When discussing monitoring and alerting from a container application perspective, there are several key points to consider. Traditional host-based monitoring approaches, such as utilization and load monitoring, may no longer be suitable in a dynamic, multi-replica Pod environment. This is due to the dynamic nature and elasticity of application services in containerized and microservices architectures.
Utilizing open-source monitoring tools such as Prometheus, Alertmanager, Loki, and Grafana for monitoring Service Level Objectives (SLOs) of infrastructure and application resource consumption. This approach also involves unifying the handling of monitoring metrics, logs, and link tracing, as well as reducing ineffective alerts. Below is a solution concept and configuration example based on the S.T.A.R. (Situation, Task, Action, Result) methodology:
The organization requires monitoring of infrastructure and application resource consumption.
There is a need to unify the handling of monitoring metrics, logs, and link tracing, as well as the alert system.
To implement comprehensive monitoring of infrastructure and applications.
To reduce ineffective alerts while ensuring SLOs are met.
Prometheus and Alertmanager Configuration:
Loki Configuration:
Grafana Configuration:
Link Tracing:
Alert Optimization:
Type | Resource Consumption | Performance Metrics | Log Monitoring | Business Metrics | Special Considerations |
---|---|---|---|---|---|
Frontend Application | Browser Performance (CPU, Memory) | Page Load Time, FCP, CLS | Frontend Errors, User Behavior | User-Related Metrics | User Experience Metrics (FID, LCP) |
Java Backend Service | CPU, Memory, I/O | Response Time, Throughput | Application Logs, Error Tracking | API Calls, Transactions | JVM Metrics (GC, Heap Usage) |
Go Backend Service | CPU, Memory, I/O | Response Time, Throughput | Application Logs, Error Tracking | API Calls, Transactions | Go Goroutine Count, GC Metrics |
Python Backend Service | CPU, Memory, I/O | Response Time, Throughput | Application Logs, Error Tracking | API Calls, Transactions | GIL Lock Contention, Python-Specific Metrics |
Cache Middleware | CPU, Memory, Network | Command Throughput, Latency | Access Logs, Error Logs | Cache Hit Rate, Key-Space Stats | Persistence Latency, Replication Latency |
Message Queue | CPU, Memory, Network | Message Throughput, Latency | Service Logs, Error Logs | Queue Length, Message Backlog | Partition Status, Consumer Lag |
Relational Database | CPU, Memory, Disk I/O | Query Throughput, Response Time | Query Logs, Error Logs | Transaction Volume, Slow Queries | Lock Waits, Replication Delay, Buffer Pool Hit Rate |
NoSQL Database | CPU, Memory, Network | Read/Write Throughput, Response Time | Operation Logs, Error Logs | Data Size, Access Patterns | Distributed Health, Partition Status, Data Replication |
When monitoring non-relational databases (such as MongoDB, Redis, Cassandra, etc.), it is essential to pay special attention to their unique architectures and usage patterns. This includes monitoring the health of distributed clusters, data replication status, and responses to specific access patterns. This supplementary entry covers the primary monitoring aspects of non-relational databases, ensuring their high performance and reliability.
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原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。