodes can be reused in other performance indexes; When the master node receives a request for querying the performance data type, it determines the corresponding slave node in the database table according to the performance index and returns the corresponding performance data; The allocating different slave nodes to the performance indexes and synchronizing performance data of the same group of slave nodes corresponding to the same performance indexes includes: Allocate different slave nodes to the performance index through a preset configuration interface; Using a data change monitoring script to monitor data changes in the cluster, and synchronizing performance data of the same group of slave nodes corresponding to the same performance index; When the performance data in the same group of slave nodes is modified and / or the cluster is expanded or reduced in capacity, the corresponding performance data in the database table is modified and synchronized.

2. The performance data cache management method according to claim 1, characterized in that: The classifying the performance data to obtain the performance index of the database table includes: Determining a performance data time type according to the time of the performance data; Classifying different performance indicators in the performance data based on the performance data time type to obtain a performance data indicator type corresponding to the performance data time type; The performance data indicator type is added to the database table to obtain a performance index of the database table.

3. The performance data cache management method according to claim 1 or 2, characterized in that: Also includes: The load of all nodes in the cluster is monitored regularly, and when the load meets a preset condition, a target node corresponding to the load is determined.

4. The performance data cache management method according to claim 3, characterized in that: The periodic monitoring of the load of all nodes in the cluster includes: Regularly monitor the CPU, memory, and bandwidth of all nodes in the cluster.

5. The performance data cache management method according to claim 3, characterized in that: When the load satisfies a preset condition, after determining the target node corresponding to the load, the method further includes: The performance data in the target node is cached to the node with the lowest load in the same group of slave nodes corresponding to the same performance index.

6. A performance data cache management device, characterized in that: Applied to a cluster that runs distributed storage management software, including: A database table creation module is used to determine the master node in the current cluster and create a database table on the master node; A performance index determination module, configured to classify performance data to obtain a performance index of the database table; wherein the performance data is data used to monitor the running status of the distributed storage management software; A data synchronization module is used to assign different slave nodes to the performance index and synchronize the performance data of the same group of slave nodes corresponding to the same performance index; wherein the slave nodes in the same performance index are divided into primary and backup, and the slave nodes can be reused in other performance indexes; A performance data query module is configured to, when the master node receives a query request related to the performance data type, determine the corresponding slave node in the database table according to the performance index and return the corresponding performance data; The data synchronization module includes: A slave node allocation unit, configured to allocate different slave nodes to the performance index through a preset configuration interface; A performance data synchronization unit is used to monitor data changes in the cluster using a data change monitoring script and synchronize performance data of the same group of slave nodes corresponding to the same performance index; The performance data synchronization unit is configured to modify and synchronize the corresponding performance data in the database table when the performance data in the same group of slave nodes is modified and / or the cluster is expanded or reduced in capacity.

7. An electronic device, characterized in that: The electronic device includes a processor and a memory; wherein the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the performance data cache management method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that Used to store a computer program; wherein when the computer program is executed by a processor, the performance data cache management method according to any one of claims 1 to 5 is implemented.

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