CN113220521A - Real-time monitoring system - Google Patents
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- CN113220521A CN113220521A CN202110151755.1A CN202110151755A CN113220521A CN 113220521 A CN113220521 A CN 113220521A CN 202110151755 A CN202110151755 A CN 202110151755A CN 113220521 A CN113220521 A CN 113220521A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
- G06F11/3068—Monitoring arrangements determined by the means or processing involved in reporting the monitored data where the reporting involves data format conversion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/327—Alarm or error message display
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
Abstract
The application discloses a real-time monitoring system, which comprises a collecting module, a processing module, a storage module, a counting module and an application module. The acquisition module adopts a high-performance real-time data acquisition architecture and supports mass data access; the processing module is used for processing log data accessed by the acquisition system and dividing the log data into real-time data and offline data, the offline data is directly accessed into an offline database for later analysis, and the real-time data calculates results in real time and stores the results into the database; the storage module stores the result processed by the processing module into a corresponding database; the statistical module is mainly used for further summarizing and counting the basic data calculated by the storage module to form a result which can be finally displayed and alarmed, and the processing logic of the statistical module is more because different data processing is needed for different service requirements; the application module is the final presentation and alarm.
Description
Technical Field
The application relates to a monitoring system, in particular to a real-time monitoring system.
Background
The monitoring system is also called as a closed circuit television monitoring system, and a typical monitoring system mainly comprises five parts, namely front-end audio and video acquisition equipment, audio and video transmission equipment and rear-end storage, control and display equipment, wherein the rear-end equipment can be further divided into central control equipment and sub-control equipment. The front-end and back-end devices have various configurations, and the connection between them (also called transmission system) can be realized by various means such as coaxial cable, twisted pair, optical fiber, microwave, wireless, etc.
The traditional monitoring system has certain defects, no large-disk total flow or error data exists, monitoring and alarming are single, data such as day, week and peak values cannot be given, only interface flow can be monitored, monitoring cannot be carried out respectively according to services, machines, departments and personnel, monitoring dimensionality is not enough, a non-uniform monitoring platform is adopted, each department or service is independent, no flow estimation alarming is carried out, complex log statistics and calculation are not supported, and logs cannot be stored for a long time. Therefore, a real-time monitoring system is proposed to address the above problems.
Content of application
The real-time monitoring system comprises an acquisition module, a processing module, a storage module, a statistical module and an application module, wherein the acquisition module adopts a high-performance real-time data acquisition architecture and supports the access of mass data; the processing module is used for processing log data accessed by the acquisition system and dividing the log data into real-time data and offline data, the offline data is directly accessed into an offline database for later analysis, and the real-time data calculates results in real time and stores the results into the database; the storage module stores the result processed by the processing module into a corresponding database; the statistical module is mainly used for further summarizing and counting the basic data calculated by the storage module to form a result which can be finally displayed and alarmed, and the processing logic of the statistical module is more because different data processing is needed for different service requirements; the application module is the final presentation and alarm.
Further, the collection module internally comprises a foreground server, a Logback collection server, a Nginx load server, a Flume server and a Kafka server, wherein the foreground server comprises multiple ways, such as an APP client, an applet, a middle station external interface service, a vehicle model, an information background server, a NodeJs server of a PCM station and the like, the Logback collection server is used for collecting logs on each server and sending the logs to the Nginx load server, the ngx load server receives the logs sent by the Logback and sends the logs to the Flume server, different foreground servers have different Flume servers to receive data, the data are sent to the Kafka server through the Flume server and enter the Kafka cluster, and the data collection work is finished.
Furthermore, the acquisition module also comprises a service stopping information server, the service stopping information server is connected with the flash server in parallel, and the service stopping information server can prevent the flash server from sending information to the Kafka server.
Further, the processing module comprises a real-time processing mode and an off-line processing mode, the off-line processing mode formats the accessed log data through the Flink, then directly writes the data into the Hdfs, and waits for the data to enter the Hive library, and the real-time processing mode comprises three steps: (1) the method comprises the steps of performing ETL and index calculation on Flink in real time, and calculating various indexes such as flow, error amount, peak value, mean value and the like in a minute level; (2) calculating a Flink real-time map, extracting data content, analyzing a calling relation between interfaces, and analyzing the whole flow map in a minute level; (3) and (4) warehousing the Flink real-time detailed data, extracting the data content, and warehousing the data content into the ES for real-time data query.
Further, the storage module comprises three storage modes of Hdfs, Mysql and ElasticSearch, the Hdfs is used for off-line calculation, the Hdfs is stored firstly, then the Hdfs enters a Hive library, then off-line calculation can be carried out, the Mysql stores the result (minute-level data) of the last real-time calculation into the Mysql to provide final data statistics and alarm use, and the ElasticSearch is used for inquiring the details of the log in real time.
Furthermore, the statistical module comprises a real-time statistical mode and an off-line statistical mode, the off-line statistical mode stores the data stored in the Hdfs in the previous layer into the hive library after processing, different front-end services have different tables, and statistics is performed on different tables, and the calculation is performed once in one hour because of off-line calculation. Calculating data with different dimensions including minutes, ten minutes, hours, days, weeks, peaks, time consumption, flow, error amount, interfaces, departments and the like, wherein the real-time statistical mode takes the minutes and statistical results as units, and then summarizing the data to different levels, such as: the dimension of statistics is consistent with the offline dimension in days, hours, ten minutes and the like, so that the comparison between real-time data and offline data can be ensured, and the system state can be judged.
Further, the application module comprises a monitoring platform, a data center, a monitoring center, a log query device and a data manager, wherein the monitoring platform displays data in various display forms, so that people can conveniently check and analyze the data, the data center is mainly used for displaying data with different dimensions in a period of time, users can regularly gather and count the data to find out some problems existing in the system for a long time, the monitoring center mainly sends an alarm, records the alarm, checks the alarm, provides an interactive interface and feeds back the progress of alarm processing in time, the log query device can query each detailed log according to multiple dimensions of personnel, interfaces, time, mobile phone numbers and the like, and is used for analyzing the reasons of errors, and the data manager manages data of personnel, departments, interfaces, alarm thresholds and the like.
Further, an information encryptor is assembled on the Logback acquisition server, the information encryptor encrypts the collected logs, the encrypted data are transmitted to the Nginx load server, an information decryptor is assembled on the Kafka server, and the information decryptor decrypts and restores the encrypted data.
Further, the Logback acquisition server is provided with an information checker, the information checker randomly extracts array data from the encrypted data to perform reduction processing, then compares the reduced data with the original data, and detects whether the data encryption by the information encryptor is wrong.
Further, the Logback acquisition server is provided with an information duplicator, the information duplicator duplicates and stores the collected logs, when the information verifier detects that data encryption has errors, the encrypted data is deleted, the duplicated and stored logs are used for encryption processing again, and when the information verifier detects that data encryption has no errors, the duplicated and stored log data is deleted.
The beneficial effect of this application is: the application provides a real-time monitoring system with monitoring dimensionality, flow pre-estimation alarming and long-term log storage functions.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a flow chart of the system of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "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 steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, it may be a fixed connection, a removable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, the real-time monitoring system includes an acquisition module, a processing module, a storage module, a statistical module, and an application module, where the acquisition module adopts a high-performance real-time data acquisition architecture to support access of mass data; the processing module is used for processing log data accessed by the acquisition system and dividing the log data into real-time data and offline data, the offline data is directly accessed into an offline database for later analysis, and the real-time data calculates results in real time and stores the results into the database; the storage module stores the result processed by the processing module into a corresponding database; the statistical module is mainly used for further summarizing and counting the basic data calculated by the storage module to form a result which can be finally displayed and alarmed, and the processing logic of the statistical module is more because different data processing is needed for different service requirements; the application module is the final presentation and alarm.
Further, the collection module internally comprises a foreground server, a Logback collection server, a Nginx load server, a Flume server and a Kafka server, wherein the foreground server comprises multiple ways, such as an APP client, an applet, a middle station external interface service, a vehicle model, an information background server, a NodeJs server of a PCM station and the like, the Logback collection server is used for collecting logs on each server and sending the logs to the Nginx load server, the ngx load server receives the logs sent by the Logback and sends the logs to the Flume server, different foreground servers have different Flume servers to receive data, the data are sent to the Kafka server through the Flume server and enter the Kafka cluster, and the data collection work is finished.
Furthermore, the acquisition module also comprises a service stopping information server, the service stopping information server is connected with the flash server in parallel, and the service stopping information server can prevent the flash server from sending information to the Kafka server.
Further, the processing module comprises a real-time processing mode and an off-line processing mode, the off-line processing mode formats the accessed log data through the Flink, then directly writes the data into the Hdfs, and waits for the data to enter the Hive library, and the real-time processing mode comprises three steps: (1) the method comprises the steps of performing ETL and index calculation on Flink in real time, and calculating various indexes such as flow, error amount, peak value, mean value and the like in a minute level; (2) calculating a Flink real-time map, extracting data content, analyzing a calling relation between interfaces, and analyzing the whole flow map in a minute level; (3) and (4) warehousing the Flink real-time detailed data, extracting the data content, and warehousing the data content into the ES for real-time data query.
Further, the storage module comprises three storage modes of Hdfs, Mysql and ElasticSearch, the Hdfs is used for off-line calculation, the Hdfs is stored firstly, then the Hdfs enters a Hive library, then off-line calculation can be carried out, the Mysql stores the result (minute-level data) of the last real-time calculation into the Mysql to provide final data statistics and alarm use, and the ElasticSearch is used for inquiring the details of the log in real time.
Furthermore, the statistical module comprises a real-time statistical mode and an off-line statistical mode, the off-line statistical mode stores the data stored in the Hdfs in the previous layer into the hive library after processing, different front-end services have different tables, and statistics is performed on different tables, and the calculation is performed once in one hour because of off-line calculation. Calculating data with different dimensions including minutes, ten minutes, hours, days, weeks, peaks, time consumption, flow, error amount, interfaces, departments and the like, wherein the real-time statistical mode takes the minutes and statistical results as units, and then summarizing the data to different levels, such as: the dimension of statistics is consistent with the offline dimension in days, hours, ten minutes and the like, so that the comparison between real-time data and offline data can be ensured, and the system state can be judged.
Further, the application module comprises a monitoring platform, a data center, a monitoring center, a log query device and a data manager, wherein the monitoring platform displays data in various display forms, so that people can conveniently check and analyze the data, the data center is mainly used for displaying data with different dimensions in a period of time, users can regularly gather and count the data to find out some problems existing in the system for a long time, the monitoring center mainly sends an alarm, records the alarm, checks the alarm, provides an interactive interface and feeds back the progress of alarm processing in time, the log query device can query each detailed log according to multiple dimensions of personnel, interfaces, time, mobile phone numbers and the like, and is used for analyzing the reasons of errors, and the data manager manages data of personnel, departments, interfaces, alarm thresholds and the like.
Further, an information encryptor is assembled on the Logback acquisition server, the information encryptor encrypts the collected logs, the encrypted data are transmitted to the Nginx load server, an information decryptor is assembled on the Kafka server, and the information decryptor decrypts and restores the encrypted data.
Further, the Logback acquisition server is provided with an information checker, the information checker randomly extracts array data from the encrypted data to perform reduction processing, then compares the reduced data with the original data, and detects whether the data encryption by the information encryptor is wrong.
Further, the Logback acquisition server is provided with an information duplicator, the information duplicator duplicates and stores the collected logs, when the information verifier detects that data encryption has errors, the encrypted data is deleted, the duplicated and stored logs are used for encryption processing again, and when the information verifier detects that data encryption has no errors, the duplicated and stored log data is deleted.
When the system is used, the acquisition module adopts a high-performance real-time data acquisition architecture to support the access of mass data; the processing module is used for processing log data accessed by the acquisition system and dividing the log data into real-time data and offline data, the offline data is directly accessed into an offline database for later analysis, and the real-time data calculates results in real time and stores the results into the database; the storage module stores the result processed by the processing module into a corresponding database; the statistical module is mainly used for further summarizing and counting the basic data calculated by the storage module to form a result which can be finally displayed and alarmed, and the processing logic of the statistical module is more because different data processing is needed for different service requirements; the application module is the final presentation and alarm.
The application has the advantages that:
the acquisition module adopts a high-performance real-time data acquisition architecture and supports mass data access; the processing module is used for processing log data accessed by the acquisition system and dividing the log data into real-time data and offline data, the offline data is directly accessed into an offline database for later analysis, and the real-time data calculates results in real time and stores the results into the database; the storage module stores the result processed by the processing module into a corresponding database; the statistical module is mainly used for further summarizing and counting the basic data calculated by the storage module to form a result which can be finally displayed and alarmed, and the processing logic of the statistical module is more because different data processing is needed for different service requirements; the application module is used for final display and alarm, and solves the problems that the total flow or error data of a large disk is absent, monitoring and alarm are single and cannot be given, data such as daily, weekly and peak values and the like can only be monitored, interface flow can not be monitored respectively according to services, machines, departments and personnel, monitoring dimensionality is insufficient, a non-uniform monitoring platform is adopted, each department or service is independent, no flow estimation alarm is provided, complex log statistics and calculation are not supported, and logs cannot be stored for a long time.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. Real-time monitoring system, its characterized in that: the system comprises an acquisition module, a processing module, a storage module, a statistical module and an application module, wherein the acquisition module adopts a high-performance real-time data acquisition architecture and supports mass data access; the processing module is used for processing log data accessed by the acquisition system and dividing the log data into real-time data and offline data, the offline data is directly accessed into an offline database for later analysis, and the real-time data calculates results in real time and stores the results into the database; the storage module stores the result processed by the processing module into a corresponding database; the statistical module is mainly used for further summarizing and counting the basic data calculated by the storage module to form a result which can be finally displayed and alarmed, and the processing logic of the statistical module is more because different data processing is needed for different service requirements; the application module is the final presentation and alarm.
2. The real-time monitoring system of claim 1, wherein: the collecting module comprises a foreground server, a Logback collecting server, a Nginx load server, a Flume server and a Kafka server, wherein the foreground server comprises multiple ways, such as an APP client, an applet, a middle platform external interface service, a NodeJs server of a vehicle type, an information background server and a PCM station, the Logback collecting server is used for collecting logs on each server and sending the logs to the Nginx load server, the Nginx load server receives the logs sent by the Logback and sends the logs to the Flume server, different foreground servers have different Flume servers to receive data, the data are sent to the Kafka server through the Flume servers and enter the Kafka cluster, and the data collecting work is finished.
3. The real-time monitoring system of claim 2, wherein: the acquisition module also comprises a service stopping information server, the service stopping information server is connected with the flash server side by side, and the service stopping information server can prevent the flash server from sending information to the Kafka server.
4. The real-time monitoring system of claim 1, wherein: the processing module comprises a real-time processing mode and an off-line processing mode, the off-line processing mode formats the accessed log data through the Flink, then directly writes the data into the Hdfs, and waits for the data to enter the Hive library, and the real-time processing mode comprises the following three steps: (1) the method comprises the steps of performing ETL and index calculation on Flink in real time, and calculating various indexes such as flow, error amount, peak value, mean value and the like in a minute level; (2) calculating a Flink real-time map, extracting data content, analyzing a calling relation between interfaces, and analyzing the whole flow map in a minute level; (3) and (4) warehousing the Flink real-time detailed data, extracting the data content, and warehousing the data content into the ES for real-time data query.
5. The real-time monitoring system of claim 1, wherein: the storage module comprises three storage modes of Hdfs, Mysql and ElasticSearch, wherein the Hdfs is used for off-line calculation, the Hdfs is stored firstly, then the Hdfs enters a Hive library, off-line calculation can be performed next, the Mysql stores the result (minute-level data) of the last real-time calculation into the Mysql to provide final data statistics and alarm for use, and the ElasticSearch is used for inquiring the details of the log in real time.
6. The real-time monitoring system of claim 1, wherein: the statistical module comprises a real-time statistical mode and an off-line statistical mode, the off-line statistical mode stores the data stored in the Hdfs in the previous layer, the processed data are sent to the hive library, different front-end services have different tables, and statistics is carried out on the different tables, so that the calculation is carried out once an hour due to off-line calculation. Calculating data with different dimensions including minutes, ten minutes, hours, days, weeks, peaks, time consumption, flow, error amount, interfaces, departments and the like, wherein the real-time statistical mode takes the minutes and statistical results as units, and then summarizing the data to different levels, such as: the dimension of statistics is consistent with the offline dimension in days, hours, ten minutes and the like, so that the comparison between real-time data and offline data can be ensured, and the system state can be judged.
7. The real-time monitoring system of claim 1, wherein: the application module comprises a monitoring platform, a data center, a monitoring center, a log query device and a data manager, wherein the monitoring platform displays data in various display forms, so that people can conveniently check and analyze the data, the data center is mainly used for displaying data with different dimensionalities in a period of time, and users can regularly gather and count to find out some problems existing in the system for a long time.
8. The real-time monitoring system of claim 2, wherein: the Logback acquisition server is provided with an information encryptor, the information encryptor encrypts the collected logs and transmits the encrypted data to the Nginx load server, the Kafka server is provided with an information decryptor, and the information decryptor decrypts and restores the encrypted data.
9. The real-time monitoring system of claim 8, wherein: the Logback acquisition server is provided with an information checker, the information checker randomly extracts array data from the encrypted data to restore the array data, then compares the restored data with the original data, and detects whether the data encryption by the information encryptor is wrong.
10. The real-time monitoring system of claim 9, wherein: the Logback acquisition server is provided with an information duplicator, the information duplicator duplicates and stores the collected logs, when the information verifier detects that data encryption has errors, the encrypted data is deleted, the duplicated and stored logs are used for encryption processing again, and when the information verifier detects that the data encryption has no errors, the duplicated and stored log data is deleted.
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| CN114471408A (en) * | 2022-01-27 | 2022-05-13 | 广东天航动力科技有限公司 | Automatic monitoring system for powder material production |
| CN117290384A (en) * | 2023-11-27 | 2023-12-26 | 同方赛威讯信息技术有限公司 | Graphic and text retrieval system and method based on combination of big data and computer vision |
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