CN113342623A - Visual early warning system and method based on dynamic threshold method - Google Patents
Visual early warning system and method based on dynamic threshold method Download PDFInfo
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- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
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Abstract
The invention provides a visual early warning system and a visual early warning method based on a dynamic threshold method, which aim to solve the problems that the existing warning technology has no dynamic threshold monitoring and warning visual display; the method comprises the following steps: the python script obtains normal service performance to serve as a dynamically updated threshold template, compares a real-time service alarm value with the threshold template, and automatically triggers the mail alarm to visually display an alarm result when the result meets the alarm threshold in the preset alarm template. Optionally, the categories of the alarm include: the service call volume is abnormal, the service performance is abnormal, the service success rate is abnormal, and the service failure volume is abnormal.
Description
Technical Field
The invention relates to the technical field of alarm monitoring, in particular to a visual early warning system and a visual early warning method based on a dynamic threshold method.
Background
In the traditional monitoring alarm, index information of different dimensions of each component is monitored by monitoring tools or means such as zabbix, nagios, sql statements, shell scripts and the like, a threshold value is set for a corresponding index, and operation and maintenance personnel are informed to process in forms of short messages or mail alarms and the like. The alarm configuration process comprises index setting, monitoring period setting, monitoring threshold setting, alarm content setting, alarm receivers and the like, and each step is built by depending on the experience of an operation and maintenance engineer in the process.
The traditional monitoring and alarming means is based on single-dimensional index monitoring and alarming, each alarming result is respectively administrative, massive alarming information is easy to generate when the system is abnormal, a large amount of redundant alarming possibly exists in the alarming information, even an alarming storm is formed, great interference is generated on operation and maintenance personnel, and the operation and maintenance work efficiency is reduced.
In an intricate IT system architecture, once an IT system fails, a great number of operation and maintenance engineers are needed, multiple troubleshooting is performed on the failure through a great amount of time, each tiny problem may bring cascading failures, and the time for solving one problem is often many hours. These are intolerable for the current increasingly accelerated business environment.
Disclosure of Invention
In order to solve the problems of defects and shortcomings in the prior art, the invention provides a visual early warning system and a visual early warning method based on a dynamic threshold method. The method aims to provide a service abnormity warning method and aims to solve the problems that the existing warning technology has no dynamic threshold monitoring and warning visual display; the method comprises the following steps: the python script obtains normal service performance to serve as a dynamically updated threshold template, compares a real-time service alarm value with the threshold template, and automatically triggers the mail alarm to visually display an alarm result when the result meets the alarm threshold in the preset alarm template. Optionally, the categories of the alarm include: the service call volume is abnormal, the service performance is abnormal, the service success rate is abnormal, and the service failure volume is abnormal.
In order to improve operation and maintenance efficiency and gather effective alarms, the invention provides an alarm monitoring tool for directly tracing the source and reducing noise of the alarm and SRE operation and maintenance, the scheme is that python development language is used for calling and constructing index data of interfaces of data sources such as draid and ES in real time, the data is analyzed according to specific scenes such as a relation model of business combing service and a system, so as to judge the operation condition of the system, when the formulated alarm rule is met, an image generated by a pre-configured large-screen monitoring model is called, and an email is sent to an alarm receiver in an attachment form after screenshot.
Compared with the traditional short message notification, the method has the advantages that the trend of fault occurrence can be visually seen, the conditions such as equipment performance, application program calling amount and performance time consumption associated with the alarm can be seen, the operation condition of the system can be checked in a multidimensional manner, preliminary fault diagnosis can be conveniently and quickly carried out, the root cause and the location of the fault can be displayed for operation and maintenance personnel at the first time, the fault processing time of the operation and maintenance personnel is shortened, and the operation and maintenance work efficiency is improved.
The invention specifically comprises the following contents:
a visual early warning system based on a dynamic threshold method is characterized by comprising the following steps: the system comprises a data storage module, a load balancing module, a data dynamic threshold module and a visualization module;
the data storage module is used for collecting logs from a plurality of servers and storing the logs in real time;
the load balancing module is used for acquiring log data in the data storage module and distributing each request to different python servers one by one according to a time sequence in a polling mode;
the data dynamic threshold module is used for pulling real-time data from the load balancing module, and when the comparison result meets the alarm threshold value in the preset multi-alarm template, the alarm of the corresponding category is triggered;
the visualization module is used for visually displaying the alarm information.
Further, if a python server is out of order, the load balancing module removes it from the python server pool.
Further, in the data dynamic threshold module,
the conditions that trigger a service performance exception are as follows:
Max(WD-SD)＞service_duration＝[3，n]
the conditions for triggering the exception of the service invocation amount are as follows:
Max(WC-SC)＞service_count＝[3，n]
the conditions for triggering the service success rate exception are as follows:
Max(WS-SS)＞service_success＝[3，n]
the conditions that trigger the service failure amount exception are as follows:
Max(WE-SE)＞service_error＝[3，n]
WD is a current real-time service per minute performance value, SD is a historical simultaneous segment service per minute performance value, service _ duration is a service performance preset threshold template, WC is a current real-time service per minute call quantity value, SC is a historical simultaneous segment service per minute call quantity value, service _ count is a service call quantity preset threshold template, WS is a current real-time service per minute power quantity value, SS is a historical simultaneous segment service per minute power quantity value, service _ success is a service success rate preset threshold template, WE is a current real-time service per minute failure quantity value, SE is a historical simultaneous segment service per minute failure quantity value, service _ error is a service failure quantity preset threshold template, and n is a monitored service performance total number.
Further, the visualization display content of the visualization module at least comprises: service exception current outlier, historical synchronization segment outlier, data trend near one hour current.
Further, the data dynamic threshold module acquires normal service performance by adopting a python script as a threshold template, and analyzes a historical time value and an interval trend value of a monitoring index by adopting a dynamic baseline alarm mode.
Further, the data dynamic threshold module compares the real-time service alarm value with the dynamic baseline threshold template, and when the result meets the alarm threshold value in the preset alarm template, the data dynamic threshold module automatically triggers the visualization module to visually display the alarm result in a mail alarm mode.
And, a visual early warning method based on dynamic threshold method, characterized by comprising the following steps:
step S1: the data storage module collects logs from a plurality of servers and stores the logs in real time;
step S2: the load balancing module acquires log data in the data storage module and distributes each request to different python servers one by one according to the time sequence in a polling mode;
step S3: the data threshold module pulls real-time data from the load balancing module, and when the comparison result meets the alarm threshold in the preset multi-alarm template, the corresponding type of alarm is triggered;
and 4, step 4: the data threshold module calls a visualization module integration interface to perform visualization display according to the alarm threshold value in the preset multi-alarm template when the comparison result meets, and the method comprises the following steps: service exception current outlier, historical synchronization segment outlier, data trend near one hour current.
Further, in step S3:
the conditions that trigger a service performance exception are as follows:
Max(WD-SD)＞service_duration＝[3，n]
the conditions for triggering the exception of the service invocation amount are as follows:
Max(WC-SC)＞service_count＝[3，n]
the conditions for triggering the service success rate exception are as follows:
Max(WS-SS)＞service_success＝[3，n]
the conditions that trigger the service failure amount exception are as follows:
Max(WE-SE)＞service_error＝[3，n]
WD is a current real-time service per minute performance value, SD is a historical simultaneous segment service per minute performance value, service _ duration is a service performance preset threshold template, WC is a current real-time service per minute call quantity value, SC is a historical simultaneous segment service per minute call quantity value, service _ count is a service call quantity preset threshold template, WS is a current real-time service per minute power quantity value, SS is a historical simultaneous segment service per minute power quantity value, service _ success is a service success rate preset threshold template, WE is a current real-time service per minute failure quantity value, SE is a historical simultaneous segment service per minute failure quantity value, service _ error is a service failure quantity preset threshold template, and n is a monitored service performance total number.
And, a visual early warning method based on dynamic threshold method, characterized by: and acquiring normal service performance through a python script to serve as a dynamically updated threshold template, comparing a real-time service alarm value with the threshold template, and automatically triggering the mail alarm to visually display an alarm result when the result meets the alarm threshold in the preset alarm template.
Further, the categories of the alarm include: the service call volume is abnormal, the service performance is abnormal, the service success rate is abnormal, and the service failure volume is abnormal.
Compared with the prior art, the invention and the optimized scheme thereof more flexibly solve the defect of single presentation of the traditional alarm, judge whether the alarm is a true alarm or an instant alarm through visual presentation by monitoring the abnormal service, thus avoiding the waste of manpower and material resources caused by blindly logging in the server. The visual display can be used for visually knowing real-time trends such as current service performance, call volume and success rate through images and judging whether the alarm is real or not at once, so that the management of operation and maintenance personnel is facilitated, unnecessary troubles are reduced, manpower and material resources are saved, and the visual display has great practical value.
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The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a system framework and a workflow diagram according to an embodiment of the present invention;
fig. 2 is an exemplary diagram of an alert mail according to an embodiment of the present invention.
Detailed Description
In order to make the features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail as follows:
as shown in fig. 1, the present embodiment provides a new service abnormality warning system and method, including: a data storage module (es, drive, mysql), a load balancing module (nginx), a data dynamic threshold module (python), a visualization module (kibana, grapana),
the specific working process comprises the following steps:
step 1: the data storage module (es, drive) collects logs from thousands of servers for real-time storage.
Step 2: and the load balancing module (nginx) acquires log data in the data storage module, distributes each request to different python servers one by one according to the time sequence in a polling mode, and can automatically remove the requests if the python servers are down.
And step 3: the data threshold module (python 3.6, awx) pulls real-time data from the load balancing module (nginx), optionally, when the comparison result meets the alarm threshold in the preset multi-alarm template, the alarm of the corresponding category is triggered, specifically including: the conditions that trigger a service performance exception are as follows:
Max(WD-SD)＞service_duration＝[3，n]
the conditions for triggering the exception of the service invocation amount are as follows:
Max(WC-SC)＞service_count＝[3，n]
the conditions for triggering the service success rate exception are as follows:
Max(WS-SS)＞service_success＝[3，n]
the conditions that trigger the service failure amount exception are as follows:
Max(WE-SE)＞service_error＝[3，n]
WD is a current real-time service per minute performance value, SD is a historical simultaneous segment service per minute performance value, service _ duration is a service performance preset threshold template, WC is a current real-time service per minute call quantity value, SC is a historical simultaneous segment service per minute call quantity value, service _ count is a service call quantity preset threshold template, WS is a current real-time service per minute power quantity value, SS is a historical simultaneous segment service per minute power quantity value, service _ success is a service success rate preset threshold template, WE is a current real-time service per minute failure quantity value, SE is a historical simultaneous segment service per minute failure quantity value, service _ error is a service failure quantity preset threshold template, and n is the total number (times) of monitored service performance.
And 4, step 4: the data threshold module (python, awx) calls a visualization module (kibana, grafana) integration interface to perform visualization display according to the alarm threshold value in the preset multi-alarm template when the comparison result meets, and the method comprises the following steps: the current abnormal value of the service abnormality, the abnormal value of the historical synchronization period and the visual Dashboard of the current data trend of one hour help operation and maintenance personnel to judge the alarm influence surface better and improve the alarm efficiency and accuracy of the service abnormality, an example of which is shown in fig. 2.
The key points of the above scheme of the embodiment include:
1. adopting a python script structure: acquiring normal service performance as a threshold template;
2. dynamic baseline alerting: and a dynamic baseline alarm mode is adopted, the historical time value and the interval trend value of the monitoring index are analyzed, the defect of manually setting a fixed threshold in the past is overcome, and accurate judgment is provided for operation and maintenance personnel.
3. E, mail alarm visual display: comparing the real-time service alarm value with the dynamic baseline threshold template, and when the result meets the alarm threshold in the preset alarm template; and automatically triggering the mail alarm to visually display the alarm result.
The present invention is not limited to the above preferred embodiments, and other various forms of visual warning system and method based on dynamic threshold method can be derived by anyone who can use the teaching of the present invention.
Claims (10)
1. A visual early warning system based on a dynamic threshold method is characterized by comprising the following steps: the system comprises a data storage module, a load balancing module, a data dynamic threshold module and a visualization module;
the data storage module is used for collecting logs from a plurality of servers and storing the logs in real time;
the load balancing module is used for acquiring log data in the data storage module and distributing each request to different python servers one by one according to a time sequence in a polling mode;
the data dynamic threshold module is used for pulling real-time data from the load balancing module, and when the comparison result meets the alarm threshold value in the preset multi-alarm template, the alarm of the corresponding category is triggered;
the visualization module is used for visually displaying the alarm information.
2. The visual early warning system based on the dynamic threshold method as claimed in claim 1, wherein: if a python server is out of order, the load balancing module removes the python server from the python server pool.
3. The visual early warning system based on the dynamic threshold method as claimed in claim 1, wherein: in the data dynamic threshold module,
the conditions that trigger a service performance exception are as follows:
Max(WD-SD)＞service_duration＝[3，n]
the conditions for triggering the exception of the service invocation amount are as follows:
Max(WC-SC)＞service_count＝[3，n]
the conditions for triggering the service success rate exception are as follows:
Max(WS-SS)＞service_success＝[3，n]
the conditions that trigger the service failure amount exception are as follows:
Max(WE-SE)＞service_error＝[3，n]
WD is a current real-time service per minute performance value, SD is a historical simultaneous segment service per minute performance value, service _ duration is a service performance preset threshold template, WC is a current real-time service per minute call quantity value, SC is a historical simultaneous segment service per minute call quantity value, service _ count is a service call quantity preset threshold template, WS is a current real-time service per minute power quantity value, SS is a historical simultaneous segment service per minute power quantity value, service _ success is a service success rate preset threshold template, WE is a current real-time service per minute failure quantity value, SE is a historical simultaneous segment service per minute failure quantity value, service _ error is a service failure quantity preset threshold template, and n is a monitored service performance total number.
4. The visual early warning system based on the dynamic threshold method as claimed in claim 1, wherein: the visualization display content of the visualization module at least comprises: service exception current outlier, historical synchronization segment outlier, data trend near one hour current.
5. The visual early warning system based on the dynamic threshold method as claimed in claim 1, wherein: the data dynamic threshold module adopts a python script to acquire normal service performance as a threshold template, and adopts a dynamic baseline alarm mode to analyze historical time values and interval trend values of monitoring indexes.
6. The visual early warning system based on the dynamic threshold method as claimed in claim 5, wherein: and the data dynamic threshold module compares the real-time service alarm value with the dynamic baseline threshold template, and when the result meets the alarm threshold value in the preset alarm template, the data dynamic threshold module automatically triggers the visualization module to visually display the alarm result in a mail alarm mode.
7. A visual early warning method based on a dynamic threshold method is characterized by comprising the following steps:
step S1: the data storage module collects logs from a plurality of servers and stores the logs in real time;
step S2: the load balancing module acquires log data in the data storage module and distributes each request to different python servers one by one according to the time sequence in a polling mode;
step S3: the data threshold module pulls real-time data from the load balancing module, and when the comparison result meets the alarm threshold in the preset multi-alarm template, the corresponding type of alarm is triggered;
and 4, step 4: the data threshold module calls a visualization module integration interface to perform visualization display according to the alarm threshold value in the preset multi-alarm template when the comparison result meets, and the method comprises the following steps: service exception current outlier, historical synchronization segment outlier, data trend near one hour current.
8. The visual early warning system based on the dynamic threshold method as claimed in claim 5, wherein: in step S3:
the conditions that trigger a service performance exception are as follows:
Max(WD-SD)＞service_duration＝[3，n]
the conditions for triggering the exception of the service invocation amount are as follows:
Max(WC-SC)＞service_count＝[3，n]
the conditions for triggering the service success rate exception are as follows:
Max(WS-SS)＞service_success＝[3，n]
the conditions that trigger the service failure amount exception are as follows:
Max(WE-SE)＞service_error＝[3，n]
WD is a current real-time service per minute performance value, SD is a historical simultaneous segment service per minute performance value, service _ duration is a service performance preset threshold template, WC is a current real-time service per minute call quantity value, SC is a historical simultaneous segment service per minute call quantity value, service _ count is a service call quantity preset threshold template, WS is a current real-time service per minute power quantity value, SS is a historical simultaneous segment service per minute power quantity value, service _ success is a service success rate preset threshold template, WE is a current real-time service per minute failure quantity value, SE is a historical simultaneous segment service per minute failure quantity value, service _ error is a service failure quantity preset threshold template, and n is a monitored service performance total number.
9. A visual early warning method based on a dynamic threshold method is characterized in that: and acquiring normal service performance through a python script to serve as a dynamically updated threshold template, comparing a real-time service alarm value with the threshold template, and automatically triggering the mail alarm to visually display an alarm result when the result meets the alarm threshold in the preset alarm template.
10. The visual early warning system based on the dynamic threshold method as claimed in claim 9, wherein: the categories of the alarm include: the service call volume is abnormal, the service performance is abnormal, the service success rate is abnormal, and the service failure volume is abnormal.
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