Practical solution for monitoring Spring Cloud microservices

Practical solution for monitoring Spring Cloud microservices

  • 1. Introduction
  • 2. Monitoring Significance and Application Scenarios
    • 1. The importance of monitoring
    • 2. Monitoring application scenarios
  • 3. Monitoring system architecture
    • 1. Data source collection
    • 2. Data storage and processing
      • Visualization of data
  • 4. Monitoring data collection scheme
    • 1. Log collection method
    • 2. Index collection method
  • 5. Monitoring data storage and processing scheme
    • 1. Storage method
    • 2. Processing method
  • 6. Monitoring data visualization display scheme
    • 1. Monitoring Panel Tools
    • 2. Data visualization display
  • 7. Monitoring data alarm scheme
    • 1. Alarm trigger conditions
    • 2. Alarm method
  • 8. Practical cases
    • 1. Monitoring with Prometheus and Grafana
    • 2. Use ELK Stack to monitor
  • 9. Summary and review
    • 1. Challenges and opportunities for monitoring practice
    • 2. Future research direction

1. Introduction

Spring Cloud is a microservice framework based on Spring Boot. It provides a wealth of microservice functions, such as distributed configuration, service registration and discovery, service fuse, 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. The importance of monitoring

Monitoring can understand the operating status of the system in real time. When the system has problems, it can be found in time 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 various scenarios as follows:

  • System running status
  • System resource utilization, such as CPU, memory, disk, etc.
  • Interface Access Times and Latency
  • Error Rate and Exception Monitoring
  • Log information monitoring

3. Monitoring 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.

// Import 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, which facilitates real-time monitoring and debugging of system operating status.

# Configure Grafana data source
datasources:
  -name: Prometheus
    type: prometheus
    url: http://localhost:9090
    access: proxy
    basicAuth: false

4. Monitoring data collection scheme

1. Log collection method

Spring Boot uses Logback as the default log framework. By configuring Logback, operations such as log file output and scrolling can be realized, and logs can also be output to the console, system event log (Windows system), Syslog (Unix/Linux system), etc. .

Using the ELK/EFK solution to collect logs 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 applications through HTTP/HTTPS, such as application startup time, status, JVM heap memory usage, etc. This information can be obtained by external systems by accessing the API via HTTP.

In addition, open source software such as Prometheus and Grafana can capture the key indicators of the application runtime based on the endpoints provided by Spring Boot Actuator, and visualize them.

5. Monitoring data storage and processing scheme

1. Storage method

Real-time monitoring data usually needs to be able to acquire and store a large amount of data in a short period of time, so NoSQL databases are more suitable for storing these data. Commonly used databases include InfluxDB, Cassandra, and Elasticsearch.

Of course, if the enterprise has established a relevant data lake, it is also feasible to store it in the data lake.

2. Processing method

Monitoring data processing needs to be done in real time. Some common processing methods are:

  • Stream processing: Data input is processed in real time without reloading, and stream computing results can be output.
  • Batch processing: Collect enough data for processing, which is more suitable for problems that are cost-sensitive and require high accuracy.

6. Monitoring data visualization display scheme

1. Monitoring panel tool

Common monitoring panel tools are:

  • Grafana: supports multiple data sources, and can customize the monitoring panel UI.
  • Kibana: Provides powerful visual analysis and search functions based on the Elastic Stack.

2. Data visualization

Data visualization is usually subdivided into business layer, middleware layer and infrastructure layer, so it is necessary to choose different visualization methods according to the actual situation. For example:

  • Business layer: display common business indicators, such as visits, user activity, etc., which can be displayed in pie charts, tables, etc.
  • Middleware layer: Displays the call relationship among the various components of the system, call frequency, delay and other indicators, which can be displayed in the form of dependency graphs, histograms, etc.
  • Infrastructure layer: Display indicators such as host resources and service status, which can be displayed in the form of a dashboard.

7. Monitoring data alarm scheme

1. Alarm trigger condition

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:

  • CPU utilization is higher than 80%
  • Memory usage is higher than 80%
  • The request response time is greater than 5 seconds

2. Alarm method

Common alarm 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 mail and Slack

8. Practical cases

1. Monitoring with Prometheus and Grafana

Prometheus is an open source monitoring system developed by SoundCloud and has been included by 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 visualize the monitoring data collected by Prometheus.

The specific practical steps are as follows:

  1. Introduce Actuator and Micrometer dependencies in the Spring Boot project.
  2. Introduce Prometheus dependencies, and configure Prometheus access addresses and monitoring indicators.
  3. Introduce Grafana, configure Prometheus data sources in Grafana, create dashboards and display monitoring data.

2. Use ELK Stack to monitor

ELK Stack refers to the combination of Elasticsearch, Logstash and Kibana three open source projects. Can be used to collect, search and visualize various data. In Spring Cloud, ELK Stack can be used to collect application logs and display status information when the application is running.

The specific practical steps are as follows:

  1. Introduce the Logback dependency in the Spring Boot project and configure the Logback log output format.
  2. Introduce Filebeat dependency to send log files to Logstash.
  3. Logs are parsed and filtered in Logstash, and log information is stored in Elasticsearch.
  4. Create indexes and display monitoring data in Kibana.

9. Summary review

1. Challenges and opportunities for monitoring practices

Key challenges in monitoring practice include:

  • How to Select and Manage Monitoring Tools
  • How to design reasonable monitoring indicators
  • How to Customize Alarm Rules and Methods

At the same time, monitoring practice also brings us many opportunities:

  • Find and solve online problems in time
  • Optimize system performance and resource utilization
  • Improve user experience and satisfaction
  • Drive business continuity and innovation

2. The next research direction

In terms of monitoring practice, the following directions can be studied:

  • Real-time monitoring and AI-based alarm strategy
  • A unified solution for monitoring across platforms and hybrid cloud environments
  • Application of Big Data and Machine Learning Technology in Surveillance
  • An approach to combining monitoring with new technologies such as containers and microservices