Observability ‐ Logging & Metrics - FullstackCodingGuy/Developer-Fundamentals GitHub Wiki
When working with a small website that runs on a few servers, logging, metrics, and automation support are good practices but not a necessity. However, now that your site has grown to serve a large business, investing in those tools is essential.
Logging: Monitoring error logs is important because it helps to identify errors and problems in the system. You can monitor error logs at per server level or use tools to aggregate them to a centralized service for easy search and viewing.
Metrics: Collecting different types of metrics help us to gain business insights and understand the health status of the system. Some of the following metrics are useful:
Host level metrics: CPU, Memory, disk I/O, etc.
Aggregated level metrics: for example, the performance of the entire database tier, cache tier, etc.
Key business metrics: daily active users, retention, revenue, etc.
Automation: When a system gets big and complex, we need to build or leverage automation tools to improve productivity. Continuous integration is a good practice, in which each code check-in is verified through automation, allowing teams to detect problems early. Besides, automating your build, test, deploy process, etc. could improve developer productivity significantly.
What is Observe?
Observe, Inc. is a cloud-based Observability platform designed to help organizations monitor, analyze, and troubleshoot their logs, metrics, and traces in one unified system. It’s often used as an alternative to traditional observability tools like Datadog, Splunk, and New Relic but with a data-first approach.
Key Features of Observe:
✅ Unified Data Model – Converts logs, metrics, and traces into structured data for better insights. ✅ Powerful Querying (LQL - Log Query Language) – Allows for deep analysis of system behavior. ✅ Scalability & Cost Efficiency – Stores observability data in cloud object storage (e.g., S3), reducing costs. ✅ Event Correlation – Helps connect different system events for better debugging and root cause analysis. ✅ Integrations – Works with AWS, Kubernetes, and common DevOps tools.
How Observe Differs from Other Observability Tools
Unlike Splunk, which is primarily log-focused, Observe structures logs as relational data for better queryability.
Compared to Datadog, Observe offers cheaper storage by leveraging object storage instead of hot databases.
Unlike Prometheus, which is mainly for metrics, Observe supports logs, traces, and events in one place.
Observe vs. Prometheus: A Detailed Comparison
Both Observe and Prometheus are observability tools, but they serve different purposes and architectures. Here's a side-by-side comparison to help you understand their differences and use cases.
- Overview
- Key Differences
🔹 Data Scope & Use Cases
Observe:
Designed for logs, traces, metrics, and events.
Helps with root cause analysis by correlating different observability signals.
Ideal for teams looking for a full-stack observability platform.
Prometheus:
Primarily a metrics-focused tool.
Best suited for real-time monitoring of infrastructure, Kubernetes, and microservices.
Works well for alerting and dashboards but lacks built-in log and trace support.
🔹 Data Storage & Scalability
Observe:
Uses cloud-based storage (e.g., S3) for cost efficiency and long-term retention.
No need to manage storage or scaling—it's handled by the cloud.
Prometheus:
Stores data locally or in a remote database (e.g., Thanos for long-term storage).
Designed for short-term storage (default retention: 15 days).
Scaling requires Prometheus federation or external storage solutions.
🔹 Query Language & Analytics
Observe:
Uses Log Query Language (LQL) for structured searches.
More powerful for ad-hoc log analysis and root cause investigation.
Allows correlation of logs, traces, and metrics in one query.
Prometheus:
Uses PromQL, optimized for time-series queries.
Strong for metric-based alerting (e.g., CPU usage, request latency).
Cannot natively correlate logs and traces.
🔹 Alerting & Dashboards
Observe:
Provides rich dashboards with a focus on insights across multiple data types.
Integrates well with Grafana and other visualization tools.
Alerts can be created from logs, traces, and metrics together.
Prometheus:
Alertmanager provides robust metrics-based alerting.
Works natively with Grafana for visualization.
Cannot trigger alerts based on logs or traces directly.
🔹 Cost & Maintenance
Observe:
SaaS-based; pricing is based on data ingestion & storage usage.
No need for infrastructure management.
Prometheus:
Free & open-source, but requires self-hosting and scaling effort.
Storage and high availability require additional tools (Thanos, Cortex).
- When to Choose Observe vs. Prometheus
✅ Choose Observe if: ✔️ You need logs, metrics, and traces in one platform. ✔️ You want a fully managed cloud solution without worrying about infrastructure. ✔️ You need long-term data retention and deep analysis.
✅ Choose Prometheus if: ✔️ You need high-performance metrics monitoring for Kubernetes, cloud, or microservices. ✔️ You want an open-source, self-hosted solution with full control. ✔️ You already have a separate logging & tracing system (e.g., Loki, Jaeger).
- Can They Be Used Together?
Yes! Many companies use Prometheus for metrics and Observe for logs, traces, and full observability.
Example: Prometheus collects CPU & memory metrics, while Observe helps correlate these with application logs and traces for faster troubleshooting.
Integration: Observe can ingest Prometheus metrics, allowing a unified view.