CN113722187A - Service monitoring system for micro-service architecture - Google Patents
Service monitoring system for micro-service architecture Download PDFInfo
- Publication number
- CN113722187A CN113722187A CN202111073688.2A CN202111073688A CN113722187A CN 113722187 A CN113722187 A CN 113722187A CN 202111073688 A CN202111073688 A CN 202111073688A CN 113722187 A CN113722187 A CN 113722187A
- Authority
- CN
- China
- Prior art keywords
- data
- query
- module
- micro
- service
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 35
- 238000013500 data storage Methods 0.000 claims abstract description 25
- 238000004364 calculation method Methods 0.000 claims abstract description 16
- 238000013480 data collection Methods 0.000 claims abstract description 6
- 238000001914 filtration Methods 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 230000001960 triggered effect Effects 0.000 claims description 3
- 238000003491 array Methods 0.000 claims 1
- 238000012545 processing Methods 0.000 description 9
- 230000002776 aggregation Effects 0.000 description 8
- 238000004220 aggregation Methods 0.000 description 8
- 230000014509 gene expression Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000006116 polymerization reaction Methods 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Quality & Reliability (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computer And Data Communications (AREA)
Abstract
The invention discloses a micro-service architecture-oriented service monitoring system, 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 collecting configuration information related to outline during micro-service time sequence data collection, determining the collected time sequence data according to the configuration information and storing the collected time sequence data in the data storage module; the data query module is used for receiving a query request and accessing the 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 state; the data storage module is used for providing a uniform data return format and a data presentation mode for the data query module during query. The invention is oriented to the micro-service, monitors the data index and the hardware running condition under the micro-service through IP and configuration, and unifies the data acquisition format, the data query format and the data return format.
Description
Technical Field
The invention relates to the field of micro-service monitoring, in particular to a micro-service architecture-oriented service monitoring system.
Background
A microservice is an architectural style, with a large complex software application consisting of one or more microservices. Each micro service in the system can be independently deployed, the micro services are loosely coupled, each micro service only focuses on completing one task and well completes the task, and in all cases, each task represents a small business capacity and has a wide application scale.
In the early development of the internet, the scale of users and data is at a small level, the performance of a single server is enough to cope with requests, and services mostly exist in the form of a single architecture, which is also called a single era. In this era, application function codes are often concentrated in one program, and the development, deployment, operation and maintenance of the application function codes are relatively simple. However, with the development of the internet, the single architecture can only improve the processing performance by means of pure hardware stacking, and quickly reaches the hardware bottleneck of the single architecture, and the cost of improving the hardware scale or the hardware scale can not be much higher than the benefit brought by the single architecture. Therefore, various splits based on a single architecture are necessary, for example, MVC is split based on a code level, and maintainability of system codes is improved; the database is read and written separately, and is divided into a database and a list, and the database is split based on the database layer; the application cluster is split for a single application with high access; after a series of evolutions, what comes up is the architecture of a microservice. The microservice emphasizes that the business needs to be completely modularized and serviced, and a complex business system can be split into a plurality of simple and independent applets, and the applets support a complex business together, which is the core of a microservice architecture. After the complex services are split, the services are mutually independent and easy to deploy and develop by a large-scale team. But simultaneously, the number of services is greatly increased, the issuing times of each service is greatly increased, and the requirement on application monitoring is greatly improved. When the single architecture or the number of the services is small, the application monitoring complexity is low, because the number of the services is relatively stable, only some key service data and logs need to be monitored. After the microservice is adopted, each microservice has a set of independent operating environment, and data and logs of each microservice need to be monitored. And when the micro-service is frequently released, increased and decreased, a set of monitoring system is needed to quickly acquire micro-service monitoring data and logs and quickly establish relevant configurations such as charts, alarms and the like.
Disclosure of Invention
The invention provides a micro-service architecture-oriented service monitoring system, and aims to solve the problem that the micro-service monitoring system in the prior art is low in monitoring strength because a unified data query format and a unified data return format are not available at all.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a micro-service architecture-oriented service monitoring system, which comprises a data query module, a data acquisition module, an alarm calculation module and a data storage module, wherein the data query module comprises a data acquisition module, a data storage module and a data processing module, and the data processing module comprises a data processing module, a data processing module and a data processing module, wherein the data processing module comprises:
the data acquisition module is used for collecting configuration information related to outline during collection of micro-service time sequence data, determining the collected time sequence data according to the configuration information and storing the collected 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 starting time and an ending time, and accesses the 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 state;
the data storage module is used for providing a uniform 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 includes a combined query unit and an aggregated query unit, the combined query unit is configured to construct a simple query for the query object, and the aggregated query unit is configured to perform a 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 if the value triggering the alarm rule after meeting the alarm threshold is greater than, less than, equal to, greater than or equal to, or less than or equal to the set threshold, when multiple conditions are combined for alarm, the judgment of the comparison, or the non-comparison between the rules is needed while each alarm rule is judged, and the alarm is triggered when the conditions are met.
Preferably, the data acquisition module includes a mode determination unit, an intermediate address, an access address, and a data conversion unit, and includes: 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 converts the type of the acquired time sequence data according to a conversion rule in the data conversion 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 used for representing the dimension of the time sequence data;
the array is used for forming a one-to-one corresponding relation with the time sequence data points.
A micro-service architecture-oriented service monitoring method is characterized in that a data acquisition module transmits acquired micro-service time sequence data to a data storage module, the data storage module identifies, analyzes and processes the acquired micro-service time sequence data and transmits the processed data to a data query module, the data query module transmits abnormal time sequence data to an alarm calculation module when the micro-service is identified to be abnormal, 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 the microservice, monitors the data index and the hardware running state under the microservice through IP and configuration, and provides a uniform query format for the system due to the difference of the query method and the implementation details of each data source, thereby shielding the difference of each data source at the bottom layer for the upper layer application. The system for acquiring the micro-service monitoring data acquires data in an active pulling mode through http, provides a uniform data return format, is convenient for processing of other subsystems, is equivalent to the function of an agent, and is convenient for back-end application analysis. The real-time performance and data effectiveness of monitoring are improved, multi-dimensional index monitoring is achieved, overall micro-service performance is displayed comprehensively, alarm calculation is carried out through a combined query mode, an aggregate query mode and an expert query mode, and an alarm threshold value is set for real-time monitoring.
Drawings
FIG. 1 is a schematic structural diagram of a micro-service architecture oriented service monitoring system according to the present invention;
FIG. 2 is a query flow chart of a micro-service architecture oriented service monitoring system according to the present invention;
fig. 3 is a simple query flowchart of a micro-service architecture oriented service monitoring system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work based on the embodiments of the present invention belong to the protection scope of the present invention.
The terms "first," "second," and the like in the claims and in the description of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order, it being understood that the terms so used are interchangeable under appropriate circumstances and are merely used to describe a distinguishing manner between similar elements in the embodiments of the present application and that the terms "comprising" and "having" 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 herein in the specification of the present application are for the purpose of describing particular embodiments only and are not intended to limit the present application.
Example 1
As shown in fig. 1 to 3, a service monitoring system facing micro-service architecture includes a data query module, a data collection module, an alarm calculation module, and a data storage module:
the data acquisition module is used for collecting configuration information related to outline during collection of micro-service time sequence data, determining the collected time sequence data according to the configuration information and storing the collected 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 starting time and an ending time, and accesses the 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 state;
the data storage module is used for providing a uniform data return format and a data presentation mode for the data query module during query.
In embodiment 1, the formats of returned data are unified, and as the name suggests, all returned data in the same project have the same format, so that the front end (iOS Android, Web) can operate data more easily, and the foreground needs to take the status information of the background and return to the foreground to perform response processing, such as communication exception, operation success, storage failure, etc.! In general, the unified return data format does not have a fixed format, as long as the status of the returned data and the specific data to be returned can be described clearly. But generally contains status codes, return messages, and data, and in this embodiment, a unified data return format is provided.
Specifically, a unified data query and alarm module, the query format is generally expressed by json, data indexes of monitoring micro-services, indexes of monitoring machines and micro-services, such as order micro-clothes, business indexes, and orders of the micro-services per se, generally, mysql is written in, the data are exposed through interfaces, and the data are accessed through the interfaces, for example, the orders of the micro-services per hour are queried, a data interface is queried, time sequence data, orders of the micro-services are counted at several points, the machine comes in, the query is not limited to the order, any data can be queried, if what data is to be queried, the data needed by the user is firstly buried in the system, and the system exposes the data needed by the user, and then the data are accessed through the interfaces
There are mainly the following fields:
● start-start time: the starting time of the data query is represented by unix time stamp, the unit is second, and the data type is long type;
● end-end time: representing the end time, the format and the starting time of the data query;
● datasource-data sources: indicating the data source that the query requires;
● metric — index name: a string name, a format string, that uniquely identifies the data. This field has different meaning in different data sources. Such as in the Prometheus data source, the time series name; the data source is represented in the drive; in the Elasticsearch data source, an index name is represented;
● tags-filtration conditions: representing a series of data filtering conditions according to the dimension, and the format is array. Each filter term consists of three fields: filtering mode, dimension name, filtering value. Because the data filtering modes of different systems are different, the patent roughly divides the data filtering into two modes: fuzzy filtering and accurate filtering.
● aggs-polymerization method: aggregation refers to subjecting the raw data to secondary operations, such as summing within one minute. The patent designs a set of multi-layer aggregation query, and supports 11 aggregation modes of summing, averaging, difference solving, rate solving, number solving, percentage solving, maximum solving, minimum solving, first time, last time and null 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.
● group-grouping field: and the array represents grouping basis of the data during aggregation. E.g., filling in an ip, then all data with the same ip address will be assigned to the same group, followed by an aggregation operation.
● mode-mode: string enumeration values, which may be null, normal, expert, sql; when the field is expert, the query is shown as an expert mode, and the query condition in the metrics field is selected to be used; when the field is SQL, the query is shown to use the SQL field; in other cases, the query will be performed using several of the fields described above.
● metrics — combinatorial query: array format, where each object represents a simple query
■ datasource, data source, datasource with outer layer
■ metric-index name, metric with outer layer
■ tags-filtration conditions with outer-layer tags
■ aggs polymerization method, aggs with outer layer
■ offset-offset time, string, indicating the amount of time that an offset is required for a query, is made up of a number plus a string, e.g., "1 d" indicates that the query is for data one day ago
■ name-the query object can be uniquely identified using alphanumeric characters.
● expr-expression: and simple four arithmetic operation and comparison expressions are supported, and calculation is carried out after data are inquired by using a simple query object in metrcis.
● SQL query: and the character string requires an SQL statement, and the Druid data source supports SQL query.
The above simply describes the format of the query data, for example, when the query data stream is followed by some alarm conditions, the query data is performed according to the query data, for example, the order quantity of one hour is queried, if the query data is less than 1000, an alarm mechanism is triggered to alarm, and an alarm is performed in real time with a little delay; time sequence data (similar to mysql tables) are named, class data are all provided with time stamps, dimension concepts are provided, for example, the use of a certain machine is inquired, the time stamps are limited, so that simple inquiry is realized, when a user wants to check the hourly use average rate of the machine, aggregate inquiry is used, 60 points are set when the user has 60 minutes in one hour, the 60 points are calculated and then averaged to become one point, and then the point is returned, and the point is reduced to be the aggregate inquiry; performing one operation on two different time sequence data, namely expert query; the format of the alarm adds an alarm related field to the format of the query, which is as follows:
● compare _ mode-alert mode: there are absolute value alarms and floating value alarms. Absolute value alarm refers to comparing the queried value with the absolute value number. The floating value alarm is to calculate the inquired data and the historical data to obtain a floating percentage, and then compare the floating percentage with an absolute value number.
● duration-query time period, string, which indicates the time period that the alarm needs to be queried each time, and string in the form of a number plus a time
● threshold-threshold, threshold for alarm final comparison
● clause-alarm aggregation condition, string, refers to how the alarm may eventually query 1 or more values from the data source, and compare those values to a threshold. Support the following polymerization modes
■ average value, alarm when the value column meets the threshold value after averaging
■ at least once, and when there is at least one point satisfying the alarm condition
■ always, all the points are required to meet the alarm condition and then alarm
■ maximum value, alarm after maximum value meets alarm condition
■ minimum value, alarm after minimum value meeting alarm condition
■ sum, alarm when the sum is required to satisfy the alarm condition
● Expression-Expression for comparison with a threshold value, support >, <, >, < ═
● start-alarm delay: a string, indicating a time period, which will not actually be sent to the user until the alarm continues to meet the time period specified by the field.
● max-alarm duration: the alarm duration, if the alarm is still being raised beyond this time, the alarm will be temporarily stopped. When the alarm condition is not met, the time is reset.
● period-running expression: the character string is in crontab expression, and the alarm is operated when the character string conforms to the expression
● users: user with alarm reminding function
notification _ method: alarm sending mode
Specifically, because the data formats returned by the data sources are different, even have a large difference, in order to facilitate the analysis of the backend application, a uniform data return format is designed herein, and the format is defined as follows:
metric, string, indicating index name
Tags, objects, consisting of k-v key-value pairs, for representing dimensions of time-series data. For example, tag: { instance: "192.168.2.2", host: "vm-1" }, which illustrates that the time series data has two dimensions, names instance and host, respectively, and corresponding values 192.168.2.2 and vm-1, respectively.
Values, array, representing time series data. Each time-series data point is represented by an array, the first element of the array is long, and linux timestamp millisecond representation is represented; the second element of the array is of double type, representing the value at that time. For example, "values" [ [1613612640000,0.88], [1613612700000,0.47] ], which means that there are a total of two time series data points, the time instants being 1613612640000 and 1613612700000 and the values being 0.88 and 0.47.
Specifically, a data acquisition format is a collection configuration information related to outline when acquiring microservice time series data, and the data acquisition format is defined as follows:
mode, string, representing data acquisition mode, is divided into a general micro service mode and a k8s mode. When data is collected, the ip address and the port of the destination end need to be known. In a common mode, acquiring service acquires the machine IP of the micro service from the cmdb; in the k8s mode, relevant data will be obtained from the API of the k8s cluster.
Type, string, valued http | https, indicates the type of http used.
IP, string, representing IP address, is split with commas. When the entry is not empty, the mode field will be overwritten and the ip address will be retrieved directly from this field.
Port, integer, representing a port
Path, http interface path representing access. This field, together with ip and port, constitutes a complete URL for access by the acquisition service.
And 6, data _ sources, wherein if the item is not empty, the acquisition service acquires the data and then sends the data to the kafka browser and the topoic corresponding to the item. For example, data _ sources [ { data _ source: 'container _ event', brooker: '192.168.2.2:9200', topoic: 'op _ container _ event' } ], indicates that the collected data will eventually be posted to 'op _ container _ event' topoc with brooker address '192.168.2.2:9200', and not to the default kafka cluster.
And the array provides a conversion rule for converting the collected data into the drive data, and the main fields comprise name, metric, type and data _ source. For example, the data name is 'cpu', the metric is 'cpu _ count', the type is 'gauge', the data _ source is 'container _ event', the data named cpu in the collected data is renamed to be 'cpu _ count', the type is gauge, if the data is of the count type, the collector calculates the difference value with the last collection and then sends the difference value.
The above description is only an embodiment of the present invention, but the technical features of the present invention are not limited thereto, and any changes or modifications within the technical field of the present invention by those skilled in the art are covered by the claims of the present invention.
Claims (7)
1. A micro-service architecture-oriented service monitoring system 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 collecting configuration information related to outline during collection of micro-service time sequence data, determining the collected time sequence data according to the configuration information and storing the collected 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 starting time and an ending time, and accesses the 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 state;
the data storage module is used for providing a uniform data return format and a data presentation mode for the data query module during query.
2. The microservice-oriented architecture service monitoring system of claim 1, wherein the data collection module is further configured to determine a unified data collection format, wherein the unified data collection format comprises a data collection mode, a type, an IP address, a port number, a path, and an array.
3. The micro-service architecture-oriented service monitoring system according to claim 1, wherein the data query module includes a combined query unit and an aggregated query unit, the combined query unit is configured to construct a simple query on a query object, and the aggregated query unit is configured to perform a secondary operation on a query result returned by the data query module.
4. The micro-service architecture-oriented service monitoring system according to claim 1, wherein the alarm calculation module queries one or more values from the data query module, compares the values with an alarm threshold, and if the value triggering the alarm rule is greater than, less than, equal to, greater than or equal to, or less than or equal to the set threshold after the alarm threshold is met, when multiple conditions are jointly alarmed, each alarm rule is judged, and meanwhile, the judgment of the mutual and/or negation of each rule is also required, and if the conditions are met, the alarm is triggered.
5. The micro-service architecture oriented service monitoring system of claim 2, wherein the data acquisition module comprises a mode determination 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 converts the type of the acquired time sequence data according to a conversion rule in the data conversion unit.
6. The micro-service architecture oriented service monitoring system of claim 1, wherein the data storage module comprises index names, data objects and arrays, the data objects are composed of key value pairs and used for representing the dimension of time series data;
the array is used for forming a one-to-one corresponding relation with the time sequence data points.
7. A micro-service architecture-oriented service monitoring method is characterized in that a data acquisition module transmits acquired micro-service time sequence data to a data storage module, the data storage module identifies, analyzes and processes the acquired micro-service time sequence data and transmits the processed data to a data query module, the data query module transmits abnormal time sequence data to an alarm calculation module when the micro-service is identified to be abnormal, and the alarm calculation module is connected with the data storage module and monitors the running condition of hardware.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111073688.2A CN113722187B (en) | 2021-09-14 | 2021-09-14 | Service monitoring system for micro-service architecture |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111073688.2A CN113722187B (en) | 2021-09-14 | 2021-09-14 | Service monitoring system for micro-service architecture |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113722187A true CN113722187A (en) | 2021-11-30 |
| CN113722187B CN113722187B (en) | 2024-03-26 |
Family
ID=78683763
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111073688.2A Active CN113722187B (en) | 2021-09-14 | 2021-09-14 | Service monitoring system for micro-service architecture |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113722187B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114860510A (en) * | 2022-07-08 | 2022-08-05 | 飞狐信息技术（天津）有限公司 | Data monitoring method and system of micro-service system |
| CN117251173A (en) * | 2023-11-15 | 2023-12-19 | 深圳万物安全科技有限公司 | Method for configuring micro-service items, device and medium for configuring micro-service items |
| WO2024174700A1 (en) * | 2023-02-24 | 2024-08-29 | 天翼云科技有限公司 | Method and apparatus for calculating component value of alarm information, and electronic device and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109474499A (en) * | 2018-12-29 | 2019-03-15 | 杭州趣链科技有限公司 | A kind of solution of distributed block chain monitoring system |
| CN109474685A (en) * | 2018-11-16 | 2019-03-15 | 中国银行股份有限公司 | Service monitoring method and system under a kind of framework based on micro services |
| CN109660407A (en) * | 2019-01-18 | 2019-04-19 | 鑫涌算力信息科技（上海）有限公司 | Distributed system monitoring system and method |
| KR20190047284A (en) * | 2017-10-27 | 2019-05-08 | (주)엔키아 | Metering and monitoring data integration management apparatus and method for cloud service |
| CN110851396A (en) * | 2019-11-07 | 2020-02-28 | 北京集奥聚合科技有限公司 | Modeling platform-based micro-service architecture unified log design method |
| US20200175010A1 (en) * | 2018-11-29 | 2020-06-04 | Sap Se | Distributed queries on legacy systems and micro-services |
| CN112434063A (en) * | 2020-11-03 | 2021-03-02 | 中国南方电网有限责任公司 | Monitoring data processing method based on time sequence database |
-
2021
- 2021-09-14 CN CN202111073688.2A patent/CN113722187B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190047284A (en) * | 2017-10-27 | 2019-05-08 | (주)엔키아 | Metering and monitoring data integration management apparatus and method for cloud service |
| CN109474685A (en) * | 2018-11-16 | 2019-03-15 | 中国银行股份有限公司 | Service monitoring method and system under a kind of framework based on micro services |
| US20200175010A1 (en) * | 2018-11-29 | 2020-06-04 | Sap Se | Distributed queries on legacy systems and micro-services |
| CN109474499A (en) * | 2018-12-29 | 2019-03-15 | 杭州趣链科技有限公司 | A kind of solution of distributed block chain monitoring system |
| CN109660407A (en) * | 2019-01-18 | 2019-04-19 | 鑫涌算力信息科技（上海）有限公司 | Distributed system monitoring system and method |
| CN110851396A (en) * | 2019-11-07 | 2020-02-28 | 北京集奥聚合科技有限公司 | Modeling platform-based micro-service architecture unified log design method |
| CN112434063A (en) * | 2020-11-03 | 2021-03-02 | 中国南方电网有限责任公司 | Monitoring data processing method based on time sequence database |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114860510A (en) * | 2022-07-08 | 2022-08-05 | 飞狐信息技术（天津）有限公司 | Data monitoring method and system of micro-service system |
| CN114860510B (en) * | 2022-07-08 | 2022-12-02 | 飞狐信息技术（天津）有限公司 | Data monitoring method and system of micro-service system |
| WO2024174700A1 (en) * | 2023-02-24 | 2024-08-29 | 天翼云科技有限公司 | Method and apparatus for calculating component value of alarm information, and electronic device and storage medium |
| CN117251173A (en) * | 2023-11-15 | 2023-12-19 | 深圳万物安全科技有限公司 | Method for configuring micro-service items, device and medium for configuring micro-service items |
| CN117251173B (en) * | 2023-11-15 | 2024-03-08 | 深圳万物安全科技有限公司 | Micro-service item configuration method, micro-service item configuration device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113722187B (en) | 2024-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113722187A (en) | Service monitoring system for micro-service architecture | |
| US10409650B2 (en) | Efficient access scheduling for super scaled stream processing systems | |
| US10262032B2 (en) | Cache based efficient access scheduling for super scaled stream processing systems | |
| CN101883098B (en) | System and method for distributed complex event detection under RFID (Radio Frequency Identification Devices) equipment network environment | |
| CN114443435B (en) | Performance monitoring alarm method and alarm system for container microservice | |
| CN107733986A (en) | Support the protection of integrated deployment and monitoring operation big data support platform | |
| CN108335075A (en) | A kind of processing system and method for Logistics Oriented big data | |
| US11960443B2 (en) | Block data storage system in an event historian | |
| US9842134B2 (en) | Data query interface system in an event historian | |
| CN111339175B (en) | Data processing method, device, electronic equipment and readable storage medium | |
| CN106940677A (en) | One kind application daily record data alarm method and device | |
| JP2005502122A (en) | System and method for monitoring software queuing applications | |
| KR102067032B1 (en) | Method and system for data processing based on hybrid big data system | |
| CN108170832B (en) | Monitoring system and monitoring method for heterogeneous database of industrial big data | |
| CN113468019A (en) | Hbase-based index monitoring method, device, equipment and storage medium | |
| US10812322B2 (en) | Systems and methods for real time streaming | |
| CN109977125A (en) | A kind of big data safety analysis plateform system based on network security | |
| CN111339052A (en) | Unstructured log data processing method and device | |
| CN110442627A (en) | Data transmission method and system between a kind of memory database system and data warehouse | |
| CN112948382A (en) | Information processing method and device based on big data and related equipment | |
| US20130024438A1 (en) | Database, business content data management server, and business content data management program | |
| WO2021217119A1 (en) | Analyzing tags associated with high-latency and error spans for instrumented software | |
| CN107515864B (en) | Method and equipment for monitoring workflow | |
| WO2020010531A1 (en) | Fault detection method and device | |
| US10579601B2 (en) | Data dictionary system in an event historian |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |