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Microservices Monitoring and Management Documentation

Overview

This documentation outlines the key tools and objectives employed by our company in maintaining and optimising our microservices infrastructure. Understanding the roles of these tools and our goals will help in effectively managing our microservice ecosystem.

In the creation of our microservice tools like Prometheus, promtail and Loki were used .Below are documents used to help is setting up leveraging tools for monitoring system with a dimensional data model, flexible query Language, efficient time series database and modern alerting approach.

i) https://prometheus.io/docs/prometheus/latest/installation/ ii) https://prometheus.io/docs/guides/cadvisor/

iii) https://prometheus.io/docs/guides/node-exporter/

iv) https://prometheus.io/docs/visualization/grafana/

v) https://grafana.com/docs/loki/latest/send-data/promtail/

-This endpoint helps to show the promtail agent/ grafana Loki and how to use the promtail agent to ship logs to loki

Tools Used in Microservice Architecture(Visibility metrics)

1. Prometheus

  • Functionality: Prometheus is an open-source monitoring system and time-series database.
  • Key Features:
    • Metrics Collection: Scrapes and collects metrics from instrumented targets at regular intervals using a pull-based model.
    • Analysis and Alerting: Performs analysis on collected metrics and generates alerts based on predefined rules or detected anomalies.
  • Usage in Microservices: Critical for monitoring the health and performance of our microservices, ensuring they operate optimally.

2. Loki

  • Functionality: Loki is a log aggregation system, designed to store and query logs efficiently.
  • Key Features:
    • Log Aggregation: Aggregates logs from various sources, allowing for efficient indexing and searching.
    • Complement to Prometheus: Enhances Prometheus by providing a means to analyze log data alongside metrics.
  • Usage in Microservices: Essential for debugging, tracing transactions, and gaining deeper insights into the microservices’ behavior.

3. Promtail

  • Functionality: Promtail is an agent for tailing logs and shipping them to Loki.
  • Key Features:
    • Log Scraping: Collects logs from various sources, adding labels and metadata.
    • Log Forwarding: Sends collected logs to Loki for centralized storage and analysis.
  • Usage in Microservices: Facilitates efficient log management and ensures comprehensive log data is available for analysis.

4. Node Exporter

  • Functionality: Node Exporter is a Prometheus exporter for machine-level metrics.
  • Key Features:
    • System-Level Metrics: Exposes metrics related to CPU, memory, disk, and network usage of individual nodes.
    • Performance Insights: Provides data essential for understanding the performance and health of each node.
  • Usage in Microservices: Offers insights into the hardware and OS-level metrics of the servers hosting the microservices.

Goals of Using These Tools

1. Monitoring

  • Objective: To gain comprehensive insights into the health, performance, and behavior of each microservice component.
  • Implementation: Utilizing Prometheus and Node Exporter for continuous metrics collection and monitoring.

2. Log Aggregation

  • Objective: To collect, store, and analyze logs for effective debugging and system behaviour understanding.
  • Implementation: Employing Loki and Promtail for efficient log aggregation and analysis, complementing metrics data.

3. Alerting and Analysis

  • Objective: To set up alerting for immediate issue detection and perform in-depth analysis during incidents.
  • Implementation: Using Prometheus for setting up alerts based on metrics and analyzing both logs and metrics for comprehensive system understanding.

Conclusion

By leveraging Prometheus, Loki, Promtail, and Node Exporter, our company achieves a high level of observability in our microservices architecture. This integrated approach allows for effective monitoring, log management, and proactive maintenance of our systems. The use of these tools ensures that our DevOps teams can maintain high system reliability, quickly resolve issues, and have better visibility into our distributed environment.