CN114647553A

CN114647553A - Data monitoring method, device, equipment and storage medium based on containerization service

Info

Publication number: CN114647553A
Application number: CN202210403904.3A
Authority: CN
Inventors: 武文轩; 程鹏; 王豪赞; 周文泽
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2022-04-18
Filing date: 2022-04-18
Publication date: 2022-06-21

Abstract

The application provides a data monitoring method, a device, equipment and a storage medium based on containerization service. The method can be used in the financial field or other fields, and comprises the following steps: acquiring data of a middle hierarchy of a container according to a current data monitoring strategy of the middle hierarchy of the container to obtain current data of the middle hierarchy of the container; if the current data of the hierarchy in the container is determined to be abnormal data according to a preset abnormal data determination condition, determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience library; and according to the target data monitoring strategy, carrying out data acquisition on the hierarchy corresponding to the abnormal data to obtain the target data of the hierarchy corresponding to the abnormal data. The method realizes dynamic monitoring of the data of each level in the container, and improves the flexibility of data monitoring.

Description

Data monitoring method, device, equipment and storage medium based on containerization service

Technical Field

The present application relates to big data technologies, and in particular, to a method, an apparatus, a device, and a storage medium for data monitoring based on containerization service.

Background

With the continuous advance of containerization technology, data of each level, such as a business level, a service level, a device level and the like, is more complex. Anomalies at different levels can cause service errors. In order to improve the data management efficiency and ensure the normal operation of the service, the monitoring of the data of each level is necessary.

In the prior art, monitoring schemes for each hierarchy are different. For example, service layer monitoring is usually built by developers, and is performed based on calling, delay, running state and the like of service services; the service layer is usually established by a system service provider, and monitors the running state of the system service, the running state of the support service, the resource scheduling and the use condition.

However, in the prior art, data monitoring of each layer depends on a preset monitoring rule to determine whether an exception exists. If the abnormal data exists, the abnormal data is directly reported to inform the staff that the current abnormal data exists for the staff to check, and the specific level of the abnormal data is determined manually. The data monitoring mode is fixed, manpower and time are wasted during monitoring, the flexibility of data monitoring is poor, and the efficiency and the precision of the data monitoring are affected.

Disclosure of Invention

The application provides a data monitoring method, a device, equipment and a storage medium based on containerization service, so as to improve the flexibility and efficiency of data monitoring.

In one aspect, the present application provides a data monitoring method based on containerization service, including:

acquiring data of a middle hierarchy of a container according to a current data monitoring strategy of the middle hierarchy of the container to obtain current data of the middle hierarchy of the container;

if the current data of the hierarchy in the container is determined to be abnormal data according to a preset abnormal data determination condition, determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience library;

and according to the target data monitoring strategy, carrying out data acquisition on the hierarchy corresponding to the abnormal data to obtain the target data of the hierarchy corresponding to the abnormal data.

In another aspect, the present application provides a data monitoring apparatus based on containerization service, including:

the current data acquisition module is used for acquiring data of the middle hierarchy in the container according to a current data monitoring strategy of the middle hierarchy in the container to obtain current data of the middle hierarchy in the container;

the target data monitoring strategy determining module is used for determining a target data monitoring strategy related to the abnormal data according to a preset expert experience base if the current data of the hierarchy in the container is determined to be the abnormal data according to a preset abnormal data determining condition;

and the target data acquisition module is used for acquiring data of the hierarchy corresponding to the abnormal data according to the target data monitoring strategy to obtain the target data of the hierarchy corresponding to the abnormal data.

In another aspect, the present application provides an electronic device comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes the computer-executable instructions stored in the memory to implement the containerization service-based data monitoring method according to any embodiment of the present application.

In another aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor, are configured to implement a containerization service-based data monitoring method according to any embodiment of the present application.

In another aspect, the present application provides a computer program product comprising a computer program, which when executed by a processor, implements a containerization service-based data monitoring method according to any of the embodiments of the present application.

According to the technical scheme, a current data monitoring strategy is set for each level, data of each level are collected, and current data of each level are obtained. The data of each level can be acquired respectively, and the data of each level can be monitored and analyzed uniformly. Presetting an abnormal data determining condition, and judging whether the current data is abnormal data. And if so, determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience base. And replacing the current data monitoring strategy with the target data monitoring strategy, and acquiring the data of the hierarchy with abnormal data according to the target data monitoring strategy to obtain the target data. When abnormal data occurs, the monitoring strategy can be adjusted for each abnormal level, monitoring is carried out under a new monitoring strategy, the abnormal condition can be acquired more accurately, and automatic updating of the level data monitoring strategy is realized. The problem of among the prior art, can't carry out the automatic adjustment of monitoring strategy according to the abnormal condition is solved, avoid fixed control rule to cause the wrong condition of judgement of abnormal data to appear. And the monitoring strategies of all levels are independent from each other and are updated respectively, so that the flexibility of data monitoring is improved, and the efficiency and the precision of the data monitoring are further improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

Fig. 1 is a schematic flowchart of a data monitoring method based on containerization service according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a data monitoring method based on containerization service according to an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of a data monitoring method based on containerization service according to an embodiment of the present disclosure;

FIG. 4 is a schematic structural diagram of a multi-dimensional real-time container monitoring and analyzing tool according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of a data monitoring apparatus based on containerization service according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Specific embodiments of the present application have been shown by way of example in the drawings and will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

It should be understood that the embodiments described are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims.

In the description of the present application, it is to be understood that the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not necessarily used to describe a particular order or sequence, nor are they to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate. Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated object, indicating that there may be three relationships, for example, a and/or B, which may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

It should be noted that, for the sake of brevity, this description does not exhaust all alternative embodiments, and it should be understood by those skilled in the art after reading this description that any combination of features may constitute an alternative embodiment as long as the features are not mutually inconsistent. The following examples are described in detail.

Fig. 1 is a schematic flow chart of a data monitoring method based on containerization service according to an embodiment of the present disclosure, and as shown in fig. 1, the method provided in this embodiment is executed by a data monitoring apparatus based on containerization service, which may be configured on a container multi-dimensional real-time monitoring and analyzing tool. As shown in fig. 1, the method comprises the steps of:

s101, according to the current data monitoring strategy of the hierarchy in the container, data acquisition is carried out on the hierarchy in the container, and current data of the hierarchy in the container are obtained.

The containerization service can monitor multidimensional data in different levels, and the levels of the container can include a business layer, a service layer, a device layer and the like. Data in the business layer can comprise dimension data such as business logs, system logs, business service calls and the like; data in the service layer can include data of dimensions such as the running state of a business container, a system middleware log and a service framework; data in the device layer may include dimension data such as CPU usage of each component, CPU allocation, memory occupation of each process, read-write resource usage of a disk, and network traffic.

Each level in the container may be configured with a data monitoring policy, where the data monitoring policy refers to a policy for acquiring data of each level for monitoring. The data monitoring policy may collect data for each dimension in the corresponding hierarchy. For example, the current data monitoring policy of the device layer is to acquire current data every 30 seconds, and then data such as memory occupation of each process, read-write resource usage of a disk, and network traffic can be acquired every 30 seconds.

The current data monitoring policy of each hierarchy is a data monitoring policy of each hierarchy at the current time, and the current data monitoring policy of each hierarchy may be different. For example, the current data monitoring policy of the service layer is to acquire current data every 1 minute; the current data monitoring policy of the device layer is to acquire current data every 30 seconds.

And determining a current data monitoring strategy of each hierarchy, and acquiring data of each hierarchy in the container in real time or at regular time according to the current data monitoring strategy of each hierarchy, wherein the acquired data is the current data of the corresponding hierarchy. For example, according to the current data monitoring policy of the device layer, it may be obtained that the current data of the device layer is that the resource utilization rate of the CPU reaches 50%.

In this embodiment, the data monitoring apparatus based on containerization service may be configured on a dynamic container multidimensional real-time monitoring and analyzing tool, where the dynamic means that a data monitoring policy used by the tool is dynamically variable. The dynamic multi-dimensional real-time container monitoring and analyzing tool may be provided with a plurality of data collectors, and the data collectors may be configured to collect current data of each layer, for example, collect log files of an application layer, collect a container running state of a system layer and CPU time consumption of each thread, collect disk storage I/O of an equipment layer and network bandwidth usage, and the like.

S102, if the condition is determined according to the preset abnormal data, the current data of the hierarchy in the container is determined to be the abnormal data, and the target data monitoring strategy related to the abnormal data is determined according to a preset expert experience base.

The abnormal data determining conditions are preset and used for determining whether the acquired current data are abnormal data or not, namely determining whether each hierarchy is abnormal or not. And judging whether the current data meets the abnormal data determining condition or not according to the abnormal data determining condition, and if so, determining that the current data is abnormal data. For example, a numerical range of the abnormal data may be set in the abnormal data determination condition, and the current data may be determined to be the abnormal data when the abnormal data is within the numerical range. And if the current data is within the preset numerical range, determining that the current data meets the abnormal data determination condition, namely that the current data is abnormal data.

The abnormal data determination conditions of all levels can be different, and the data of different levels at the same time can be abnormal and normal, or some levels have abnormal current data and some levels have normal current data. Different abnormal data determination rules can be set for current data of different dimensions of the same hierarchy. For example, for the running state of the service container in the service layer, an abnormal data determination rule one may be set; for the system middleware log in the service layer, an abnormal data determination rule II can be set; for the network traffic in the device layer, an abnormal data determination rule three may be set.

After the current data of each hierarchy is obtained, determining abnormal data determination conditions associated with the current data of each dimension of each hierarchy. And determining whether the current data of each dimensionality of each hierarchy is abnormal data or not according to abnormal data determination rules corresponding to various current data.

If the current data is not abnormal data, the current data can be recorded and stored, and the current data acquisition and abnormal data judgment are carried out on each hierarchy according to the current data monitoring strategy of each hierarchy.

And if the current data is abnormal data, determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience library. The expert experience base can be a strategy database preset by a user according to expert experience, and various data monitoring strategies are stored in the expert experience base. After the current data are determined to be abnormal data, a data monitoring strategy different from the current data monitoring strategy can be searched from the expert experience base to serve as a target data monitoring strategy. In this embodiment, the target data monitoring policy of the hierarchy where the abnormal data is located may be searched according to the abnormal data. For example, if the current data of the service layer is abnormal and the current data of the service layer and the device layer is normal, the target data monitoring policy related to the service layer may be searched.

The expert experience base can store the association relationship between each hierarchy and the data monitoring strategy, after the current data are determined to be abnormal, the hierarchy where the abnormal data are located is determined, and the target data monitoring strategy is determined according to the association relationship between the preset hierarchy and the data monitoring strategy. The expert experience base can also store the incidence relation between various abnormal data and data monitoring strategies, and the abnormal data with different values are associated with different data monitoring strategies. For example, if the abnormal data is that the usage amount of the CPU resource is between 50% and 59%, the associated target data monitoring policy is policy one; and if the abnormal data is that the CPU resource usage is between 60% and 69%, the related target data monitoring strategy is strategy two. That is, a target data monitoring policy associated with the anomaly data may be found from the expert experience base.

S103, according to the target data monitoring strategy, data acquisition is carried out on the hierarchy corresponding to the abnormal data, and target data of the hierarchy corresponding to the abnormal data are obtained.

After the target data monitoring strategy of the hierarchy where the abnormal data is located is determined, the target data monitoring strategy is used for replacing the current data monitoring strategy of the corresponding hierarchy. If the current data of the hierarchy is normal, the current data monitoring strategy of the hierarchy does not need to be replaced.

And adopting a target data monitoring strategy to re-collect new current data of the level where the abnormal data is located, wherein the collected new current data is the target data of the level. For example, the current data monitoring policy is to be collected every 30 seconds, and the target data monitoring policy may be to be collected every 1 minute. The monitoring strategies of all levels can be mutually independent and respectively updated, and the flexibility and the efficiency of data monitoring are improved. The collection of the target data in S103 may be performed by a data collector in the dynamic multi-dimensional real-time container monitoring and analyzing tool.

The data monitoring method based on the containerization service can be used in the financial field and can also be used in any field except the financial field.

According to the embodiment of the application, a current data monitoring strategy is set for each level, and data of each level are collected to obtain current data of each level. The data of each level can be acquired respectively, and the data of each level can be monitored and analyzed uniformly. And presetting an abnormal data determining condition, and judging whether the current data is abnormal data. And if so, determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience library. And replacing the current data monitoring strategy with the target data monitoring strategy, and acquiring the data of the hierarchy according to the target data monitoring strategy to obtain the target data. When the abnormal data monitoring strategy is abnormal, the monitoring strategy can be adjusted for each abnormal level, monitoring is carried out under a new monitoring strategy, the abnormal condition can be acquired more accurately, and automatic updating of the data monitoring strategy of each level is realized. The problem of among the prior art, can't carry out the automatic adjustment of monitoring strategy according to the abnormal condition is solved, the condition that fixed monitoring rule caused the abnormal judgment mistake appears is avoided. And the monitoring strategies of all levels are independent from each other and are updated respectively, so that the flexibility of data monitoring is improved, and the efficiency and the precision of the data monitoring are further improved.

Fig. 2 is a schematic flowchart of a data monitoring method based on containerization service according to an embodiment of the present application, which is an alternative embodiment based on the foregoing embodiment.

In this embodiment, according to a preset abnormal data determination condition, it is determined that current data of a hierarchy in a container is abnormal data, and the condition may be detailed as: determining conditions according to preset abnormal data of the levels in the container, and determining data to be confirmed of the levels from the current data; and if the data to be confirmed meet the abnormal data determination conditions of the corresponding levels, determining that the data to be confirmed are abnormal data.

As shown in fig. 2, the method comprises the steps of:

s201, acquiring data of the middle hierarchy of the container according to the current data monitoring strategy of the middle hierarchy of the container to obtain current data of the middle hierarchy of the container.

S202, determining conditions according to preset abnormal data of the levels in the container, and determining data to be confirmed of the levels from the current data.

The method includes the steps that corresponding abnormal data determining conditions can be preset in each level of a container, all current data of each level can be judged according to the abnormal data determining conditions, and abnormal data can also be judged on one part of the current data of each level. For example, the current data in the service layer includes the running state of the current service container, a system middleware log and a service framework, and the abnormal data determination condition of the service layer is to determine the running state of the service container by using the abnormal data, that is, the abnormal data determination condition of the service layer is to determine a part of the current data of the layer where the abnormal data is located.

The data to be confirmed is current data that needs to be determined as abnormal data indicated in the abnormal data determination condition, for example, the current data is an operating state of a current service container, a system middleware log, and a service framework, and if the abnormal data determination condition is that the operating state of the current service container is determined, the data to be confirmed is the operating state of the current service container. According to the abnormal data determination condition of each level in the container, the data to be confirmed in the current data of each level can be determined. The method is beneficial to reducing the judgment range of the abnormal data and improving the determination precision and efficiency of the abnormal data.

And S203, if the data to be confirmed meet the abnormal data determining conditions of the corresponding levels, determining that the data to be confirmed are abnormal data.

Determining whether the data to be confirmed of the corresponding hierarchy meets the condition or not according to the abnormal data determination condition, and if so, determining that the data to be confirmed is abnormal data; if not, determining that the data to be confirmed is not abnormal data. For example, a receiving time threshold of the data to be confirmed is set in the abnormal data determination condition, and if the receiving time of the data to be confirmed exceeds the receiving time threshold, it is determined that the data to be confirmed has a delay, and the data to be confirmed is abnormal data.

The current data of one hierarchy may include a plurality of data to be confirmed, that is, one hierarchy may be provided with a plurality of abnormal data determination rules to determine the data to be confirmed of different dimensions in one hierarchy. The data to be confirmed of each level can be judged whether to be abnormal data or not, and the result of the abnormal data of each level is obtained. There may be a plurality of data to be confirmed in one level, that is, one level may obtain the judgment results of a plurality of abnormal data. For current data in a hierarchy, there may be some current data that are anomalous data and some that are not anomalous data. By determining the abnormal data determination conditions corresponding to each level, the judgment precision of the abnormal data is effectively improved, the artificial judgment process is reduced, and the data monitoring efficiency is further improved.

In this embodiment, if the data to be confirmed meets the abnormal data determination condition of the corresponding hierarchy, determining that the data to be confirmed is the abnormal data includes: and if the data to be confirmed exceed the preset current monitoring threshold value of the corresponding level, determining that the data to be confirmed are abnormal data, and sending alarm prompt information.

Specifically, the abnormal data determination condition may be set with a current monitoring threshold, where the current monitoring threshold is a maximum value, a minimum value, or a numerical range that the data to be confirmed in the current data is allowed to reach. For example, the abnormal data determination condition may specify that data to be confirmed that exceeds a current monitoring threshold is determined as abnormal data. When abnormal data is judged, comparing the data to be confirmed with a current monitoring threshold value, judging whether the data to be confirmed exceeds the preset current monitoring threshold value, and if so, determining the data to be confirmed as abnormal data; if not, determining that the data to be confirmed is not abnormal data.

For example, the current monitoring threshold set in the abnormal data determination condition is 50% for determining the resource usage rate of the CPU. The data to be confirmed is the current resource utilization rate of the CPU in the current data, the current resource utilization rate of the CPU is 60%, and 60% exceeds 50%, so that it can be determined that the current resource utilization rate of the CPU is abnormal data.

After determining that the abnormal data exists, an alarm prompt message may be sent to the user, for example, a voice alarm may be sent or a pop-up window prompt may be sent to prompt the user that the abnormality exists currently. The alarm prompt information can include the hierarchy of the abnormal data, so that the abnormal data can be conveniently checked by a user. The current data which is not the abnormal data can be displayed without prompting, and the abnormal data is labeled, so that the viewing efficiency and precision of a user are improved.

The beneficial effect who sets up like this lies in, through predetermineeing current monitoring threshold value, can judge unusual data fast, improves the efficiency of data monitoring. The user can modify the current monitoring threshold according to actual requirements, and the flexibility and the precision of data monitoring are improved. By sending the alarm prompt information, the user can be reminded in time, the influence of abnormal data on the application of the container is avoided, the professional requirements on the user are reduced, and the operation efficiency and the operation precision of the container are improved.

And S204, determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience base.

The expert experience base can store various data monitoring strategies, and different data monitoring strategies can correspond to schemes for solving different abnormal data conditions. When abnormal data occurs, a data monitoring strategy which can be applied when the abnormal data is solved can be searched and used as a target data monitoring strategy.

In this embodiment, determining a target data monitoring policy associated with the abnormal data according to a preset expert experience library includes: searching historical data corresponding to the abnormal data according to a preset expert experience library; and obtaining a target data monitoring strategy associated with the abnormal data according to the historical data monitoring strategy of the historical data.

Specifically, each data monitoring policy may include a policy manually input by a user, and may also include a historical data monitoring policy applied in historical data. And after the abnormal data are obtained, searching historical data corresponding to the abnormal data from the expert experience base. For example, historical data that is consistent with the abnormal data may be found, or historical data that is within a preset numerical range of the abnormal data may be found. And determining a historical data monitoring strategy applied under the condition of the found historical data, namely, obtaining the historical data monitoring strategy applied for solving the abnormal condition of the historical data. And determining the found historical data monitoring strategy as a target data monitoring strategy associated with the abnormal data.

The data monitoring policy may include acquisition frequency, acquisition data, and the like, for example, the current data monitoring policy is to acquire the acquisition data every 30 seconds, and the acquisition data is the current resource utilization rate of the CPU. If the current CPU resource utilization rate is 60% and the current CPU resource utilization rate is abnormal data, whether the 60% CPU resource utilization rate exists in the historical data or not is searched. And if so, determining a data monitoring strategy when the CPU resource utilization rate is 60% in the historical data, and determining the data monitoring strategy as a target data monitoring strategy.

If not, one strategy can be selected from the manually input strategies to serve as the target data monitoring strategy. For example, in the artificially input policies, each policy corresponds to a preset abnormal data, the currently and actually determined abnormal data is compared with the preset abnormal data to determine whether the currently and actually determined abnormal data exists in the expert experience base, and if yes, the policy corresponding to the abnormal data is determined as the target data monitoring policy. And randomly searching a data monitoring strategy of a corresponding level from an expert experience library to serve as a target data monitoring strategy.

The method has the advantages that through searching the historical data, the solution when the same abnormal data occurs in the historical data can be determined, the target data monitoring strategy is directly obtained, the target data monitoring strategy is prevented from being re-determined, the strategy updating efficiency and precision are improved, and the data monitoring efficiency and precision are further improved.

In this embodiment, after searching for historical data corresponding to the abnormal data according to a preset expert experience library, the method further includes: determining a historical monitoring threshold value in the historical data according to the historical data; and determining the historical monitoring threshold as a target monitoring threshold instead of the current monitoring threshold.

Specifically, the expert experience base may include a plurality of data monitoring strategies and may further include a plurality of monitoring thresholds. The monitoring threshold may be set in advance by a person, or may be a historical monitoring threshold applied to various types of historical data. After determining the historical data corresponding to the abnormal data, determining a historical monitoring threshold under the historical data, determining the historical monitoring threshold as a target monitoring threshold, and replacing the current monitoring threshold.

For example, if the current monitoring threshold is 50%, the abnormal data is 60%, and if the historical data is also 60%, the historical monitoring threshold is 55%, 55% may be used as the target monitoring threshold instead of 50%. If the historical data and the historical monitoring threshold corresponding to the abnormal data do not exist in the expert experience base, the current monitoring threshold can be increased or decreased according to a preset monitoring threshold adjusting rule to obtain the target monitoring threshold. For example, the monitoring threshold adjustment rule may be that the current monitoring threshold is increased by 5% each time.

The beneficial effect of the setting is that the expert experience base can store various data monitoring strategies and various monitoring threshold values. By searching historical data, the target monitoring threshold can be quickly determined, judgment conditions for monitoring abnormal data are obtained, the determination efficiency of the target monitoring threshold is improved, and monitoring threshold errors caused by re-determining the monitoring threshold are avoided. The monitoring strategy and the monitoring threshold value are flexibly changed, user operation is reduced, and the efficiency and the flexibility of data monitoring are improved.

And S205, according to the target data monitoring strategy, carrying out data acquisition on the hierarchy corresponding to the abnormal data to obtain the target data of the hierarchy corresponding to the abnormal data.

Fig. 3 is a schematic flowchart of a data monitoring method based on containerization service according to an embodiment of the present application, which is an alternative embodiment based on the foregoing embodiment.

In this embodiment, the data monitoring policy includes acquisition frequency and acquisition data; correspondingly, according to the current data monitoring strategy of the hierarchy in the container, the hierarchy data in the container is acquired to obtain the current data of the hierarchy in the container, and the hierarchy data can be subdivided into: and acquiring the acquired data of the hierarchy in the container according to the acquisition frequency in the current data monitoring strategy to obtain the current data of the hierarchy.

As shown in fig. 3, the method comprises the steps of:

s301, collecting the collected data of the hierarchy in the container according to the collection frequency in the current data monitoring strategy to obtain the current data of the hierarchy.

The data monitoring strategy may include acquisition frequency and acquisition data, the acquisition frequency refers to an acquisition period for acquiring the acquisition data, the acquisition data is data in each layer to be acquired, and the acquisition data may be represented by a data name or a number. For example, for the service layer, if the current data monitoring policy is to collect service logs every ten minutes, the collection frequency is ten minutes, and the collected data is the service logs.

And determining the acquisition frequency and the acquisition data in the current data monitoring strategy of each hierarchy, and acquiring the acquisition data in the corresponding hierarchy according to the acquisition frequency to obtain the current data of the corresponding hierarchy. By setting the acquisition frequency and the acquisition data, the real-time or timed data acquisition can be realized, the user operation is reduced, the data omission is avoided, and the efficiency and the precision of data monitoring are improved.

In this embodiment, after determining that the current data of the hierarchy in the container is abnormal data, the method further includes: determining a target abnormal data operation and maintenance rule associated with abnormal data according to a preset candidate abnormal data operation and maintenance rule; and according to the operation and maintenance rule of the target abnormal data, checking the corresponding level of the abnormal data to obtain a checking result.

Specifically, if abnormal data exists in any hierarchy in the container, the abnormal data can be recorded, and the operation and maintenance of the abnormal data can be automatically performed to investigate the reason of the abnormality.

Various abnormal data operation and maintenance rules can be preset to serve as candidate abnormal data operation and maintenance rules. And pre-storing the association relationship between the candidate abnormal data operation and maintenance rules and the preset abnormal data, namely, associating and storing different operation and maintenance methods for different preset abnormal data. And after the abnormal data are determined to exist, determining an abnormal data operation and maintenance rule associated with the abnormal data from the candidate abnormal data operation and maintenance rules as a target abnormal data operation and maintenance rule. For example, the abnormal data indicates that the CPU resource usage rate is too high, and for the abnormal data, if the associated candidate abnormal data operation and maintenance rule is rule one, the rule one is determined as the target abnormal data operation and maintenance rule.

And according to the operation and maintenance rule of the target abnormal data, checking the hierarchy corresponding to the abnormal data, determining the cause of the abnormal data, and obtaining a checking result. The investigation result may be the cause of the abnormal data, or may be new current data acquired again after the operation and maintenance of the abnormal data. For example, for abnormal data with a decreased service success rate, the checking mode set in the operation and maintenance rule of the target abnormal data may be to synchronously complete service call check, check whether the service is normal, and check whether the service delay is increased to obtain a checking result. The operation state of the system layer container may be queried according to a service exception scenario, and if the container is not ready due to the node state exception, the service traffic may be switched to the standby container. Aiming at the problems of normal service and delayed rise, the method inquires the occupied data of the system layer connection and simultaneously monitors and checks whether the database connection is normal or not. If the system side connection is normal, the use of the CPU, the memory, the storage and the network resource of the equipment layer is further checked and analyzed, and the corresponding data is snapshot-stored so as to be developed and analyzed. If resource bottlenecks occur in the process of checking the CPU, the memory, the storage and the network resources of the equipment layer, the acquisition frequency of the monitoring component can be reduced or the acquisition is closed. And recovering the original data monitoring strategy when the resources are sufficient. In this embodiment, the specific abnormal data operation and maintenance rule may provide a reference configuration according to the operation and maintenance experience library, and may be adjusted by the user.

The method has the advantages that the operation and maintenance rules of the target abnormal data can be determined according to the abnormal data, automatic operation and maintenance and troubleshooting can be carried out, the abnormality can be corrected in time, the influence of the abnormal data is reduced, and automatic processing of container monitoring is realized.

In this embodiment, after obtaining the current data of the hierarchy in the container, the method further includes: and storing the current data and the corresponding hierarchy into a preset database in an associated manner, and displaying the data on a visual interface.

Specifically, after the current data of each hierarchy is obtained, the current data of each hierarchy may be stored, and the current data may be displayed on a visualization interface. For example, a data storage may be provided in the multi-dimensional real-time monitoring and analysis tool of the container for storing collected log data and the like. The data memory is an abstract memory, and may adopt different memory structures, for example, a plurality of different memory types such as a database, Kafka (Kafka card), and HDFS (Hadoop Distributed File System). A monitoring displayer can be arranged in the multi-dimensional real-time monitoring and analyzing tool of the container and used for visually displaying the current data stored in the data storage device so as to be convenient for inquiring.

The beneficial effect of setting up like this lies in, unify storage and processing to current data, carry out unified data presentation according to the data of storage, the subsequent look over of user of being convenient for and improve to the unified analysis of each dimension data.

In this embodiment, before acquiring the hierarchy data in the container according to the current data monitoring policy of the hierarchy in the container and obtaining the current data of the hierarchy in the container, the method further includes: a data monitoring policy associated with a tier in the container is determined as a current data monitoring policy for the tier.

Specifically, each hierarchy corresponds to its own current data monitoring policy, and an initial current data monitoring policy may be associated with each hierarchy in advance. And determining a current data monitoring strategy associated with each hierarchy in the container, so that data acquisition is conveniently carried out on each hierarchy in the container according to the current data monitoring strategy of the hierarchy in the container to obtain current data of the hierarchy in the container. By determining the current data monitoring rule of each level, data acquisition can be carried out aiming at each level, so that data confusion is avoided, and the precision and the efficiency of data monitoring are improved.

S302, if the condition is determined according to the preset abnormal data, the current data of the hierarchy in the container is determined to be the abnormal data, and the target data monitoring strategy related to the abnormal data is determined according to a preset expert experience base.

And S303, according to the target data monitoring strategy, performing data acquisition on the hierarchy corresponding to the abnormal data to obtain the target data of the hierarchy corresponding to the abnormal data.

According to the embodiment of the application, a current data monitoring strategy is set for each level, and data of each level are collected to obtain current data of each level. The data of each level can be acquired respectively, and the data of each level can be monitored and analyzed uniformly. Presetting an abnormal data determining condition, and judging whether the current data is abnormal data. And if so, determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience library. And replacing the current data monitoring strategy with the target data monitoring strategy, and acquiring the data of the hierarchy according to the target data monitoring strategy to obtain the target data. When the abnormal data monitoring strategy is abnormal, the monitoring strategy can be adjusted for each abnormal level, monitoring is carried out under a new monitoring strategy, the abnormal condition can be acquired more accurately, and automatic updating of the data monitoring strategy of each level is realized. The problem of among the prior art, can't carry out the automatic adjustment of monitoring strategy according to the abnormal condition is solved, the condition that fixed monitoring rule caused the abnormal judgment mistake appears is avoided. And the monitoring strategies of all levels are independent from each other and are updated respectively, so that the flexibility of data monitoring is improved, and the efficiency and the precision of the data monitoring are further improved.

Fig. 4 is a schematic structural diagram of a multi-dimensional real-time container monitoring and analyzing tool according to an embodiment of the present disclosure. The tool provided by the embodiment can execute a data monitoring method based on containerization service. As shown in fig. 4, the container multi-dimensional real-time monitoring and analyzing tool 41 includes a data collector 410, a policy configurator 411, an expert experience library 412, a data storage 413 and a monitoring presenter 414.

The data collector 410 collects data of each hierarchy according to the current data monitoring policy of each hierarchy in the policy configurator 411, and obtains current data of each hierarchy. After obtaining the current data, the data collector 410 may determine a condition according to preset abnormal data, and determine whether the current data is abnormal data. If yes, a target data monitoring policy associated with the abnormal data is determined according to a preset expert experience base 412, and the target data monitoring policy is sent to the policy configurator 411. It is convenient to acquire new current data according to the target data monitoring policy in the policy configurator 411. The current data obtained by data collector 410 may also be sent to data storage 413 for storage and monitoring display 414 for display.

Fig. 5 is a schematic structural diagram of a data monitoring apparatus based on containerization service according to an embodiment of the present disclosure, where the apparatus may be configured on a container multi-dimensional real-time monitoring and analyzing tool, and the apparatus may be implemented by software, hardware, or a combination of the two. As shown in fig. 5, the apparatus includes: a current data acquisition module 501, a target data monitoring policy determination module 502 and a target data acquisition module 503.

A current data acquisition module 501, configured to acquire data of a hierarchy in a container according to a current data monitoring policy of the hierarchy in the container, so as to obtain current data of the hierarchy in the container;

a target data monitoring policy determining module 502, configured to determine, according to a preset expert experience base, a target data monitoring policy associated with abnormal data if it is determined that current data of a hierarchy in the container is abnormal data according to a preset abnormal data determining condition;

and a target data obtaining module 503, configured to perform data acquisition on the hierarchy corresponding to the abnormal data according to the target data monitoring policy, so as to obtain target data of the hierarchy corresponding to the abnormal data.

Optionally, the target data monitoring policy determining module 502 includes:

the data to be confirmed determining unit is used for determining data to be confirmed of the hierarchy from the current data according to preset abnormal data determining conditions of the hierarchy in the container;

and the abnormal data determining unit is used for determining the data to be confirmed as abnormal data if the data to be confirmed meets the abnormal data determining condition of the corresponding level.

Optionally, the abnormal data determining unit is specifically configured to:

and if the data to be confirmed exceed the preset current monitoring threshold value of the corresponding level, determining that the data to be confirmed are abnormal data, and sending alarm prompt information.

Optionally, the target data monitoring policy determining module 502 includes:

the historical data searching unit is used for searching historical data corresponding to the abnormal data according to a preset expert experience base;

and the target strategy obtaining unit is used for obtaining the target data monitoring strategy related to the abnormal data according to the historical data monitoring strategy of the historical data.

Optionally, the apparatus further comprises:

the historical monitoring threshold determining module is used for determining a historical monitoring threshold in the historical data according to the historical data after searching the historical data corresponding to the abnormal data according to a preset expert experience base;

and the target monitoring threshold determining module is used for determining the historical monitoring threshold as a target monitoring threshold to replace the current monitoring threshold.

Optionally, the apparatus further comprises:

the target abnormal data operation and maintenance rule determining module is used for determining a target abnormal data operation and maintenance rule related to the abnormal data according to a preset candidate abnormal data operation and maintenance rule after determining that the current data of the hierarchy in the container is the abnormal data;

and the troubleshooting result obtaining module is used for troubleshooting the corresponding level of the abnormal data according to the operation and maintenance rule of the target abnormal data to obtain a troubleshooting result.

Optionally, the apparatus further comprises:

and the data storage module is used for storing the current data and the corresponding hierarchy into a preset database in a correlated manner after the current data of the hierarchy in the container is obtained, and displaying the data on a visual interface.

Optionally, the apparatus further comprises:

and the current data monitoring strategy determining module is used for determining a data monitoring strategy related to the middle hierarchy of the container as the current data monitoring strategy of the hierarchy before acquiring the current data of the middle hierarchy of the container according to the current data monitoring strategy of the middle hierarchy of the container.

Optionally, the data monitoring policy includes acquisition frequency and acquisition data;

accordingly, the current data obtaining module 501 is specifically configured to:

and acquiring the acquired data of the hierarchy in the container according to the acquisition frequency in the current data monitoring strategy to obtain the current data of the hierarchy.

Optionally, the hierarchy of containers includes a business layer, a service layer, and a device layer.

FIG. 6 is a block diagram illustrating an electronic device, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like, in accordance with an exemplary embodiment.

Apparatus 600 may include one or more of the following components: a processing component 602, a memory 604, a power component 606, a multimedia component 608, an audio component 610, an input/output (I/O) interface 612, a sensor component 614, and a communication component 616.

The processing component 602 generally controls overall operation of the device 600, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 602 can include one or more modules that facilitate interaction between the processing component 602 and other components. For example, the processing component 602 can include a multimedia module to facilitate interaction between the multimedia component 608 and the processing component 602.

The memory 604 is configured to store various types of data to support operations at the apparatus 600. Examples of such data include instructions for any application or method operating on device 600, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 604 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

Power supply component 606 provides power to the various components of device 600. The power components 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 600.

The multimedia component 608 includes a screen that provides an output interface between the device 600 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 600 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 610 is configured to output and/or input audio signals. For example, audio component 610 includes a Microphone (MIC) configured to receive external audio signals when apparatus 600 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 604 or transmitted via the communication component 616. In some embodiments, audio component 610 further includes a speaker for outputting audio signals.

The I/O interface 612 provides an interface between the processing component 602 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor component 614 includes one or more sensors for providing status assessment of various aspects of the apparatus 600. For example, the sensor component 614 may detect an open/closed state of the device 600, the relative positioning of components, such as a display and keypad of the device 600, the sensor component 614 may also detect a change in position of the device 600 or a component of the device 600, the presence or absence of user contact with the device 600, orientation or acceleration/deceleration of the device 600, and a change in temperature of the device 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 616 is configured to facilitate communications between the apparatus 600 and other devices in a wired or wireless manner. The apparatus 600 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 616 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 600 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 604 comprising instructions, executable by the processor 620 of the apparatus 600 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

A non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor of a terminal device, enable the terminal device to perform the above-mentioned containerization service-based data monitoring method.

The application also discloses a computer program product comprising a computer program which, when executed by a processor, implements the method as described in the embodiments.

Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or electronic device.

In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data electronic device), or that includes a middleware component (e.g., an application electronic device), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include a client and an electronic device. The client and the electronic device are generally remote from each other and typically interact through a communication network. The relationship of client and electronic device arises by virtue of computer programs running on the respective computers and having a client-electronic device relationship to each other. The electronic device may be a cloud electronic device, which is also called a cloud computing electronic device or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service extensibility in a traditional physical host and a VPS service ("Virtual Private Server", or "VPS" for short). The electronic device may also be a distributed system of electronic devices or an electronic device incorporating a blockchain. It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A data monitoring method based on containerization service is characterized by comprising the following steps:

acquiring data of the middle hierarchy of the container according to a current data monitoring strategy of the middle hierarchy of the container to obtain current data of the middle hierarchy of the container;

2. The method according to claim 1, wherein determining that current data of a hierarchy in the container is abnormal data according to a preset abnormal data determination condition comprises:

determining conditions according to preset abnormal data of the levels in the container, and determining data to be confirmed of the levels from the current data;

and if the data to be confirmed meet the abnormal data determination conditions of the corresponding levels, determining that the data to be confirmed are abnormal data.

3. The method according to claim 2, wherein determining that the data to be confirmed is abnormal data if the data to be confirmed satisfies an abnormal data determination condition of a corresponding hierarchy includes:

4. The method of claim 1, wherein determining a target data monitoring strategy associated with the abnormal data according to a preset expert experience library comprises:

searching historical data corresponding to the abnormal data according to a preset expert experience library;

and obtaining the target data monitoring strategy associated with the abnormal data according to the historical data monitoring strategy of the historical data.

5. The method according to claim 4, after searching the historical data corresponding to the abnormal data according to a preset expert experience library, further comprising:

determining a historical monitoring threshold value in the historical data according to the historical data;

and determining the historical monitoring threshold as a target monitoring threshold instead of the current monitoring threshold.

6. The method of claim 1, after determining that current data of the hierarchy in the container is anomalous data, further comprising:

determining a target abnormal data operation and maintenance rule associated with the abnormal data according to a preset candidate abnormal data operation and maintenance rule;

and according to the operation and maintenance rule of the target abnormal data, checking the corresponding level of the abnormal data to obtain a checking result.

7. The method of claim 1, further comprising, after obtaining current data for a hierarchy in the container:

and storing the current data and the corresponding hierarchy into a preset database in an associated manner, and displaying the data on a visual interface.

8. The method according to any one of claims 1-7, wherein before performing data collection on a hierarchy in a container according to a current data monitoring policy of the hierarchy in the container to obtain current data of the hierarchy in the container, the method further comprises:

determining a data monitoring policy associated with a hierarchy in the container as a current data monitoring policy for the hierarchy.

9. The method according to any one of claims 1-7, wherein the data monitoring strategy comprises a collection frequency and a collection data;

correspondingly, according to the current data monitoring strategy of the middle hierarchy of the container, acquiring the data of the middle hierarchy of the container to obtain the current data of the middle hierarchy of the container, and the method comprises the following steps:

10. The method of any of claims 1-7, wherein the hierarchy of containers comprises a business layer, a service layer, and a device layer.

11. A containerized service-based data monitoring apparatus, comprising:

12. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored by the memory to implement the containerization service-based data monitoring method of any of claims 1-10.

13. A computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when executed by a processor, are used for implementing the containerization service-based data monitoring method according to any one of claims 1-10.

14. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, implements a containerization services-based data monitoring method according to any one of claims 1-10.