Multi-data center mutual backup monitoring method and device, computer equipment, readable storage medium and program product
By establishing a mutual backup monitoring mechanism between data centers and using cache queues and timing databases to realize monitoring and alarms across data centers, the problem of low stability of monitoring and alarms in traditional technologies is solved, and the efficiency of fault detection and troubleshooting is improved , and optimize the use of network and storage resources.
Patent Information
- Application Number
- CN202411693756.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-02-18
AI Technical Summary
In traditional technology, monitoring and alarming of data centers is not very stable, especially in scenarios such as network blockage, service burst traffic, power supply or network failure, which makes it difficult to quickly detect and troubleshoot data center failures.
Using the multi-data center mutual backup monitoring method, by reading the monitoring indicator data of each component in the first data center, determining the overall risk status, and writing the target indicator data to the cache queue in the cloud, the second data center is cached. Data is read from the queue and written to the timing database to achieve low-cost cross-monitoring and mutual backup monitoring across data centers.
It improves the stability of data center monitoring alarms, ensures that problems can be quickly discovered and troubleshooted in the event of failure, reduces the use of network bandwidth and storage capacity, and reduces redundant data transmission when risks are low, and improves system efficiency.
Smart Images
Figure CN119473797A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of computer technology, and in particular to a data center mutual backup monitoring method, device, computer equipment, computer-readable storage medium, and computer program product. Background Art
[0002] With the development of technologies such as the Internet, cloud computing, and big data, software products based on hybrid cloud architecture have generated a strong demand for data monitoring and analysis in various business scenarios to ensure high data availability and business continuity.
[0003] Traditional technology mainly deploys independent monitoring systems in each data center. However, this method often causes the monitoring system of the entire data center to lose connection in scenarios such as network congestion, sudden business traffic, power supply or network failure. It cannot support the rapid discovery and troubleshooting of data center failures, making the monitoring and alarm of the data center unstable.
[0004] Therefore, there is a problem in the traditional technology that the monitoring and alarm of the data center is not stable. Summary of the invention
[0005] Based on this, it is necessary to provide a data center mutual backup monitoring method, device, computer equipment, computer readable storage medium and computer program product that can improve the stability of monitoring and alarming of data centers in response to the above technical problems.
[0006] In a first aspect, the present application provides a multi-data center mutual backup monitoring method, which is applied to a first data center, where the first data center is one of the multiple data centers, and the data center is configured with a monitoring system, and the method includes:
[0007] Reading monitoring indicator data of each component in the first data center;
[0008] Determining an overall risk status of the first data center according to the monitoring indicator data of each component;
[0009] According to the overall risk status, writing the target indicator data corresponding to the first data center into the first cache queue; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself;
[0010] Among them, the first cache queue is the indicator data cache queue corresponding to the first data center in the cloud; the first cache queue is communicatively connected to the second data center, and the second data center is another data center of the multiple data centers. The second data center is used to write the indicator data cached in the first cache queue to the time series database of the second data center, so as to monitor and alarm the first data center through the second data center.
[0011] In one embodiment, the first data center is in communication with a second cache queue, the second cache queue is an indicator data cache queue corresponding to the second data center in the cloud, and the method further includes:
[0012] Read the index data cached in the second cache queue as the target index data corresponding to the second data center; the target index data includes the full amount of index data of the data center or the index data of the monitoring system itself;
[0013] The target indicator data corresponding to the second data center is written into the time series database of the first data center so as to monitor and alarm the second data center through the first data center.
[0014] In one embodiment, the monitoring system includes a monitoring component and an alarm component, and the method further includes:
[0015] The monitoring component collects the indicator data of the business services of the first data center, the indicator data of the alarm component, the indicator data of the time series database, the indicator data of the intelligent converter, the indicator data of the data processor, and the indicator data of the monitoring component itself, and stores them as the monitoring indicator data of each component in the first preset directory of the local disk of the monitoring system;
[0016] The alarm component monitors the indicator data in the first preset directory and whether the indicator data in the target preset directory triggers the alarm rules; the target preset directory is a preset directory that stores the target indicator data corresponding to the second data center in the time series database of the first data center.
[0017] In one embodiment, the monitoring system includes a monitoring component and an alarm component, and the method further includes:
[0018] The monitoring component collects the indicator data of the business services of the first data center, the indicator data of the alarm component, the indicator data of the time series database, the indicator data of the intelligent converter, the indicator data of the data processor, and the indicator data of the monitoring component itself, and stores them as the monitoring indicator data of each component in a second preset directory of the time series database of the first data center;
[0019] The alarm component monitors the indicator data in the second preset directory and whether the indicator data in the target preset directory triggers the alarm rules; the target preset directory is a preset directory that stores the target indicator data corresponding to the second data center in the time series database of the first data center.
[0020] In one embodiment, writing the target indicator data corresponding to the first data center to the first cache queue according to the overall risk status includes:
[0021] Acquire risk level information of the overall risk status; the risk level information is used to characterize the first risk or the second risk; the first risk is greater than the second risk;
[0022] When the risk level information represents the first risk, writing the full amount of data center indicator data corresponding to the first data center into the first cache queue;
[0023] In a case where the risk level information represents the second risk, the monitoring system's own indicator data corresponding to the first data center is written into the first cache queue.
[0024] In one embodiment, determining the overall risk status of the first data center according to the monitoring indicator data of each component includes:
[0025] According to the monitoring indicator data of each component, a preset sliding window is used to determine the disk usage rate, business-critical event growth rate and local alarm queue depth of the first data center;
[0026] According to the disk usage, the growth rate of business-critical events, and the local alarm queue depth, the overall risk status of the first data center is determined to be the first risk or the second risk.
[0027] In a second aspect, the present application further provides a multi-data center mutual backup monitoring device, which is applied to a first data center, where the first data center is one of the multiple data centers, and the data center is configured with a monitoring system, and the device includes:
[0028] A reading module, used to read monitoring indicator data of each component in the first data center;
[0029] A risk status monitoring module, used to determine the overall risk status of the first data center based on the monitoring indicator data of each component;
[0030] A writing module, used for writing the target indicator data corresponding to the first data center into the first cache queue according to the overall risk status; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself;
[0031] Among them, the first cache queue is the indicator data cache queue corresponding to the first data center in the cloud; the first cache queue is communicatively connected to the second data center, and the second data center is another data center of the multiple data centers, and the second data center is used to write the indicator data cached in the first cache queue into the time series database of the second data center.
[0032] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.
[0033] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.
[0034] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.
[0035] The above-mentioned multi-data center mutual backup monitoring method, device, computer equipment, computer-readable storage medium and computer program product are applied to a first data center, which is one of the multiple data centers. Each data center is equipped with a monitoring system. The first data center determines the overall risk status of the first data center by reading the monitoring indicator data of each component in the first data center and based on the monitoring indicator data of each component; the first data center then writes the target indicator data corresponding to the first data center into the first cache queue based on the overall risk status; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself; wherein the first cache queue is the indicator data cache queue corresponding to the first data center in the cloud; the first cache queue is communicatively connected to the second data center, which is another data center in the multiple data centers, and the second data center is used to write the indicator data cached in the first cache queue into the time series database of the second data center, so as to monitor and alarm the first data center through the second data center; in this way, the effect of low-cost cross-monitoring and mutual backup monitoring between the first data center and the second data center in the multiple data centers can be achieved. In addition, the data center can back up the full amount of indicator data of the data center or the indicator data of the monitoring system itself to the time series database of the second data center according to the overall risk status. When the system risk is low, it can reduce redundant monitoring indicator data transmission and storage, thereby reducing network bandwidth occupancy and storage capacity occupancy. When the system risk increases, the full amount of monitoring indicator data can be backed up to the second data center in time, effectively providing technical support for timely risk warning and troubleshooting. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0037] FIG1 is an application environment diagram of a multi-data center mutual backup monitoring method in one embodiment;
[0038] FIG2 is a schematic flow chart of a multi-data center mutual backup monitoring method according to an embodiment;
[0039] FIG3 is a schematic diagram of a dual data center architecture in Example 1;
[0040] FIG4 is a schematic diagram of a flow chart of a multi-data center mutual backup monitoring method in Example 1;
[0041] FIG5 is a schematic diagram of a dual data center architecture in Example 2;
[0042] FIG6 is a schematic diagram of a flow chart of a multi-data center mutual backup monitoring method in Example 2;
[0043] FIG7 is a structural block diagram of a multi-data center mutual backup monitoring device in one embodiment;
[0044] FIG. 8 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION
[0045] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0046] The multi-data center mutual backup monitoring method provided in the embodiment of the present application can be applied to the application environment as shown in Figure 1. Among them, the first data center 102 and the second data center 104 both communicate with the cloud 106 through the network. Among them, the cloud 106 is configured with a first cache queue corresponding to the first data center 102, and a second cache queue corresponding to the second data center 104; the first data center 102 is communicatively connected to the second cache queue; the second data center 104 is communicatively connected to the first cache queue. The first data center 102 and the second data center 104 are both configured with a monitoring system; it can be understood that the terms "first", "second", etc. used in this application can be used in this article to describe various data centers, but these data centers are not limited by these terms. These terms are only used to distinguish the first data center from another data center.
[0047] In actual applications, for the first data center 102, the first data center 102 can read the monitoring indicator data of each component in the first data center 102; the first data center 102 can determine the overall risk status of the first data center 102 based on the monitoring indicator data of each component; the first data center 102 can write the target indicator data corresponding to the first data center 102 into the first cache queue based on the overall risk status; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself. In this way, the second data center 104 can write the indicator data cached in the first cache queue into the time series database of the second data center 104, so as to monitor and alarm the first data center 102 through the second data center 104.
[0048] For the second data center 104, the second data center 104 can read the monitoring indicator data of each component in the second data center 104; the second data center 1042 can determine the overall risk status of the second data center 104 according to the monitoring indicator data of each component; the second data center 104 can write the target indicator data corresponding to the second data center 104 into the second cache queue according to the overall risk status; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself. In this way, the first data center 102 can write the indicator data cached in the second cache queue into the time series database of the first data center 102, so as to monitor and alarm the second data center 104 through the first data center 102. Thereby, the effect of low-cost cross-monitoring and mutual backup monitoring between the first data center 102 and the second data center 104 is achieved. Among them, the first data center 102 and the second data center 104 can be independent physical servers, or they can be server clusters or distributed systems composed of multiple physical servers, or they can be cloud servers that provide cloud computing services.
[0049] In an exemplary embodiment, as shown in FIG2 , a multi-data center mutual backup monitoring method is provided, which is described by taking the method applied to the first data center 102 in FIG1 as an example, and includes the following steps S202 to S206. Among them:
[0050] Step S202, reading monitoring indicator data of each component in the first data center.
[0051] The component may refer to a component of the data center. For example, the component may include but is not limited to an intelligent converter Adapter, a data processor DataWorker, a monitoring system, a time series database, and a business service unit in the data center.
[0052] Among them, the monitoring system includes a monitoring component and an alarm component.
[0053] Among them, the intelligent converter Adapter is used to intelligently infer the risk status of the system according to preset conditions, and is a component device with the ability to convert monitoring data formats, compress data, and remotely write indicator data cache queues.
[0054] Among them, the data processor DataWorker is a component device capable of reading the index data cache queue, decompressing data, and converting data format.
[0055] In a specific implementation, the first data center can read the monitoring index data of each component collected by the monitoring system through the intelligent converter in the first data center. For example, the first data center is data center A, and data center A deploys a monitoring system. The monitoring component A in the monitoring system collects the index data of each component of data center A, such as business services (for example, service1~serviceN), alarm component A, time series data block library A, intelligent converter A, data processor A, and the metric interface of the monitoring component A itself, to obtain the monitoring index data of each component. Then, the intelligent converter A reads the monitoring index data of each component of data center A collected by the monitoring component A.
[0056] Step S204: determining the overall risk status of the first data center based on the monitoring indicator data of each component.
[0057] In a specific implementation, the intelligent converter of the first data center can analyze the monitoring indicator data of each component and intelligently infer the overall risk status of the first data center according to preset conditions. The overall risk status can be divided into a high risk status or a low risk status.
[0058] Step S206: write the target indicator data corresponding to the first data center into the first cache queue according to the overall risk status.
[0059] Among them, the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself.
[0060] The full amount of indicator data of the data center may refer to the full amount of monitoring indicator data of each component of the data center.
[0061] The monitoring system's own indicator data may refer to the monitoring system's own indicator data, including monitoring indicator data of monitoring components and monitoring indicator data of alarm components.
[0062] Among them, the first cache queue is the indicator data cache queue corresponding to the first data center in the cloud; the first cache queue can be communicated with the second data center 104 in Figure 1; the second data center is used to write the indicator data cached in the first cache queue to the time series database of the second data center, so as to monitor and alarm the first data center through the second data center.
[0063] In a specific implementation, the intelligent converter of the first data center can also write the full amount of data center indicator data corresponding to the first data center or the indicator data of the monitoring system itself into the first cache queue according to the overall risk status. Specifically, when the intelligent converter of the first data center determines that the overall risk status of the first data center is a high-risk state, the intelligent converter of the first data center can convert the full amount of data center indicator data corresponding to the first data center into JSON format (a data transmission format) and compress it, and write the processed full amount of data center indicator data into the indicator data cache queue corresponding to the first data center in the cloud, such as a Kafka queue.
[0064] When the first data center's intelligent converter determines that the overall risk status of the first data center is a low-risk status, the first data center's intelligent converter can filter out the monitoring indicator data of the monitoring component and the monitoring indicator data of the alarm component from the data center's full indicator data as the monitoring system's own indicator data. The first data center's intelligent converter converts the monitoring system's own indicator data into JSON format and compresses it, and writes the processed monitoring system's own indicator data to the first data center's corresponding indicator data cache queue in the cloud, such as a Kafka queue.
[0065] The data processor of the second data center can read the index data cached in the first cache queue, and the data processor of the second data center converts the index data cached in the first cache queue and writes it to the time series database of the second data center, so as to realize the dynamic backup of the monitoring index of the first data center to the time series database of the second data center on demand. In this way, the monitoring system of the second data center can monitor and alarm the full amount of data center index data corresponding to the first data center stored in the time series database of the second data center or the index data of the monitoring system itself, so as to realize the monitoring and alarm of the first data center through the second data center.
[0066] The above-mentioned multi-data center mutual backup monitoring method is applied to the first data center, which is one of the multiple data centers. Each data center is equipped with a monitoring system. The first data center determines the overall risk status of the first data center by reading the monitoring indicator data of each component in the first data center and according to the monitoring indicator data of each component; the first data center then writes the target indicator data corresponding to the first data center into the first cache queue according to the overall risk status; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself; wherein, the first cache queue is the indicator data cache queue corresponding to the first data center in the cloud; the first cache queue is communicatively connected with the second data center, which is another data center in the multiple data centers, and the second data center is used to write the indicator data cached in the first cache queue into the time series database of the second data center, so as to monitor and alarm the first data center through the second data center; in this way, the effect of low-cost cross-monitoring and mutual backup monitoring between the first data center and the second data center in the multiple data centers can be achieved. In addition, the data center can back up the full amount of indicator data of the data center or the indicator data of the monitoring system itself to the time series database of the second data center according to the overall risk status. When the system risk is low, it can reduce redundant monitoring indicator data transmission and storage, thereby reducing network bandwidth occupancy and storage capacity occupancy. When the system risk increases, the full amount of monitoring indicator data can be backed up to the second data center in time, effectively providing technical support for timely risk warning and troubleshooting.
[0067] In an exemplary embodiment, the first data center is communicatively connected with the second cache queue, and the second cache queue is the indicator data cache queue corresponding to the second data center in the cloud. The method also includes: reading the indicator data cached in the second cache queue as the target indicator data corresponding to the second data center; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself; writing the target indicator data corresponding to the second data center into the time series database of the first data center, so as to monitor and alarm the second data center through the first data center.
[0068] In a specific implementation, the monitoring indicators of the second data center can also be dynamically backed up on demand to the time series database of the first data center, so as to monitor and alarm the second data center through the first data center. In actual applications, the working principle of dynamically backing up the monitoring indicators of the second data center to the time series database of the first data center on demand is the same as the working principle of dynamically backing up the monitoring indicators of the first data center to the time series database of the second data center on demand in the above text; specifically, the intelligent converter of the second data center can analyze the monitoring indicator data of each component and intelligently infer the overall risk status of the second data center according to preset conditions. When the intelligent converter of the second data center determines that the overall risk status of the second data center is a high-risk state, the intelligent converter of the second data center can convert the full amount of indicator data of the data center corresponding to the second data center into JSON format (a data transmission format) and compress it, and write the processed full amount of indicator data of the data center to the indicator data cache queue corresponding to the second data center in the cloud, such as a Kafka queue.
[0069] When the intelligent converter of the second data center determines that the overall risk status of the second data center is a low-risk status, the intelligent converter of the second data center can filter out the monitoring indicator data of the monitoring component and the monitoring indicator data of the alarm component from the full indicator data of the data center as the indicator data of the monitoring system itself. The intelligent converter of the second data center converts the indicator data of the monitoring system itself into JSON format and compresses it, and writes the processed indicator data of the monitoring system itself to the corresponding indicator data cache queue of the second data center in the cloud, such as the Kafka queue.
[0070] The data processor of the first data center can read the index data cached in the second cache queue, and the data processor of the first data center converts the index data cached in the second cache queue and writes it to the time series database of the first data center, so as to realize the dynamic backup of the monitoring index of the second data center to the time series database of the first data center on demand. In this way, the monitoring system of the first data center can monitor and alarm the full amount of index data of the data center corresponding to the second data center stored in the time series database of the first data center or the index data of the monitoring system itself, so as to realize the monitoring and alarm of the second data center through the first data center.
[0071] According to the technical solution of this embodiment, the first data center reads the indicator data cached in the second cache queue as the full indicator data of the data center corresponding to the second data center or the indicator data of the monitoring system itself; the target indicator data corresponding to the second data center is written into the time series database of the first data center, so as to monitor and alarm the second data center through the first data center, thereby effectively achieving the technical effect of low-cost cross-monitoring and mutual backup monitoring between the first data center and the second data center in multiple data centers.
[0072] In an exemplary embodiment, according to the overall risk status, the target indicator data corresponding to the first data center is written to the first cache queue, including: obtaining risk level information of the overall risk status; when the risk level information represents a first risk, writing the full amount of data center indicator data corresponding to the first data center to the first cache queue; when the risk level information represents a second risk, writing the monitoring system's own indicator data corresponding to the first data center to the first cache queue.
[0073] The risk level information is used to characterize the first risk or the second risk; the first risk is greater than the second risk. In practical applications, the first risk may refer to a high risk, and the second risk may refer to a low risk.
[0074] In a specific implementation, when the intelligent converter of the first data center determines that the risk level information of the overall risk status of the first data center is the first risk, the intelligent converter of the first data center can convert the full amount of indicator data of the data center corresponding to the first data center into JSON format (a data transmission format) and compress it, and write the processed full amount of indicator data of the data center to the first cache queue, that is, the indicator data cache queue corresponding to the first data center in the cloud.
[0075] When the intelligent converter of the first data center determines that the risk level information of the overall risk status of the first data center is the second risk, the intelligent converter of the first data center can filter out the monitoring indicator data of the monitoring component and the monitoring indicator data of the alarm component from the full indicator data of the data center as the indicator data of the monitoring system itself. The intelligent converter of the first data center converts the indicator data of the monitoring system itself into JSON format and compresses it, and writes the processed indicator data of the monitoring system itself to the first cache queue, that is, the indicator data cache queue corresponding to the first data center in the cloud.
[0076] The technical solution of this embodiment obtains risk level information of the overall risk status; when the risk level information represents a first risk, writes the full amount of indicator data of the data center corresponding to the first data center to the first cache queue; when the risk level information represents a second risk, writes the indicator data of the monitoring system itself corresponding to the first data center to the first cache queue. This can realize the dynamic backup of the monitoring indicator data in the first data center to the time series database of the second data center on demand in combination with the overall system risk of the first data center, so that when the system risk is low, redundant monitoring indicator data transmission and storage can be reduced, thereby reducing network bandwidth occupancy and storage capacity occupancy; when the system risk increases, the full amount of monitoring indicator data can be backed up to the second data center in a timely manner, effectively providing technical support for timely risk warning and troubleshooting.
[0077] In an exemplary embodiment, the overall risk status of the first data center is determined based on the monitoring indicator data of each component, including: based on the monitoring indicator data of each component, using a preset sliding window to determine the disk usage rate, business-critical event growth rate, and local alarm queue depth of the first data center; based on the disk usage rate, business-critical event growth rate, and local alarm queue depth, determining whether the overall risk status of the first data center is the first risk or the second risk.
[0078] Among them, the business critical event may refer to a single business monitoring indicator with a custom identifier set according to an actual business scenario or a special business monitoring indicator composed of a combination of multiple business indicators.
[0079] The alarm queue depth may refer to a monitoring indicator for measuring the processing status of an alarm component or the backlog status of alarm information.
[0080] In a specific implementation, when the first data center determines the overall risk status of the first data center based on the monitoring indicator data of each component, the intelligent converter of the first data center can read the monitoring indicator data in the monitoring component, and dynamically calculate the disk usage, number of key business events, and local alarm queue depth of the center at the past n custom moments in a sliding window manner; then, the intelligent converter of the first data center temporarily stores the disk usage, number of key business events, and local alarm queue depth in the local memory, and then calculates the average disk usage, average key business event growth rate, and average alarm queue depth for the past m hours respectively; if the m-hour average growth rate of i items of data is greater than the threshold k, the intelligent converter of the first data center determines that the overall risk status of the first data center is the first risk, i.e., high risk, and triggers the writing of the full amount of data center indicator data corresponding to the first data center to the first cache queue; otherwise, the intelligent converter of the first data center determines that the overall risk status of the first data center is the second risk, and triggers the writing of the monitoring system's own indicator data corresponding to the first data center to the first cache queue.
[0081] The technical solution of this embodiment uses a preset sliding window to determine the disk usage rate, business-critical event growth rate and local alarm queue depth of the first data center based on the monitoring indicator data of each component, and determines whether the overall risk status of the first data center is the first risk or the second risk based on the disk usage rate, business-critical event growth rate and local alarm queue depth. This can effectively and quickly assess the overall system risk of the data center based on the monitoring indicator data of each component of the data center, and facilitate the subsequent dynamic backup of the monitoring indicator data of the first data center to the time series database of the second data center on demand in combination with the overall system risk of the first data center.
[0082] In an exemplary embodiment, the method also includes: collecting indicator data of business services of the first data center, indicator data of the alarm component, indicator data of the time series database, indicator data of the intelligent converter, indicator data of the data processor, and indicator data of the monitoring component itself through the monitoring component, and storing them as monitoring indicator data of each component in the first preset directory of the local disk of the monitoring system; monitoring the indicator data in the first preset directory through the alarm component, and whether the indicator data in the target preset directory triggers the alarm rules.
[0083] The target preset directory is a preset directory (eg, / api / data / center / b) that stores target indicator data corresponding to the second data center in the time series database of the first data center.
[0084] Among them, the monitoring system includes a monitoring component and an alarm component.
[0085] In a specific implementation, the first data center can collect the indicator data of the business services of the first data center, the indicator data of the alarm component, the indicator data of the time series database, the indicator data of the intelligent converter, the indicator data of the data processor, and the indicator data of the monitoring component itself through the monitoring component, and store them as the monitoring indicator data of each component in the first preset directory (e.g., / mnt / dataA directory) of the local disk of the monitoring system.
[0086] Then, the first data center can monitor the indicator data in the first preset directory and whether the indicator data in the target preset directory triggers the alarm rules through the alarm component. Specifically, the first data center can read the indicator data in the / mnt / dataA directory and the indicator data in the / api / data / center / b directory of the time series database of the first data center through the alarm component; then, calculate the value of the expression M1 ~ Mn according to the preset rules, where Mn is the rule calculation expression of the nth article, and compare the value of the rule calculation expression M1 ~ Mn with the corresponding alarm threshold Threshold_1 ~Threshold_n to determine whether the alarm rule is triggered, that is, to achieve monitoring and alarming of the first data center and the second data center through the first data center.
[0087] The technical solution of this embodiment can effectively realize low-cost cross-monitoring and mutual backup monitoring between multiple data centers. It is worth mentioning that the monitoring data source of the first data center is two independent modules, that is, the monitoring data of the first data center is stored in the disk directory of the monitoring component of the first data center, and the backup monitoring data of the second data center is stored in the time series database of the first data center, which can effectively improve data isolation and ensure the isolated storage of the monitoring data of the first data center and the monitoring data of the second data center, which helps to reduce the fault domain in abnormal scenarios.
[0088] To facilitate understanding by those skilled in the art, the following is an exemplary embodiment of a multi-data center mutual backup monitoring method of the present application:
[0089] Example 1
[0090] As shown in Figures 3 and 4, center A (i.e., the first data center) includes an intelligent converter A (AdapterA), a data processor A (DataWorkerA), a monitoring component A, an alarm component A, and a time series library A; AdapterA is connected to the Kafka queue A (i.e., the first cache queue) in the cloud Kafka, and DataWorkerA is connected to the Kafka queue B (i.e., the second cache queue) in the cloud Kafka; center B (i.e., the second data center) includes an intelligent converter B (AdapterB), a data processor B (DataWorkerB), a monitoring component B, an alarm component B, and a time series library B; AdapterB is connected to the Kafka queue B (i.e., the second cache queue) in the cloud Kafka, and DataWorkerB is connected to the Kafka queue A (i.e., the first cache queue) in the cloud Kafka.
[0091] Monitoring component A is responsible for collecting indicator data of the business services of center A (such as service1~serviceN), alarm component A, time series library A, intelligent converter A, data processor A and the metric interface of monitoring component A itself, and stores it in the / mnt / dataA directory of the local disk. That is, the full amount of indicator data of center A is obtained and recorded as FullDataA.
[0092] Smart converter A reads the indicator data in the / mnt / dataA directory of monitoring component A, and dynamically calculates the disk usage, number of key business events, and local alarm queue depth of the center in the past 10 minutes, 15 minutes, 30 minutes, and 60 minutes in a sliding window manner, and temporarily stores the calculation results in the local memory. Then, the average disk usage, average key business event growth rate, and average alarm queue depth of the past hour are calculated respectively. If the 1-hour average growth rate of two of the data is greater than 1, the smart converter A is triggered to convert the full amount of monitoring indicator data of the past hour into JSON format, and compress it and write it to the cloud Kafka queue A; otherwise, the smart converter A only filters out the indicator data of monitoring component A and alarm component A in the past hour, converts them into JSON format, and compresses them and writes them to the cloud Kafka queue A. Data processor A reads the data of cloud Kafka queue B and converts it into the timestamp:key:value format of time series data, and then stores it in / api / data / center / b in the time series library A of center A.
[0093] Alarm component A reads the indicator data in the / mnt / dataA directory and the indicator data / api / data / center / b in the time series library A, calculates the values of expressions M1 ~ Mn according to the preset rules, where Mn is the rule calculation expression of the nth rule, and compares the values of the rule calculation expressions M1 ~ Mn with the corresponding alarm thresholds Threshold_1 ~ Threshold_n to determine whether the alarm rule is triggered, that is, to achieve monitoring and alarming of center A and center B through center A.
[0094] Correspondingly, monitoring component B is responsible for collecting indicator data of business services of center B (such as service1~serviceN), alarm component B, time series library B, AdapterB, DataWorkerB and the metric interface of monitoring component B itself, and stores it in the / mnt / dataB directory of the local disk.
[0095] Smart converter B reads the indicator data in the / mnt / dataB directory of monitoring component B, and dynamically calculates the disk usage, number of key business events, and local alarm queue depth of the center in the past 10 minutes, 15 minutes, 30 minutes, and 60 minutes in a sliding window manner, temporarily stores the calculation results in the local memory, and then calculates the average disk usage, average key business event growth rate, and average alarm queue depth in the past hour. If the 1-hour average growth rate of two of the data is greater than 1, the smart converter B is triggered to convert the full amount of indicator data in the past hour into JSON format, and compress it and write it to the cloud Kafka queue B; otherwise, the smart converter B only filters out the indicator data of monitoring component B and alarm component B in the past hour, converts it to JSON format, and compresses it and writes it to the cloud Kafka queue B. Data processor B reads the data of cloud Kafka queue A, converts it into the timestamp:key:value format of time series data, and then stores it in / api / data / center / a in the time series library B of center B.
[0096] Alarm component B reads the data in the / mnt / dataB directory and the data / api / data / center / a in the time series library B, calculates the values of expressions M1 ~ Mn according to the preset rules, where Mn is the rule calculation expression of the nth rule, and compares the values of the rule calculation expressions M1 ~ Mn with the corresponding alarm thresholds Threshold_1 ~ Threshold_n to determine whether the alarm rule is triggered, that is, to monitor and alarm center A and center B through center B.
[0097] The technical solution of this embodiment can effectively realize low-cost cross-monitoring and mutual backup monitoring between multiple data centers. It is worth mentioning that the monitoring data source of the first data center is two independent modules, that is, the monitoring data of the first data center is stored in the disk directory of the monitoring component of the first data center, and the backup monitoring data of the second data center is stored in the time series database of the first data center, which can effectively improve data isolation and ensure the isolated storage of the monitoring data of the first data center and the monitoring data of the second data center, which helps to reduce the fault domain in abnormal scenarios.
[0098] In an exemplary embodiment, the method also includes: collecting indicator data of business services of the first data center, indicator data of the alarm component, indicator data of the time series database, indicator data of the intelligent converter, indicator data of the data processor, and indicator data of the monitoring component itself through the monitoring component, and storing them as monitoring indicator data of each component in a second preset directory of the time series database of the first data center; monitoring the indicator data in the second preset directory through the alarm component, and whether the indicator data in the target preset directory triggers the alarm rules.
[0099] The target preset directory is a preset directory (eg, / api / data / center / b directory) that stores target indicator data corresponding to the second data center in the time series database of the first data center.
[0100] Among them, the monitoring system includes a monitoring component and an alarm component.
[0101] In a specific implementation, the first data center can collect the indicator data of the business services of the first data center, the indicator data of the alarm component, the indicator data of the time series database, the indicator data of the intelligent converter, the indicator data of the data processor, and the indicator data of the monitoring component itself through the monitoring component, and store the monitoring indicator data of each component in the second preset directory (e.g., / api / data / center / a directory) of the time series database of the first data center.
[0102] Then, the first data center can monitor the indicator data in the second preset directory through the alarm component, and whether the indicator data in the target preset directory triggers the alarm rule. Specifically, the first data center can read the indicator data in the time series database / api / data / center / a directory of the first data center and the indicator data in the time series database / api / data / center / b directory of the first data center through the alarm component; then, calculate the value of the expression M1 ~ Mn according to the preset rules, where Mn is the rule calculation expression of the nth article, and compare the value of the rule calculation expression M1 ~ Mn with the corresponding alarm threshold Threshold_1 ~ Threshold_n to determine whether the alarm rule is triggered, that is, to achieve monitoring and alarming of the first data center and the second data center through the first data center.
[0103] The technical solution of this embodiment can effectively realize low-cost cross-monitoring and mutual backup monitoring between multiple data centers. It is worth mentioning that the monitoring data source of the first data center is unified as the time series database of the first data center, that is, the monitoring data of the first data center and the backup monitoring of the second data center are all stored in the time series database of the first data center. By specifying a unified storage method, it helps to reduce the complexity of the storage components and alarm components of the data center, and can further reduce the system hardware cost.
[0104] To facilitate understanding by those skilled in the art, the following is another exemplary embodiment of a multi-data center mutual backup monitoring method of the present application:
[0105] Example 2
[0106] As shown in Figures 5 and 6, center A (i.e., the first data center) includes an intelligent converter A (AdapterA), a data processor A (DataWorkerA), a monitoring component A, an alarm component A, and a time series library A; AdapterA is connected to the Kafka queue A (i.e., the first cache queue) in the cloud Kafka, and DataWorkerA is connected to the Kafka queue B (i.e., the second cache queue) in the cloud Kafka; center B (i.e., the second data center) includes an intelligent converter B (AdapterB), a data processor B (DataWorkerB), a monitoring component B, an alarm component B, and a time series library B; AdapterB is connected to the Kafka queue B (i.e., the second cache queue) in the cloud Kafka, and DataWorkerB is connected to the Kafka queue A (i.e., the first cache queue) in the cloud Kafka.
[0107] Among them, monitoring component A is responsible for collecting indicator data of business services (such as service1~serviceN), alarm component A, time series library A, intelligent converter A, data processor A and the metric interface of monitoring component A itself, and stores it in / api / data / center / a of time series library A.
[0108] Smart converter A reads the indicator data / api / data / center / a related to center A in time series library A, and dynamically calculates the disk usage, number of key business events, and local alarm queue depth of the center in the past 10 minutes, 15 minutes, 30 minutes, and 60 minutes in a sliding window manner, and temporarily stores the calculation results in the local memory. Then, the average disk usage, average key business event growth rate, and average alarm queue depth of the past hour are calculated respectively. If the 1-hour average growth rate of two of the data is greater than 1, the smart converter A is triggered to convert the full indicator data of the past hour into JSON format, and compress it and write it to the cloud Kafka queue A; otherwise, the smart converter A only filters out the indicator data of monitoring component A and alarm component A in the past hour, converts it into JSON format, and compresses it and writes it to the cloud Kafka queue A. Data processor A reads the data of cloud Kafka queue B, converts it into the timestamp:key:value format of time series data, and then stores it in / api / data / center / b in time series library A of center a.
[0109] Alarm component A reads data / api / data / center / a and data / api / data / center / b of time series library A, calculates the values of expressions M1 ~ Mn according to preset rules, where Mn is the rule calculation expression of the nth rule, and compares the values of rule calculation expressions M1 ~ Mn with the corresponding alarm thresholds Threshold_1 ~ Threshold_n to determine whether the alarm rule is triggered, that is, to monitor and alarm center A and center B through center A.
[0110] In addition, monitoring component B is responsible for collecting indicator data of the business services of center B (such as service1~serviceN), alarm component B, time series library B, intelligent converter B, data processor B and the metric interface of monitoring component B itself, and stores it in / api / data / center / b of time series library B.
[0111] Smart converter B reads the indicator data / api / data / center / b related to center B in time series library B, and dynamically calculates the disk usage, number of key business events, and local alarm queue depth of center B in the past 10 minutes, 15 minutes, 30 minutes, and 60 minutes in a sliding window manner, temporarily stores the calculation results in the local memory, and then calculates the average disk usage, average key business event growth rate, and average alarm queue depth in the past hour. If the 1-hour average growth rate of two of the data is greater than 1, the data processor B is triggered to convert the full indicator data of the past hour into JSON format, and compress it and write it to the cloud Kafka queue B; otherwise, smart converter B only filters out the indicator data of monitoring component B and alarm component B in the past hour, converts it to JSON format, and compresses it and writes it to the cloud Kafka queue B. Data processor B reads the data of cloud Kafka queue A, converts it into the timestamp:key:value format of time series data, and then stores it in / api / data / center / a in time series library B of center B.
[0112] Alarm component B reads data / api / data / center / b and data / api / data / center / a of time series library B, calculates the values of expressions M1 ~ Mn according to preset rules, where Mn is the rule calculation expression of the nth rule, and compares the values of rule calculation expressions M1 ~ Mn with the corresponding alarm thresholds Threshold_1 ~ Threshold_n to determine whether the alarm rule is triggered, that is, to monitor and alarm center A and center B through center B.
[0113] The technical solution of this embodiment can effectively realize low-cost cross-monitoring and mutual backup monitoring between multiple data centers. It is worth mentioning that the monitoring data source of the first data center is unified as the time series database of the first data center, that is, the monitoring data of the first data center and the backup monitoring of the second data center are all stored in the time series database of the first data center. By specifying a unified storage method, it helps to reduce the complexity of the storage components and alarm components of the data center, and can further reduce the system hardware cost.
[0114] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0115] Based on the same inventive concept, the embodiment of the present application also provides a multi-data center mutual backup monitoring device for implementing the multi-data center mutual backup monitoring method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more multi-data center mutual backup monitoring devices provided below can refer to the limitations of the multi-data center mutual backup monitoring method above, and will not be repeated here.
[0116] In an exemplary embodiment, as shown in FIG. 7 , a multi-data center mutual backup monitoring device is provided, which is applied to a first data center, where the first data center is one of the multiple data centers, and the data center is configured with a monitoring system, including:
[0117] A reading module 710, configured to read monitoring indicator data of each component in the first data center;
[0118] A risk status monitoring module 720, configured to determine the overall risk status of the first data center based on the monitoring indicator data of each component;
[0119] A writing module 730 is used to write the target indicator data corresponding to the first data center into the first cache queue according to the overall risk status; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself;
[0120] Among them, the first cache queue is the indicator data cache queue corresponding to the first data center in the cloud; the first cache queue is communicatively connected to the second data center, and the second data center is another data center of the multiple data centers, and the second data center is used to write the indicator data cached in the first cache queue into the time series database of the second data center.
[0121] In one embodiment, the first data center is communicatively connected with a second cache queue, and the second cache queue is an indicator data cache queue corresponding to the second data center in the cloud. The device is also used to read the indicator data cached in the second cache queue as the target indicator data corresponding to the second data center; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself; the target indicator data corresponding to the second data center is written into the time series database of the first data center, so as to monitor and alarm the second data center through the first data center.
[0122] In one embodiment, the monitoring system includes a monitoring component and an alarm component, and the device is also used to collect indicator data of business services of the first data center, indicator data of the alarm component, indicator data of the time series database, indicator data of the intelligent converter, indicator data of the data processor, and indicator data of the monitoring component itself through the monitoring component, and store them as monitoring indicator data of each component in a first preset directory on the local disk of the monitoring system; monitor the indicator data in the first preset directory and whether the indicator data in the target preset directory triggers alarm rules through the alarm component; the target preset directory is a preset directory for storing target indicator data corresponding to the second data center in the time series database of the first data center.
[0123] In one embodiment, the monitoring system includes a monitoring component and an alarm component, and the device is also used to collect indicator data of business services of the first data center, indicator data of the alarm component, indicator data of the time series database, indicator data of the intelligent converter, indicator data of the data processor, and indicator data of the monitoring component itself through the monitoring component, and store them as monitoring indicator data of each component in a second preset directory of the time series database of the first data center; monitor the indicator data in the second preset directory and whether the indicator data in the target preset directory triggers alarm rules through the alarm component; the target preset directory is a preset directory for storing target indicator data corresponding to the second data center in the time series database of the first data center.
[0124] In one embodiment, the writing module 730 is used to obtain risk level information of the overall risk status; the risk level information is used to characterize a first risk or a second risk; the first risk is greater than the second risk; when the risk level information characterizes the first risk, the full amount of indicator data of the data center corresponding to the first data center is written to the first cache queue; when the risk level information characterizes the second risk, the monitoring system's own indicator data corresponding to the first data center is written to the first cache queue.
[0125] In one of the embodiments, the risk status monitoring module 720 is used to determine the disk usage rate, business-critical event growth rate and local alarm queue depth of the first data center based on the monitoring indicator data of each component using a preset sliding window; and determine whether the overall risk status of the first data center is the first risk or the second risk based on the disk usage rate, business-critical event growth rate and local alarm queue depth.
[0126] Each module in the above-mentioned multi-data center mutual backup monitoring device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.
[0127] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG8. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store monitoring indicator data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-data center mutual backup monitoring method is implemented.
[0128] Those skilled in the art will understand that the structure shown in FIG. 8 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.
[0129] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned method for mutual backup monitoring of multiple data centers. The steps of the method for mutual backup monitoring of multiple data centers here may be the steps of the method for mutual backup monitoring of multiple data centers in each of the above-mentioned embodiments.
[0130] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor executes the steps of the multi-data center mutual backup monitoring method described above. The steps of the multi-data center mutual backup monitoring method here can be the steps of the multi-data center mutual backup monitoring method described in each of the above embodiments.
[0131] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, causes the processor to execute the steps of the multi-data center mutual backup monitoring method described above. The steps of the multi-data center mutual backup monitoring method here may be the steps of the multi-data center mutual backup monitoring method described above in each of the embodiments.
[0132] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0133] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.
[0134] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0135] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.
Claims
1. A multi-data center mutual backup monitoring method, characterized in that: Applied to a first data center, the first data center is one of the multiple data centers, and the data center is equipped with a monitoring system. The method includes: Reading monitoring indicator data of each component in the first data center; Determining an overall risk status of the first data center according to the monitoring indicator data of each component; According to the overall risk status, writing the target indicator data corresponding to the first data center into the first cache queue; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself; Among them, the first cache queue is the indicator data cache queue corresponding to the first data center in the cloud; the first cache queue is communicatively connected to the second data center, and the second data center is another data center of the multiple data centers. The second data center is used to write the indicator data cached in the first cache queue to the time series database of the second data center, so as to monitor and alarm the first data center through the second data center.
2. The method according to claim 1, characterized in that: The first data center is in communication connection with a second cache queue, the second cache queue is an indicator data cache queue corresponding to the second data center in the cloud, and the method further includes: Read the index data cached in the second cache queue as the target index data corresponding to the second data center; the target index data includes the full amount of index data of the data center or the index data of the monitoring system itself; The target indicator data corresponding to the second data center is written into the time series database of the first data center so as to monitor and alarm the second data center through the first data center.
3. The method according to claim 2, characterized in that The monitoring system includes a monitoring component and an alarm component, and the method further includes: The monitoring component collects the indicator data of the business services of the first data center, the indicator data of the alarm component, the indicator data of the time series database, the indicator data of the intelligent converter, the indicator data of the data processor, and the indicator data of the monitoring component itself, and stores them as the monitoring indicator data of each component in the first preset directory of the local disk of the monitoring system; The alarm component monitors the indicator data in the first preset directory and whether the indicator data in the target preset directory triggers the alarm rules; the target preset directory is a preset directory that stores the target indicator data corresponding to the second data center in the time series database of the first data center.
4. The method according to claim 2, characterized in that: The monitoring system includes a monitoring component and an alarm component, and the method further includes: The monitoring component collects the indicator data of the business services of the first data center, the indicator data of the alarm component, the indicator data of the time series database, the indicator data of the intelligent converter, the indicator data of the data processor, and the indicator data of the monitoring component itself, and stores them as the monitoring indicator data of each component in a second preset directory of the time series database of the first data center; The alarm component monitors the indicator data in the second preset directory and whether the indicator data in the target preset directory triggers the alarm rules; the target preset directory is a preset directory that stores the target indicator data corresponding to the second data center in the time series database of the first data center.
5. The method according to claim 1, characterized in that The step of writing the target indicator data corresponding to the first data center to the first cache queue according to the overall risk status includes: Acquire risk level information of the overall risk status; the risk level information is used to characterize the first risk or the second risk; the first risk is greater than the second risk; When the risk level information represents the first risk, writing the full amount of data center indicator data corresponding to the first data center into the first cache queue; In a case where the risk level information represents the second risk, the monitoring system's own indicator data corresponding to the first data center is written into the first cache queue.
6. The method according to claim 5, characterized in that Determining the overall risk status of the first data center according to the monitoring indicator data of each component includes: According to the monitoring indicator data of each component, a preset sliding window is used to determine the disk usage rate, business-critical event growth rate and local alarm queue depth of the first data center; According to the disk usage, the growth rate of business-critical events, and the local alarm queue depth, the overall risk status of the first data center is determined to be the first risk or the second risk.
7. A multi-data center mutual backup monitoring device, characterized in that: Applied to a first data center, the first data center is one of the multiple data centers, the data center is equipped with a monitoring system, and the device includes: A reading module, used to read monitoring indicator data of each component in the first data center; A risk status monitoring module, used to determine the overall risk status of the first data center based on the monitoring indicator data of each component; A writing module, used for writing the target indicator data corresponding to the first data center into the first cache queue according to the overall risk status; the target indicator data includes the full indicator data of the data center or the indicator data of the monitoring system itself; Among them, the first cache queue is the indicator data cache queue corresponding to the first data center in the cloud; the first cache queue is communicatively connected to the second data center, and the second data center is another data center of the multiple data centers, and the second data center is used to write the indicator data cached in the first cache queue into the time series database of the second data center.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.