1Introduction
Spring Cloud is a microservice framework implemented based on Spring Boot. It provides rich microservice functions, such as distributed configuration, service registration and discovery, service circuit breaker, load balancing, etc. In order to better manage and monitor such a complex microservice system, it needs to be monitored.
2 Monitoring significance and application scenarios
1. Importance of monitoring
Monitoring can understand the operating status of the system in real time. When problems occur in the system, timely detection and measures can be taken to avoid system crashes. At the same time, system performance optimization can be performed based on monitoring data to improve system throughput and performance.
2. Monitoring application scenarios
Monitoring can be applied in a variety of scenarios as follows:
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System running status
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System resource utilization, such as CPU, memory, disk, etc.
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Interface access times and latency
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Error rate and exception monitoring
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Log information monitoring
3 Monitoring System Architecture
The monitoring system architecture mainly includes the following three parts:
1. Data source collection
Collect system operating status and performance parameters by integrating corresponding monitoring components in microservices, such as using the Spring Boot Actuator module for monitoring and collection.
<!--Introducing the Spring Boot Actuator module --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-actuator</artifactId> </dependency>
2. Data storage and processing
Store the collected monitoring data in the database and organize and process the monitoring data through data analysis and mining technology to provide valuable data support for the next step of data visualization and display.
Visual display of data
Use open source components such as Grafana and Elasticsearch for data display and visual analysis, and display the collected monitoring data in the form of charts to facilitate real-time monitoring and debugging of the system’s operating status.
# Configure Grafana data source
datasources:
- name: Prometheus
type: prometheus
url: http://localhost:9090
access:proxy
basicAuth: false
4Monitoring data collection plan
1. Log collection method
Spring Boot uses Logback as the default logging framework. By configuring Logback, you can realize log file output, scrolling and other operations, and you can also output logs to the console, system event log (Windows system), Syslog (Unix/Linux system) and other locations. .
If you use the ELK/EFK solution to collect logs, you can use Logstash/Fluentd to extract the information in the logs for analysis and storage.
2. Indicator collection method
Spring Cloud is connected to Spring Boot Actuator by default, which can expose some key indicators of the application through HTTP/HTTPS, such as application startup time, status, JVM heap memory usage, etc. This information can be obtained by external systems via HTTP access API.
In addition, open source software such as Prometheus and Grafana can capture key application runtime indicators based on the endpoints provided by Spring Boot Actuator and display them visually.
5 Monitoring data storage and processing solution
1. Storage method
Real-time monitoring data usually requires the ability to acquire and store large amounts of data in a short period of time, so NoSQL databases are more suitable for storing this data. Commonly used databases include InfluxDB, Cassandra, and Elasticsearch.
Of course, if the enterprise has already established a relevant data lake, it is also feasible to store it in the data lake.
2. Processing method
Monitoring data processing requires a real-time approach. Some common processing methods are:
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Stream processing: Data input is processed immediately without reloading, and streaming calculation results can be output.
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Batch processing: Collect enough data for processing, which is more suitable for cost-sensitive and accuracy-requiring problems.
6 Monitoring data visualization display plan
1. Monitoring panel tool
Common monitoring panel tools include:
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Grafana: Supports multiple data sources and can customize the monitoring panel UI.
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Kibana: Provides powerful visual analysis and search functions based on the Elastic Stack.
2. Data visualization display
Data visualization is usually subdivided into business layer, middleware layer and infrastructure layer, so different visualization methods need to be selected according to the actual situation. For example:
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Business layer: Display common business indicators, such as visits, user activity, etc., which can be displayed in the form of pie charts, tables, etc.
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Middleware layer: Displays the calling relationship between the various components of the system and indicators such as call frequency and latency. It can be displayed in the form of dependency graphs, histograms, etc.
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Infrastructure layer: Displays host resources, service status and other indicators, which can be displayed using dashboards and other forms.
7Monitoring data alarm solution
1. Alarm triggering conditions
Alarm triggering conditions need to be customized according to the actual situation and can be set according to the nature of the application and the indicators of concern. Generally speaking, the more common trigger conditions include:
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CPU utilization is higher than 80%
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Memory usage is higher than 80%
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Request response time is greater than 5 seconds
2. Alarm method
Common alert methods include email and SMS notifications. In Spring Cloud, you can use the Actuator provided by Spring Boot to implement the alarm function. Actuator can provide us with various monitoring data, and it also supports the integration of different notification methods such as email and Slack.
8Practical Cases
1. Monitoring using Prometheus and Grafana
Prometheus is an open source monitoring system developed by SoundCloud and has been included in CNCF. Prometheus can monitor Spring Boot applications, collect various indicator data, and provide query and alarm functions.
Grafana is an open source data visualization tool that can be seamlessly integrated with Prometheus to visually display the monitoring data collected by Prometheus.
The specific practical steps are as follows:
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Introduce Actuator and Micrometer dependencies into the Spring Boot project.
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Introduce Prometheus dependencies, and configure Prometheus’s access address and monitoring indicators.
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Introduce Grafana, configure the Prometheus data source in Grafana, create dashboards and display monitoring data.
2. Monitor using ELK Stack
ELK Stack refers to the combination of three open source projects: Elasticsearch, Logstash and Kibana. Can be used to collect, search and visualize various data. ELK Stack can be used in Spring Cloud to collect application logs and display status information when the application is running.
The specific practical steps are as follows:
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Introduce Logback dependency into the Spring Boot project and configure the Logback log output format.
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Introduce Filebeat dependency and send log files to Logstash.
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Parse and filter logs in Logstash, and store log information in Elasticsearch.
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Create indexes and display monitoring data in Kibana.
9 Summary Review
1. Challenges and opportunities in monitoring practice
Major challenges in monitoring practices include:
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How to choose and manage monitoring tools
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How to design reasonable monitoring indicators
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How to customize alarm rules and methods
At the same time, monitoring practice also brings us many opportunities:
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Find and solve online problems in a timely manner
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Optimize system performance and resource utilization
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Improve user experience and satisfaction
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Promote sustainable business development and innovation
2. Next research direction
In terms of monitoring practice, the following directions can be studied:
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Real-time monitoring and AI-based alarm strategies
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Unified monitoring solution across platforms and hybrid cloud environments
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Application of big data and machine learning technology in monitoring
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Ways to combine monitoring with new technologies like containers and microservices
Source: blog.csdn.net/u010349629/
article/details/130837190
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