❇️ feat: Add Observability section to wiki page with logs, metrics, a…

…nd tracing info

hugo-blog/content/docs/roadmap/observability/Tracing/_index.md

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+---
+title: "Tracing"
+weight: 3
+---
+## Introduction
+
+In modern distributed systems, understanding the flow of requests and the interactions between various services is crucial for ensuring performance, reliability, and scalability. This is where tracing observability comes into play. Tracing provides detailed insights into the lifecycle of a request as it propagates through different components of a system. This blog post explores the concept of tracing within the scope of observability and discusses some popular tools that help achieve comprehensive tracing observability.
+
+## Tracing and Observability
+
+Observability consists of three primary pillars: logs, metrics, and traces. Tracing focuses on capturing the end-to-end journey of requests across different services and components. This helps in identifying performance bottlenecks, understanding dependencies, and diagnosing issues in complex systems.
+
+### Why Tracing Matters
+
+- **Performance Analysis**: Tracing helps in identifying slow or failing components by providing detailed timing information about each segment of a request's journey.
+- **Root Cause Analysis**: When an issue occurs, tracing can pinpoint the exact location and cause of the problem, reducing the mean time to recovery (MTTR).
+- **Service Dependency Mapping**: Tracing provides a clear picture of how different services interact with each other, aiding in the understanding of dependencies and impacts.
+- **Optimizing Resource Usage**: By analyzing traces, teams can optimize resource allocation and usage, improving overall system efficiency.
+
+### Best Practices for Tracing
+
+- **Instrument All Services**: Ensure that tracing is implemented across all microservices to get a complete picture of request flows.
+- **Use Unique Identifiers**: Assign unique identifiers to each request to track its path accurately across different services.
+- **Integrate with Other Observability Tools**: Combine tracing data with logs and metrics to gain a holistic view of system performance and issues.
+- **Regularly Review Tracing Data**: Continuously analyze tracing data to identify patterns, trends, and areas for improvement.
+
+## Related Tools
+
+Several tools are available to facilitate tracing observability. Here are two of the most popular ones:
+
+### Jaeger
+
+Jaeger is an open-source end-to-end distributed tracing tool originally developed by Uber Technologies. It is used for monitoring and troubleshooting microservices-based distributed systems, allowing users to track the progress and performance of requests as they flow through various services.
+
+#### Key Features of Jaeger
+
+- **Distributed Context Propagation**: Tracks requests across multiple services.
+- **Performance and Latency Optimization**: Identifies slow services and bottlenecks.
+- **Dependency Analysis**: Visualizes service dependencies and communication patterns.
+- **Root Cause Identification**: Pinpoints exact failure points and causes.
+
+### Splunk
+
+Splunk is a powerful platform for searching, monitoring, and analyzing machine-generated data. It also provides robust tracing capabilities, allowing users to trace requests through distributed systems, correlate tracing data with logs and metrics, and gain comprehensive insights into system performance.
+
+#### Key Features of Splunk
+
+- **Unified Observability**: Combines traces with logs and metrics for a complete observability solution.
+- **Real-Time Monitoring**: Provides real-time insights into system performance and issues.
+- **Scalable and Extensible**: Handles large volumes of tracing data with ease.
+- **Advanced Analytics**: Offers powerful search and analytics capabilities to derive meaningful insights from tracing data.
+
+## Learning Resources
+
+### Books
+- [Distributed Systems Observability](https://www.oreilly.com/library/view/distributed-systems-observability/9781492033431/) by Cindy Sridharan.
+- [Mastering Distributed Tracing](https://www.amazon.com/Mastering-Distributed-Tracing-performance-microservices-ebook/dp/B07MBNGF7Q) by Yuri Shkuro.
+
+### Miscellaneous
+- [Getting Started with Jaeger](https://www.youtube.com/watch?v=auLtKhrkzdw)
+- [Tracing with Splunk](https://docs.splunk.com/Documentation/Splunk/latest/Tracing)
+- [Comprehensive Guide to Distributed Tracing](https://opentracing.io/guides/)
+

hugo-blog/content/docs/roadmap/observability/logging/_index.md

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+---
+title: "Logging"
+weight: 1
+---
+
+## Introduction
+
+In today's complex and dynamic software environments, maintaining observability is crucial for ensuring the reliability and performance of applications. Observability refers to the ability to measure the internal states of a system based on the outputs it produces. Logging plays a critical role in observability, as it provides detailed records of events that occur within a system. This blog post explores the concept of logging within the scope of observability and discusses some popular tools that aid in achieving comprehensive observability.
+
+## Observability and Logging
+
+Observability consists of three main pillars: logging, metrics, and traces. Logging captures discrete events and provides detailed context about what happens within a system. This data is invaluable for debugging and monitoring applications, as it helps engineers understand system behavior and diagnose issues.
+
+### Why Logging Matters
+
+- **Debugging**: Logs offer detailed insights into application behavior, helping developers pinpoint and resolve issues quickly.
+- **Monitoring**: Continuous logging allows for real-time monitoring of applications, ensuring that any anomalies or performance bottlenecks are promptly identified.
+- **Auditing and Compliance**: Logs provide an immutable record of events, which is essential for auditing and meeting regulatory compliance requirements.
+
+### Best Practices for Logging
+
+- **Consistency**: Use a consistent logging format across your applications to simplify analysis.
+- **Granularity**: Log at appropriate levels (e.g., error, warning, info, debug) to balance between verbosity and usability.
+- **Contextual Information**: Include relevant context in logs, such as user IDs, request IDs, and timestamps, to facilitate deeper insights.
+
+## Related Tools
+
+Several tools are available to help manage and analyze logs effectively. Here are some of the most popular ones:
+
+### ELK Stack
+
+The ELK Stack consists of Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine, Logstash is a server-side data processing pipeline that ingests data from multiple sources, and Kibana is a visualization tool. Together, they form a powerful suite for searching, analyzing, and visualizing log data.
+
+### Splunk
+
+Splunk is a leading platform for operational intelligence, providing powerful search, monitoring, and analysis capabilities. It allows you to collect, index, and correlate real-time data in a searchable repository, making it easier to generate insights and dashboards.
+
+### EFK Stack
+
+Similar to the ELK Stack, the EFK Stack consists of Elasticsearch, Fluentd, and Kibana. Fluentd is a data collector that unifies the data collection and consumption process. The EFK Stack is known for its flexibility and scalability in handling large volumes of log data.
+
+### Loki
+
+Loki, developed by Grafana Labs, is a log aggregation system designed to store and query logs from various sources. It is highly efficient and integrates seamlessly with Grafana, providing a streamlined solution for log visualization and analysis.
+
+### Sentry
+
+Sentry is an open-source error tracking tool that helps developers monitor and fix crashes in real-time. It provides detailed crash reports, helping you identify the root cause of issues quickly and efficiently.
+
+### Graylog
+
+Graylog is an open-source log management platform that provides real-time analysis and visualization of log data. It offers a range of features, including powerful search capabilities, alerting, and dashboards, making it a popular choice for log management.
+
+## Learning Resources
+
+### Books
+- [Observability Engineering: Achieving Production Excellence](https://amazon.com/Observability-Engineering-Achieving-Production-Excellence/dp/1492076449) by Charity Majors, Liz Fong-Jones, and George Miranda.
+- [The Authoritative Guide to Understanding the Concepts Surrounding Logging and Log Management](https://www.amazon.com/Logging-Log-Management-Authoritative-Understanding/dp/1597496359) by Anton Chuvakin, Kevin Schmidt, and Chris Phillips.
+
+### Courses
+- [FreeCodeCamp's Introduction to Monitoring and Observability](https://www.freecodecamp.org/learn/quality-assurance/)
+
+### Miscellaneous
+- [Introduction to the ELK Stack](https://www.youtube.com/playlist?list=PLS1QulWo1RIYkDHcPXUtH4sqvQQMH3_TN)
+- [Getting Started with Graylog](https://docs.graylog.org/en/4.0/pages/getting_started.html)
+- [Understanding Loki and Grafana for Log Aggregation](https://grafana.com/docs/loki/latest/)

hugo-blog/content/docs/roadmap/observability/monitoring/_index.md

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+---
+title: "Monitoring"
+weight: 2
+---
+## Introduction
+
+In the realm of software development and IT operations, monitoring and observability are essential practices that ensure system reliability, performance, and availability. Monitoring observability involves collecting and analyzing data from applications, infrastructure, and services to gain insights into their health and performance. This blog post delves into the importance of monitoring in achieving observability and highlights several tools that facilitate these processes.
+
+## Monitoring and Observability
+
+Observability encompasses three primary pillars: metrics, logs, and traces. Monitoring focuses on the metrics aspect, providing quantitative data about system performance and behavior. By continuously observing key metrics, teams can detect anomalies, identify trends, and respond to issues before they impact end users.
+
+### Why Monitoring Matters
+
+- **Proactive Issue Detection**: Monitoring allows teams to identify potential problems before they escalate, minimizing downtime and improving user experience.
+- **Performance Optimization**: Continuous monitoring helps in identifying performance bottlenecks, enabling teams to optimize applications and infrastructure for better performance.
+- **Capacity Planning**: Monitoring provides data on resource usage, which is critical for effective capacity planning and scaling decisions.
+- **Compliance and Auditing**: Monitoring can help meet compliance requirements by providing a historical record of system performance and changes.
+
+### Best Practices for Monitoring
+
+- **Define Key Metrics**: Identify and monitor critical metrics that reflect the health and performance of your system.
+- **Set Thresholds and Alerts**: Establish thresholds for key metrics and configure alerts to notify teams of potential issues.
+- **Use Dashboards**: Utilize dashboards to visualize metrics in real-time, making it easier to understand system status at a glance.
+- **Regularly Review and Adjust**: Continuously review monitoring data and adjust thresholds, alerts, and monitored metrics as needed.
+
+## Related Tools
+
+A variety of tools are available to assist with monitoring and observability. Here are some of the most popular ones:
+
+### Prometheus
+
+Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It collects metrics from configured targets at given intervals, evaluates rule expressions, and displays results. Its powerful query language (PromQL) allows for flexible and accurate metric querying.
+
+### Grafana
+
+Grafana is an open-source platform for monitoring and observability that enables you to query, visualize, alert on, and understand your metrics no matter where they are stored. It integrates seamlessly with Prometheus and many other data sources, providing rich visualizations and interactive dashboards.
+
+### Alertmanager
+
+Alertmanager is a component of the Prometheus ecosystem that handles alerts sent by Prometheus. It manages alerts, deduplicates, groups, and routes them to the correct receiver integrations, like email, Slack, or PagerDuty.
+
+### Datadog
+
+Datadog is a monitoring and analytics platform for cloud-scale applications. It provides end-to-end visibility across your infrastructure, applications, and logs, offering powerful dashboards, alerts, and collaboration tools to help teams quickly detect and resolve issues.
+
+### Statsd
+
+Statsd is a network daemon that listens for statistics, such as counters and timers, sent over UDP or TCP, and sends aggregates to one or more pluggable backend services (e.g., Graphite). It's often used to collect metrics from applications in real-time.
+
+### Zabbix
+
+Zabbix is an open-source monitoring software tool for diverse IT components, including networks, servers, virtual machines, and cloud services. It provides monitoring metrics, network discovery, and customizable alerts.
+
+## Learning Resources
+
+### Books
+- [Prometheus: Up & Running: Infrastructure and Application Performance Monitoring](https://www.amazon.com/Prometheus-Infrastructure-Application-Performance-Monitoring/dp/1492034142) by Brian Brazil.
+- [The Art of Monitoring](https://www.amazon.com/Art-Monitoring-James-Turnbull-ebook/dp/B01GU387MS) by James Turnbull.
+
+### Miscellaneous
+- [Getting Started with Prometheus and Grafana](https://www.youtube.com/watch?v=h4Sl21AKiDg)
+- [Setting Up Alertmanager](https://prometheus.io/docs/alerting/latest/alertmanager/)
+- [Comprehensive Guide to Using Datadog](https://www.datadoghq.com/blog/datadog-tutorial/)
+