CN113722187B - Service monitoring system for micro-service architecture - Google Patents
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- G06F11/00—Error detection; Error correction; Monitoring
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Abstract
The invention discloses a service monitoring system facing a micro-service architecture, which comprises a data query module, a data acquisition module, an alarm calculation module and a data storage module, wherein the data acquisition module is used for outlining relevant acquisition configuration information when acquiring micro-service time sequence data, determining the acquired time sequence data according to the configuration information and storing the acquired time sequence data in the data storage module; the data query module is used for receiving a query request and accessing time sequence data stored in the data storage module through an interface according to the query request; the alarm calculation module is used for monitoring the query result returned by the data query module and the hardware running condition; the data storage module is used for providing a unified data return format and a data presentation mode for the data query module during query. The invention is oriented to micro-service, monitors data indexes and hardware running conditions under the micro-service through IP (Internet protocol) adding configuration, and unifies a data acquisition format, a data query format and a data return format.
Description
Technical Field
The invention relates to the field of micro-service monitoring, in particular to a service monitoring system facing a micro-service architecture.
Background
Microservices are an architectural style, a large complex software application consisting of one or more microservices. The micro services in the system can be deployed independently, the micro services are loosely coupled, each micro service only focuses on completing one task and well completing the task, each task represents a small business capability, and the application scale is wide.
In the early stages of internet development, both user and data size were at a small level, and the performance of a single server was sufficient to handle the requests, with services mostly existing in the form of a monolithic architecture, also known as the monolithic era. In this era, application function codes are often concentrated in a program, and development, deployment and operation of the application function codes are relatively simple. However, with the development of the internet, the single architecture can only rely on simple hardware stacking to improve the processing performance, and quickly, the single architecture quickly touches the hardware bottleneck, and the cost of improving the hardware scale of the single architecture can be far higher than the benefit brought by improving the hardware scale of the single architecture. Therefore, various splitting based on a single architecture is indispensable, such as MVC, namely splitting based on a code layer, so that maintainability of system codes is improved; the database read-write separation and database table division are based on the database layer separation; an application cluster is a split with high access to a single application; after a series of evolutions, a micro-service architecture is brought about. Micro services emphasize that services need to be thoroughly componentized and served, a complex service system may be split into multiple simple, independent applets, which together support a complex service, which is the core of the micro service architecture. After the complex services are split, the services are mutually independent, easy to deploy and easy to develop on a large scale. But at the same time, the number of services is greatly increased, the release times of each service is greatly increased, and the requirement for application monitoring is greatly improved. When the single architecture or the service quantity is small, the application monitoring complexity is low, because the service quantity is relatively stable, and only some key business data and logs are monitored. When the micro-service is adopted, each micro-service has a set of independent running environment, and the data and the log of each micro-service need to be monitored. And in the face of frequent release and increase and decrease of micro services, a set of monitoring system is required to be capable of rapidly acquiring micro service monitoring data and logs and rapidly establishing relevant configurations such as charts, alarms and the like.
Disclosure of Invention
The invention provides a service monitoring system facing a micro-service architecture, which aims to solve the problem that the micro-service monitoring system in the prior art is low in monitoring strength due to the fact that the micro-service monitoring system is not provided with a unified data query format and a unified data return format.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the invention discloses a service monitoring system facing a micro-service architecture, which comprises a data query module, a data acquisition module, an alarm calculation module and a data storage module:
the data acquisition module is used for abstracting related acquisition configuration information when acquiring the microservice time sequence data, determining the acquired time sequence data according to the configuration information and storing the acquired time sequence data in the data storage module;
the data query module is used for receiving a query request, wherein the query request is not limited to a query data source, an index name, a filtering condition, a query mode, a start time and an end time, and accessing time sequence data stored in the data storage module through an interface according to the query request;
the alarm calculation module is used for monitoring the query result returned by the data query module and the hardware running condition;
the data storage module is used for providing a unified data return format and a data presentation mode for the data query module during query.
Preferably, the data acquisition module is further configured to determine a unified data acquisition format, where the unified data acquisition format includes a data acquisition mode, a type, an IP address, a port number, a path, and an array.
Preferably, the data query module comprises a combined query unit and an aggregation query unit, wherein the combined query unit is used for constructing simple query on the query object, and the aggregation query unit is used for performing secondary operation on the query result returned by the data query module.
Preferably, the alarm calculation module queries one or more values from the data query module, compares the values with an alarm threshold, and triggers whether the value of an alarm rule is larger than, smaller than, equal to, larger than or equal to or smaller than a set threshold after the alarm threshold is met, and when multi-condition combined alarm is performed, each alarm rule is judged, meanwhile, judgment of the rule and/or each rule is needed, and the alarm is triggered after the condition is met.
Preferably, the data acquisition module includes a mode determining unit, an intermediate address, an access address, and a data conversion unit, including: the mode determining unit determines a data acquisition mode, determines the access address during data acquisition according to the data acquisition mode, sends the acquired time sequence data to the intermediate address, and transforms the acquired time sequence data into a type according to a transformation rule at the data transformation unit.
Preferably, the data storage module comprises an index name, a data object and an array, wherein the data object is composed of key value pairs and is used for representing the dimension of time sequence data;
the array is used for forming a one-to-one correspondence with the time sequence data points.
The service monitoring method for the micro-service architecture comprises the steps that the data acquisition module transmits acquired micro-service time sequence data to the data storage module, the data storage module identifies, analyzes and processes the acquired micro-service time sequence data and transmits the processed data to the data query module, the data query module transmits abnormal time sequence data to the alarm calculation module when identifying abnormal micro-service, and the alarm calculation module is connected with the data storage module and monitors the running condition of hardware.
The invention has the following beneficial effects:
the invention is oriented to micro-service, monitors the data index and the hardware running condition under the micro-service through IP (Internet protocol) adding configuration, and provides a unified query format for the system due to the differences between the query method and the implementation details of each data source, thereby shielding the differences of each data source at the bottom layer for the upper layer application. The system for collecting the micro-service monitoring data collects data in an active pulling mode through http, provides a unified data return format, is convenient for processing of other subsystems, is equivalent to the function of an agent, and is convenient for analysis of back-end application. The real-time performance and the data effectiveness of the monitoring are improved, the multi-dimensional index monitoring is realized, the micro-service performance overall view is comprehensively displayed, the alarm calculation is carried out by adopting the combined inquiry, the aggregate inquiry and the expert inquiry modes, and the alarm threshold real-time monitoring is set.
Drawings
FIG. 1 is a schematic diagram of a service monitoring system facing to a micro-service architecture according to the present invention;
FIG. 2 is a query flow chart of a service monitoring system for a micro-service architecture according to the present invention;
fig. 3 is a simple query flow chart of a service monitoring system for micro-service architecture according to the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
The terms "first," "second," and the like in the claims and the description of the present application are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, and it should be understood that the terms so used may be interchanged, if appropriate, merely to describe the manner in which objects of the same nature are distinguished in the embodiments of the present application when described, and furthermore, the terms "comprise" and "have" and any variations thereof are intended to cover a non-exclusive inclusion such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, and the terms used in the specification of this application are for the purpose of describing particular embodiments only and are not intended to be limiting of this application.
Example 1
As shown in fig. 1-3, a service monitoring system facing to a micro-service architecture includes a data query module, a data acquisition module, an alarm calculation module and a data storage module:
the data acquisition module is used for abstracting related acquisition configuration information when acquiring the microservice time sequence data, determining the acquired time sequence data according to the configuration information and storing the acquired time sequence data in the data storage module;
the data query module is used for receiving a query request, wherein the query request is not limited to a query data source, an index name, a filtering condition, a query mode, a start time and an end time, and accessing time sequence data stored in the data storage module through an interface according to the query request;
the alarm calculation module is used for monitoring the query result returned by the data query module and the hardware running condition;
the data storage module is used for providing a unified data return format and a data presentation mode for the data query module during query.
In embodiment 1, the data formats are unified, and as the name suggests, all the returned data formats in the same item are the same, so that the front end (iOS Android, web) can operate the data more easily, the front end needs to take the state information of the back end to return to the front end to respond to the processing, such as communication exception, operation success, storage failure and the like-! In general, the unified return data format is not fixed, so long as the status of the data to be returned and the specific data to be returned can be described clearly. But will generally contain status codes, return messages, data, and in this embodiment a unified data return format is provided.
Specifically, the unified data query and alarm module, the query format is generally represented by json, monitors the data index of the micro-service, monitors the index of the machine and the micro-service itself, such as the order micro-service, the business index, how many orders are in a hour, typically write mysql, expose the data through the interfaces, we access the data through the interfaces, for example, query how many orders are in a hour, query the data interface, time series data, how many points are counted, which machine is in, the query is not limited to the order, any data can be queried, if what data is to be queried, the next point is buried in the system first, and the like, the system exposes the data needed by the user, and then accesses the data through the interfaces
Mainly the following fields:
● start-start time: the starting time of the data query is represented by unix time stamp, the unit is seconds, and the data type is long type;
● end-end time: the end time of the data query is represented, and the format is the same as the start time;
● datasource-data source: a data source for indicating that the query requires a query;
● metric-index name: the string name of the unique identification data, the format string. This field has different meanings in different data sources. Such as in a promethaus data source, a time series name; dataSource is represented in Draid; in the elastic search data source, an index name is represented;
● tags-filtration conditions: representing a series of data filtering conditions according to dimensions, and forming an array. Each filter term consists of three fields: filtering mode, dimension name and filtering value. Because the data filtering modes of different systems are different, the patent divides the data filtering into two modes: fuzzy filtering and precise filtering.
● aggs-polymerization method: aggregation refers to performing secondary operations on raw data, such as summing over a minute. The patent designs a set of multi-layer aggregation inquiry, and supports 11 aggregation modes of summation, averaging, difference value calculation, speed calculation, number calculation, percentage calculation, maximum calculation, minimum calculation, first time, last time and null value filling. The field format is an array, and can be composed of a plurality of aggregation modes, and the aggregation modes are sequentially aggregated by the sequence in the array.
● groupby-packet field: and the array represents the grouping basis of the data in aggregation. E.g. fill in ip, then all data with the same ip address will be assigned to the same group, followed by an aggregation operation.
● mode-mode: the string enumeration value may be null, normal, expert, sql; when the field is expert, indicating that the query is expert mode, selecting the query condition in the metrics field; when the field is SQL, it indicates that the SQL field will be used for this query; in other cases, the several fields described above will be used for the query.
● metrics-combined query: array format in which each object represents a simple query
■ datasource, data source, and datasource of the same layer
■ Metric-index name, metric of the same outer layer
■ Tags-filtration conditions, same as outer layer of Tags
■ Method of polymerizing aggs and aggs of the same outer layer
■ offset-time, string, representing the amount of time that the query needs to be offset, consists of a number plus string, e.g. "1d" representing the data one day before the query
■ name-name, the query object may be uniquely identified using an alphanumeric.
● The expr-expression: and supporting simple four-rule operation and comparison expression, and performing calculation after inquiring data by using a simple inquiry object in the metrcis.
● SQL query: the character string, the field requirement is an SQL statement, because the guide data source supports SQL queries.
The format of the query data is described briefly above, for example, the query data flow will follow some alarm conditions, the query data is performed according to the query data, for example, the query data is queried for an order quantity of one hour, if the query data is less than 1000, an alarm mechanism is triggered, an alarm is performed, and a little delay is caused in real time during the alarm; time sequence data (similar to mysql table) are provided with names, class data are provided with time stamps, and dimension concepts such as user of a certain machine to be queried and time stamp limit are provided, so that the time sequence data is a simple query, i want to query the average rate of use of the machine in each hour, use aggregation query, have 60 minutes in one hour, set 60 points, calculate the 60 points to be averaged again, turn into one point, return, and the multiple points become smaller, namely the aggregation query; two different time sequence data do an operation, namely expert inquiry; the format of the alarm is added with the relevant alarm field on the format of the query, which is as follows:
● Compparison_mode-alert mode: the method is divided into absolute value alarms and floating value alarms. Absolute value alarms refer to comparing a queried value to an absolute value number. The float value alarm is to calculate the queried data and the historical data to obtain a float percentage, and then compare the float percentage with an absolute value number.
● duration-time period of inquiry, character string, this field represents the time period of each need inquiry of this alarm, format is number plus character string representing time
● threshold-threshold, threshold for final comparison of alarms
● clase-alert aggregation condition, string, refers to how the alert might ultimately query 1 or more values from the data source, comparing those values to a threshold. The following polymerization scheme is supported
■ Average value, alarm after threshold value is met after numerical value column is averaged
■ At least once, at least one point is in alarm after meeting alarm conditions
■ Always, all points are required to meet the alarm condition and then alarm
■ Maximum value, alarm after the maximum value is required to meet alarm condition
■ Minimum value, alarm after minimum value meets alarm condition
■ Sum, alarm after meeting alarm condition is required to sum
● Expression-Expression compared to threshold, support >, <, =, < =
● start-alert delay: a string, representing a time period, is actually sent to the user after the alert continues to satisfy the time period specified in the field.
● max-alarm duration: the alarm duration, if exceeded, is still in alarm, will temporarily stop the alarm. When the alarm condition is not met, the time will be reset.
● period-run expression: character string, format is crontab expression, when the expression is met, the alarm can be operated
● users: user of alarm reminding
Identification_method: alarm sending mode
Specifically, because the data formats returned by the data sources are different and even have large differences, in order to facilitate analysis by the back-end application, a unified data return format is designed herein, and the format is defined as follows:
metric, character string, representing index name
Tags, objects, consisting of k-v key-value pairs, are used to represent the dimensions of the time-series data. For example, tags: { instance: "192.168.2.2", host: "vm-1" }, which illustrates that the time series data has two dimensions, named instance and host, respectively, corresponding values 192.168.2.2 and vm-1, respectively.
Values, arrays, represent time series data. Each time sequence data point is represented by an array, the first element of the array is long, and the first element represents a linux time stamp millisecond representation; the second element of the array is a double type, representing the value at that time. For example, "values": [ [1613612640000,0.88], [1613612700000,0.47] ] means that there are a total of two time series data points, time instants 1613612640000 and 1613612700000, and values of 0.88 and 0.47.
Specifically, a data acquisition format is defined as follows, and is related acquisition configuration information of an outline when acquiring microservice time sequence data:
mode, string, representing data acquisition mode, divided into a general micro-service mode and a k8s mode. During data acquisition, the ip address and port of the destination end need to be known. In a common mode, the acquisition service acquires the machine IP of the micro service from the cmdb; in k8s mode, relevant data will be obtained from the API of the k8s cluster.
Type, string, value http|https, indicating the type of http used.
IP, character string, which indicates IP address, is divided by comma. When this entry is not empty, the mode field will be overridden and the ip address will be obtained directly from this field.
Port, integer, representing Port
Path, represents the http interface path of access. This field, together with ip and port, forms a complete URL for access by the acquisition service.
And (3) data_sources, and if the item is not empty, sending the data to the kafka reader and topic corresponding to the item after the acquisition service acquires the data. For example, data_sources [ { data_source: 'container_event', cookie: '192.168.2.2:9200', topic: 'op_container_event' }, means that the collected data will eventually be delivered to the 'op_container_event' topic with a cookie address of '192.168.2.2:9200', and not to the default kafka cluster.
A drive_rule, an array, a conversion rule for converting collected data into drive data, the main field is name, metric, type, data _source. For example, the data name is "cpu", the metric is "cpu_count", the type is "gauge", the data_source is "container_event", and the data named cpu in the acquired data is changed to cpu_count, the type is gauge, and if the type is count, the collector calculates the difference value from the last acquisition and then sends the difference value.
The above embodiments are merely illustrative embodiments of the present invention, but the technical features of the present invention are not limited thereto, and any changes or modifications made by those skilled in the art within the scope of the present invention are included in the scope of the present invention.
Claims (3)
1. The service monitoring system facing the micro-service architecture is characterized by comprising a data query module, a data acquisition module, an alarm calculation module and a data storage module:
the data acquisition module is used for abstracting related acquisition configuration information when acquiring the microservice time sequence data, determining the acquired time sequence data according to the configuration information and storing the acquired time sequence data in the data storage module;
the data query module is used for receiving a query request, wherein the query request is not limited to a query data source, an index name, a filtering condition, a query mode, a start time and an end time, and accessing time sequence data stored in the data storage module through an interface according to the query request;
the alarm calculation module is used for monitoring the query result returned by the data query module and the hardware running condition;
the data storage module is used for providing a unified data return format and a data presentation mode for the data query module during query; the data acquisition module is also used for determining a unified data acquisition format, wherein the unified data acquisition format comprises a data acquisition mode, a type, an IP address, a port number, a path and an array; the data query module comprises a combined query unit and an aggregation query unit, wherein the combined query unit is used for constructing simple query on a query object, and the aggregation query unit is used for performing secondary operation on a query result returned by the data query module; the alarm calculation module inquires one or more values from the data inquiry module, compares the values with an alarm threshold, and triggers whether the value of an alarm rule is larger than, smaller than, equal to, larger than or equal to or smaller than a set threshold after the alarm threshold is met; the data acquisition module comprises a mode determining unit, an intermediate address, an access address and a data conversion unit, and comprises: the mode determining unit determines a data acquisition mode, determines the access address during data acquisition according to the data acquisition mode, sends the acquired time sequence data to the intermediate address, and transforms the acquired time sequence data into a type according to a transformation rule at the data transformation unit.
2. The service monitoring system of claim 1, wherein the data storage module comprises an index name, a data object, and an array, the data object is composed of key-value pairs and is used for representing the dimension of time sequence data;
the array is used for forming a one-to-one correspondence with the time sequence data points.
3. The service monitoring method facing to the micro-service architecture is characterized by being applied to the service monitoring system facing to the micro-service architecture, which is characterized in that any one of the claims 1-2, the data acquisition module is used for transmitting acquired micro-service time sequence data to the data storage module, the data storage module is used for identifying, analyzing and processing the acquired micro-service time sequence data and transmitting the processed data to the data query module, the data query module is used for transmitting abnormal time sequence data to the alarm calculation module when identifying abnormal micro-service, and the alarm calculation module is connected with the data storage module and is used for monitoring the running condition of hardware.
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