CN110750425A - Database monitoring method, device and system and storage medium - Google Patents
Database monitoring method, device and system and storage medium Download PDFInfo
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- CN110750425A CN110750425A CN201911020815.5A CN201911020815A CN110750425A CN 110750425 A CN110750425 A CN 110750425A CN 201911020815 A CN201911020815 A CN 201911020815A CN 110750425 A CN110750425 A CN 110750425A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
Abstract
The invention relates to a database monitoring method, a device, a system and a storage medium, wherein the method comprises the following steps: collecting log information in real time, and preprocessing the log information to obtain state information; the monitoring server sends the state information to the execution server; the execution server stores the state information to a pre-constructed training model to obtain a target model; and monitoring the state of the current database by using the target model, and sending an early warning message when the current database is abnormal. The real-time monitoring and intelligent early warning of the state of the database are realized, and a large amount of operation and maintenance labor cost is saved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a database monitoring method, a database monitoring device, a database monitoring system and a storage medium.
Background
A database, which is a collection of data stored together in a manner that can be shared by multiple users, has as little redundancy as possible, and is independent of the application program, can be considered as an electronic file cabinet for storing electronic files. The user can perform operations such as adding, inquiring, updating and deleting on the data in the file.
With the advancement of science and technology, more and more files are stored in the form of databases, however, the traffic volume is also increasing, the storage volume is also becoming larger and larger, and the volume of the databases becomes larger and larger. In this case, the monitoring and operation and maintenance of the database are very important. For example, predicting database problems in advance and avoiding risks are of paramount importance.
In a database monitoring system in the related art, a monitoring system is usually deployed on one server, and if the monitoring system fails, information of a database cannot be sent out. Moreover, whether the risk exists is judged by depending on the experience of database management personnel, the risk can only be predicted and avoided by actively processing and optimizing the data by the database management personnel. In addition, there is a certain time lag in data acquisition.
Disclosure of Invention
In view of this, a database monitoring method, apparatus, system and storage medium are provided to solve the problems in the prior art that data acquisition cannot be performed in real time, risks cannot be predicted and avoided, and operation and maintenance labor costs are high in the database monitoring process.
The invention adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a database monitoring method, where the database monitoring method includes:
collecting log information in real time, and preprocessing the log information to obtain state information;
the monitoring server sends the state information to an execution server;
the execution server stores the state information to a pre-constructed training model to obtain a target model;
and monitoring the state of the current database by applying the target model, and sending an early warning message when the current database is abnormal.
Further, the acquiring log information in real time and preprocessing the log information to obtain state information includes:
collecting log information in real time;
preliminarily cleaning the log information, and sending the preliminarily cleaned log information to a message queue;
and carrying out secondary cleaning and formatting treatment on the cleaned log information in the message queue to obtain state information.
Further, the sending, by the monitoring server, the status information to an execution server includes:
and the monitoring server applies a fair scheduling algorithm to transmit the state information to an execution server.
Further, the execution server stores the state information to a pre-constructed training model to obtain a target model;
the execution server stores the state information to a pre-constructed training model as a training sample;
and training the pre-constructed training model by applying the training sample to obtain a target model.
Further, the monitoring the state of the current database by applying the target model, and sending an early warning message when the current database is abnormal includes:
packaging the target model to an interface;
and calling the interface, and sending an early warning message when the current database is abnormal.
Further, the method also comprises the following steps: the monitoring server and the execution server monitor each other by sending heartbeat packets.
Further, the monitoring server and the execution server monitor each other by sending heartbeat packets, including:
if the monitoring server stops working, the execution server gives an alarm and controls the monitoring server to restart, and after the restart fails for more than the preset times, the execution server which detects that the monitoring server stops working at the earliest time is automatically changed into the monitoring server;
and if the execution server stops working, the monitoring server controls the execution server to restart.
In a second aspect, an embodiment of the present application provides a database monitoring apparatus, where the apparatus includes:
the information acquisition module is used for acquiring log information in real time and preprocessing the log information to obtain state information;
the information issuing module is used for indicating the monitoring server to issue the state information to the execution server;
the target model determining module is used for indicating the execution server to store the state information into a pre-constructed training model so as to obtain a target model;
and the early warning module is used for monitoring the state of the current database by applying the target model and sending early warning information when the current database is abnormal.
Further, the information acquisition module is specifically configured to:
collecting log information in real time;
the log information is preliminarily cleaned, and the log information after preliminary cleaning is sent to a message queue;
and carrying out secondary cleaning and formatting treatment on the cleaned log information in the message queue to obtain state information.
Further, the information issuing module is specifically configured to:
the monitoring server applies a fair scheduling algorithm to send the state information to the execution server.
Further, the object model determining module 303 is specifically configured to;
the execution server stores the state information to a pre-constructed training model as a training sample;
and training the pre-constructed training model by using the training sample to obtain the target model.
Further, the early warning module is specifically configured to:
packaging the target model to an interface;
and calling an interface, and sending an early warning message when the current database is abnormal.
Further, still include the monitoring module, the monitoring module is specifically used for: the monitoring server and the execution server monitor each other by sending heartbeat packets.
Furthermore, the monitoring module comprises a control module, which is used for alarming the execution server when the monitoring server stops working, controlling the monitoring server to restart, and automatically converting the execution server which detects that the monitoring server stops working into the monitoring server at the earliest time after the monitoring server fails to restart for more than the preset times;
the monitoring module also comprises a restarting module, and the restarting module is used for controlling the execution server to restart when the execution server stops working.
In a third aspect, an embodiment of the present application provides a database monitoring system, where the database monitoring system includes:
a monitoring server;
at least one execution server;
a processor, and a memory coupled to the processor;
the memory is configured to store a computer program configured to perform at least the database monitoring method of the first aspect;
the processor is used for calling and executing the computer program in the memory.
In a fourth aspect, an embodiment of the present application provides a storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps in the database monitoring method according to the first aspect.
By adopting the technical scheme, the log information is collected in real time, and is preprocessed to obtain the state information, so that the obtained state information can be directly applied by the monitoring server and the execution server; the monitoring server sends the state information to the execution server; the execution server stores the state information to a pre-constructed training model to obtain a target model; and monitoring the state of the current database by using the target model, and sending an early warning message when the current database is abnormal. Therefore, the problem that the state information of the database cannot be sent out when the monitoring system fails is avoided; the intelligent risk early warning and the intelligent tuning of database monitoring are realized, and a large amount of operation and maintenance labor cost is saved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a database monitoring method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another database monitoring method provided by an embodiment of the invention;
fig. 3 is a schematic structural diagram of a database monitoring apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a database monitoring system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
First, the basic concept of the embodiments of the present application will be explained. The monitoring server included in the embodiment of the application can be used as a main node or a management node in a monitoring network; the execution server may also be referred to as a work server, and may serve as a leaf node or a work node in the monitoring network. The embodiment of the application adopts an M/S (Master/Slave) framework.
Examples
Fig. 1 is a flowchart of a database monitoring method according to an embodiment of the present invention, where the method may be performed by a database monitoring apparatus according to an embodiment of the present invention, and the apparatus may be implemented in software and/or hardware. Referring to fig. 1, the method may specifically include the following steps:
s101, collecting log information in real time, and preprocessing the log information to obtain state information.
The log information is stored in a log file, and the log file is a recording file or a file set for recording the operating time of the system, can be divided into an event log and a message log, and has important functions of processing historical data, tracing diagnosis problems, understanding system activities and the like. Specifically, in the normal operation process of the database, log information can be generated in real time, and the operation state and other conditions of the database can be known by analyzing the log information. In the embodiment of the present application, after the log information is collected in real time, the log information needs to be preprocessed to obtain processable or available data, which is referred to as status information. And then pushing the state information to a monitoring server.
In a specific example, log information can be collected in real time by a data capture module deployed on a database server, where the data capture module can be a software program, and can be represented by an agent, for example.
Optionally, the state information of the database includes running data of the data, state information of the operating system, database operation information, and the like.
And S102, the monitoring server sends the state information to the execution server.
Specifically, the monitoring server serves as a master node and issues the state information to each downstream execution server. In an actual application process, the number of execution servers is usually multiple. Therefore, a plurality of execution servers can receive the state information, and the condition that the state information is lost when one execution server is broken is avoided.
S103, the execution server stores the state information to a pre-constructed training model to obtain a target model.
Specifically, after receiving the state information, each execution server stores the state information in a pre-constructed training model. For example, the state information may be used as a training sample to train a training model, and when a certain convergence condition is satisfied, the training is completed to obtain a target model.
In a specific example, in the process from the pre-constructed training model to the training of the target model, the historical monitoring data may be labeled, where the historical monitoring data is status information, and then the pre-constructed training model may be trained by combining a supervised algorithm and an unsupervised algorithm to obtain the target model. The pre-constructed training model may be a machine learning model, for example, a machine learning model in the prior art, and is only for illustration and not intended to be limiting.
And S104, monitoring the state of the current database by using the target model, and sending an early warning message when the current database is abnormal.
Specifically, the state information of the current database is input into the target model to obtain the output of the target model, so that the target model can be used for monitoring and judging the state of the current database. In a specific example, if the current database is monitored to be abnormal, an early warning message may be sent to the terminal device of the monitoring manager, where the early warning message may be a text message, a voice message, or a combination of the text message and the voice message; the terminal device of the monitoring manager can be a mobile phone or a tablet personal computer of the monitoring manager, and can also be a computer in a monitoring room. Therefore, when the database is abnormal, the monitoring management personnel can know the abnormal information at the first time and further take corresponding measures. In a specific example, the corresponding measures may be optimization actions as well as optimization suggestions. For example, the content of the warning information may be "when the space is waiting to be full" or "when the number of connections is waiting to be full" or the like.
By adopting the technical scheme, the log information is collected in real time, and is preprocessed to obtain the state information, so that the obtained state information can be directly applied by the monitoring server and the execution server; the monitoring server sends the state information to the execution server; the execution server stores the state information to a pre-constructed training model to obtain a target model; and monitoring the state of the current database by using the target model, and sending an early warning message when the current database is abnormal. Therefore, the problem that the state information of the database cannot be sent out when the monitoring system fails is avoided; the intelligent risk early warning and the intelligent tuning of database monitoring are realized, and a large amount of operation and maintenance labor cost is saved.
Fig. 2 is a flowchart of a database monitoring method according to another embodiment of the present invention, which is implemented on the basis of the foregoing embodiment. Referring to fig. 2, the method may specifically include the following steps:
s201, collecting log information in real time.
S202, carrying out primary cleaning on the log information, and sending the log information after the primary cleaning to a message queue.
The message queue is used for storing the log information after the initial cleaning. Data cleansing refers to the last procedure to find and correct recognizable errors in data files, including checking data consistency, processing invalid and missing values, etc. In the embodiment of the application, after the log information is cleaned, the primarily cleaned log information is sequentially sent to the message queue for storage, so that a plurality of log information can be stored in the same message queue.
S203, carrying out secondary cleaning and formatting treatment on the cleaned log information in the message queue to obtain state information.
Specifically, the real-time calculation program is used for analyzing the messages in the message queue, then secondary cleaning is carried out on the log information after primary cleaning in the message queue, and formatting processing is carried out, so that state information which can be recognized and processed by the monitoring server and the execution server can be obtained. That is, the monitoring server may receive the pushed status information.
And S204, the monitoring server applies a fair scheduling algorithm to transmit the state information to the execution server.
The scheduling process refers to that an operating system manages limited resources of the system, when there are requests sent by multiple processes or multiple processes to use the resources, the processes or the requests must be selected according to a certain principle to occupy the resources because of the limitation of the resources, and the process is called scheduling. The scheduling algorithm includes a policy of how to perform scheduling, and a fairness scheduling algorithm is applied in the embodiment of the present application, and specifically how to apply the fairness scheduling algorithm to issue state information is the same as the principle of the fairness scheduling algorithm in the related art, which is not described herein again. Specifically, the monitoring server applies a fair scheduling algorithm to issue the state information to the execution server.
S205, the execution server stores the state information to a pre-constructed training model to be used as a training sample.
In the embodiment of the application, the execution server stores the state information as a training sample of a pre-constructed training model. Specifically, the training samples may be state information of different servers in different working states. The larger the number of training samples is, the more accurate the target model obtained by final training is.
S206, training the pre-constructed training model by applying the training samples to obtain the target model.
Specifically, the target model may be obtained by training a pre-constructed training model using the training samples, for example, the pre-constructed training model may be determined by combining a supervised algorithm and an unsupervised algorithm. Therefore, the training samples which cannot be applied in the supervision algorithm can be applied in the unsupervised algorithm, the waste of the training samples is avoided, and the training samples can be fully utilized.
And S207, packaging the target model to an interface.
Specifically, the target model is encapsulated to the interface for subsequent convenient direct application. In the application process, the interface is directly called, so that the target model corresponding to the interface can be directly applied.
And S208, calling an interface, and sending the early warning message when the current database is abnormal.
For example, in an actual application process, in order to determine whether the current state of the database is normal or abnormal, after the current state of the current database is acquired, the interface is called, and the target model is applied to determine the current working state of the current database. If the current working state of the current database is an abnormal state, sending an early warning message to a monitoring manager, for example, sending a voice early warning message to a mobile phone of the monitoring manager.
In addition, the application of the interface also comprises table space expansion, master-slave switching, data backup reply, synchronous delay processing, database lock processing, automatic restart service and the like.
In the embodiment of the application, after log information is collected in real time, the log information is subjected to primary cleaning, secondary cleaning and formatting treatment, so that state information which can be identified by the monitoring server and the execution server is obtained. Then the monitoring server applies a fair scheduling algorithm to send the state information to each execution server, so that the state information is guaranteed not to be lost after one server stops working; and the execution server trains a pre-constructed training model according to the state information to obtain a target model, and then calls an interface packaged by the model to realize the detection of whether the state of the database is abnormal. Therefore, intelligent early warning of the state of the server is realized, and a large amount of operation and maintenance labor cost is saved.
On the basis of the above embodiment, the technical solution of the embodiment of the present application further includes: the monitoring server and the execution server monitor each other by sending heartbeat packets; if the monitoring server stops working, the execution server gives an alarm and controls the monitoring server to restart, and after the restart fails for more than the preset times, the execution server which detects that the monitoring server stops working at the earliest time is automatically changed into the monitoring server; and if the execution server stops working, the monitoring server controls the execution server to restart.
In addition, in the embodiment of the application, the monitoring server and the execution server perform mutual monitoring in a mode of sending the heartbeat packet, so that one type of server can be controlled to notify the other type of server in time after the other type of server stops working. Specifically, if the monitoring server stops working, the execution server will actively alarm and try to restart the monitoring server, and after the restart fails for more than the preset number of times, the execution server that detects that the monitoring server stops working at the earliest is automatically changed into the monitoring server. In a specific example, the preset number may be 5, which is only illustrative and not intended to be limiting. In addition, the operation of converting the execution server into the monitoring server can be automatically modifying the internal parameters of the server to realize the conversion of the server type, so that different servers realize different functions.
In addition, the monitoring server can confirm whether the execution server normally works according to the heartbeat packet sent by the execution server, and if the execution server stops working, the monitoring server controls the execution server to restart.
In summary, in the existing database monitoring technology, most database system states are collected at regular time based on java or shell scripts and are pushed to database management personnel through mails, short messages and the like. Compared with the prior art, the technical scheme of the embodiment of the application can monitor the state of the database in real time, carry out intelligent risk early warning and intelligent tuning based on the machine learning algorithm, and save a large amount of operation and maintenance labor cost based on the reliability and expandability of the M/S framework. In addition, database failures can be intelligently preprocessed.
Fig. 3 is a schematic structural diagram of a database monitoring apparatus according to an embodiment of the present invention, which is suitable for executing a database monitoring method according to an embodiment of the present invention. As shown in fig. 3, the apparatus may specifically include: the system comprises an information acquisition module 301, an information issuing module 302, a target model determining module 303 and an early warning module 304.
The information acquisition module 301 is configured to acquire log information in real time and preprocess the log information to obtain state information; the information issuing module 302 is configured to instruct the monitoring server to issue the state information to the execution server; a target model determining module 303, configured to instruct the execution server to store the state information in a pre-constructed training model to obtain a target model; and the early warning module 304 is configured to monitor a state of the current database by using the target model, and send an early warning message when the current database is abnormal.
By adopting the technical scheme, the log information is collected in real time, and is preprocessed to obtain the state information, so that the obtained state information can be directly applied by the monitoring server and the execution server; the monitoring server sends the state information to the execution server; the execution server stores the state information to a pre-constructed training model to obtain a target model; and monitoring the state of the current database by using the target model, and sending an early warning message when the current database is abnormal. Therefore, the problem that the state information of the database cannot be sent out when the monitoring system fails is avoided; the intelligent risk early warning and the intelligent tuning of database monitoring are realized, and a large amount of operation and maintenance labor cost is saved.
Further, the information collection module 301 is specifically configured to:
collecting log information in real time;
the log information is preliminarily cleaned, and the log information after preliminary cleaning is sent to a message queue;
and carrying out secondary cleaning and formatting treatment on the cleaned log information in the message queue to obtain state information.
Further, the information issuing module 302 is specifically configured to:
the monitoring server applies a fair scheduling algorithm to send the state information to the execution server.
Further, the object model determining module 303 is specifically configured to;
the execution server stores the state information to a pre-constructed training model as a training sample;
and training the pre-constructed training model by using the training sample to obtain the target model.
Further, the early warning module 304 is specifically configured to:
packaging the target model to an interface;
and calling an interface, and sending an early warning message when the current database is abnormal.
Further, still include the monitoring module, the monitoring module is specifically used for: the monitoring server and the execution server monitor each other by sending heartbeat packets.
Furthermore, the monitoring module comprises a control module, which is used for alarming the execution server when the monitoring server stops working, controlling the monitoring server to restart, and automatically converting the execution server which detects that the monitoring server stops working into the monitoring server at the earliest time after the monitoring server fails to restart for more than the preset times;
the monitoring module also comprises a restarting module, and the restarting module is used for controlling the execution server to restart when the execution server stops working.
The database monitoring device provided by the embodiment of the invention can execute the data control monitoring method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
An embodiment of the present invention further provides a database monitoring system, please refer to fig. 4, and fig. 4 is a schematic structural diagram of the database monitoring system, as shown in fig. 4, the database monitoring system includes: a monitoring server 410, at least one execution server 420; a processor 430, and a memory 440 coupled to the processor 430; therein, two execution servers 420 are illustrated in fig. 4. The memory 440 is used for storing a computer program for performing at least the database monitoring method in the embodiment of the present invention; processor 430 is used to invoke and execute computer programs in memory; the database monitoring method specifically comprises the following steps: collecting log information in real time, and preprocessing the log information to obtain state information; the monitoring server sends the state information to the execution server; the execution server stores the state information to a pre-constructed training model to obtain a target model; and monitoring the state of the current database by using the target model, and sending an early warning message when the current database is abnormal.
The embodiment of the present invention further provides a storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the method implements the following steps in the database monitoring method in the embodiment of the present invention: collecting log information in real time, and preprocessing the log information to obtain state information; the monitoring server sends the state information to the execution server; the execution server stores the state information to a pre-constructed training model to obtain a target model; and monitoring the state of the current database by using the target model, and sending an early warning message when the current database is abnormal.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A database monitoring method, comprising:
collecting log information in real time, and preprocessing the log information to obtain state information;
the monitoring server sends the state information to an execution server;
the execution server stores the state information to a pre-constructed training model to obtain a target model;
and monitoring the state of the current database by applying the target model, and sending an early warning message when the current database is abnormal.
2. The method of claim 1, wherein collecting log information in real time and preprocessing the log information to obtain status information comprises:
collecting log information in real time;
preliminarily cleaning the log information, and sending the preliminarily cleaned log information to a message queue;
and carrying out secondary cleaning and formatting treatment on the cleaned log information in the message queue to obtain state information.
3. The method of claim 1, wherein the monitoring server sending the status information to an execution server comprises:
and the monitoring server applies a fair scheduling algorithm to transmit the state information to an execution server.
4. The method of claim 1, wherein the execution server stores the state information to a pre-constructed training model to obtain a target model;
the execution server stores the state information to a pre-constructed training model as a training sample;
and training the pre-constructed training model by applying the training sample to obtain a target model.
5. The method of claim 1, wherein the applying the target model monitors a status of a current database and sends an early warning message when the current database is abnormal, comprising:
packaging the target model to an interface;
and calling the interface, and sending an early warning message when the current database is abnormal.
6. The method of claim 1, further comprising: the monitoring server and the execution server monitor each other by sending heartbeat packets.
7. The method of claim 6, wherein the monitoring server and the execution server monitor each other by sending heartbeat packets, and the method comprises:
if the monitoring server stops working, the execution server gives an alarm and controls the monitoring server to restart, and after the restart fails for more than the preset times, the execution server which detects that the monitoring server stops working at the earliest time is automatically changed into the monitoring server;
and if the execution server stops working, the monitoring server controls the execution server to restart.
8. A database monitoring apparatus, comprising:
the information acquisition module is used for acquiring log information in real time and preprocessing the log information to obtain state information;
the information issuing module is used for indicating the monitoring server to issue the state information to the execution server;
the target model determining module is used for indicating the execution server to store the state information into a pre-constructed training model so as to obtain a target model;
and the early warning module is used for monitoring the state of the current database by applying the target model and sending early warning information when the current database is abnormal.
9. A database monitoring system, comprising:
a monitoring server;
at least one execution server;
a processor, and a memory coupled to the processor;
the memory is adapted to store a computer program for performing at least the database monitoring method of any of claims 1-7;
the processor is used for calling and executing the computer program in the memory.
10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, performs the steps of the database monitoring method according to any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112612679A (en) * | 2020-12-29 | 2021-04-06 | 太平金融科技服务（上海）有限公司 | System running state monitoring method and device, computer equipment and storage medium |
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