Observability refers to the ability to assess a system's internal state by examining its outputs. In modern software development, it plays a crucial role in ensuring robust and high-performing systems by enabling detailed performance tracking, monitoring, and optimization.
Key practices like Performance Tracing and Performance Tuning provide insights into a system's performance and areas for improvement. Observability tools like Jaeger for tracing and ASIC Monitoring for hardware-level performance monitoring are integral to observability.
The RAIL Model and Performance Profiling help optimize user experience and identify performance bottlenecks. This shift from performance testing to Performance Engineering emphasizes continuous improvement and proactive issue resolution, ensuring systems are efficient, resilient, and high-performing.
What is Observability?
Observability is a way to get insights into the whole infrastructure. It is essential for the operations team. It means assembling all fragments from logs and monitoring tools and organizing them in such a way that gives actionable knowledge of the whole environment, thus creating insight. It is a combination of multiple items to create a deep understanding of the actual health, real issues, and what should be done to improve the environment and troubleshoot at a root level.
A tool that enables end users, administrators and organizations to gauge and evaluate the performance of a given system. Click to explore about, Performance Monitoring Tools
This means the service can explain any questions about what is happening inside the system just by observing the outside of the system without entering a new code to answer further questions. Software is enhancing exponentially extra complexity. It is a term from the control method. It is a test of how well the inner states of a system can be assumed by knowledge of its external outputs. The Observability and controllability of a system are numerical duals. It might mean various points to various characters. For unusual, it's about logs, metrics, and traces. To others, it's the first wine of monitoring in a fresh container.
- Creating and operating an extra-visible system
- A system that can explain itself without the need to deploy new code
- Understanding connections between parts of your background or System
- No complex system is always healthy
- Categorized systems are pathologically unpredictable
Observability platforms enable organizations to monitor the overall health of their dynamic system. By utilizing system performance metrics and user experience data, teams can ensure systems are continuously optimized. Self-regulating systems and metrics workflows are integral to maintaining performance standards, allowing teams to focus on critical debugging tasks. These systems can provide critical debugging data and system performance debugging without manual intervention, ensuring a faster response to emerging issues.
Monitoring vs Observability
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Aspect |
Monitoring |
Observability |
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Approach |
Pre-configured dashboards to alert on expected performance issues. |
Instrumented environments provide complete data for flexible exploration. |
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Assumption |
Assumes the ability to predict potential problems. |
Enables discovery of unexpected issues by exploring data and tracing root causes. |
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Environment |
Works well for static, predictable environments. |
Best suited for dynamic and complex cloud-native environments. |
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Data Focus |
Relies primarily on predefined metrics and alerts. |
Expands beyond logs, metrics, and traces to include metadata, user behaviour, and network mapping. |
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Scope |
Limited to predefined scenarios and performance expectations. |
Covers a broader scope with access to code-level details and system context. |
A Platform for Monitoring, Logging, Tracing and Visualization of Big Data Cluster and Kubernetes with ML and Deep learning. Click to explore, Observability for Kubernetes
Why is observability important?
In enterprise environments, observability helps cross-functional teams understand and answer specific questions about what’s happening in highly distributed systems. Observability lets you understand what is slow or broken and what you must do to improve performance. With an observability solution, teams can receive alerts about issues and proactively resolve them before they impact users.
Site Reliability Engineering Approach
Site reliability engineering (SRE) is Google's way of service management, where software engineers work production systems using a software engineering approach. It's explicit that Google is different, and they usually need to catch software bugs and errors in various non-conventional ways.
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Site Reliability engineering reliable operating system and infrastructure scale.
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Define metrics that matter most to the business, such as typical values for those metrics, and plan a reaction if the value isn't met.
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Service level indicator, service level objectives, service level agreement
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RED: The acronym stands for Rate, Errors, and Duration. These are request-scoped, not resource-scoped, as the USE method is. Duration is explicitly taken to mean distributions, not averages.
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The increase means time to failure (MTTF), and the decrease means time to repair (MTTR)
Service level objective (SLO)
There is usually difficulty in using Service Level Agreement (SLA) and Service Level Objective (SLO). Certain SLOs (Service Level Objectives) are usually meant to define the precise, measurable targets that a service must meet to satisfy the expectations outlined in the SLA (Service Level Agreement). These objectives provide specific criteria for evaluating service performance, such as availability, throughput, repetition, response time, or quality. By establishing clear SLOs, organizations can effectively measure and manage service levels, ensuring that they adhere to agreed-upon standards and maintain a high level of service quality. The service demanded by the provider and the client varies depending on the service's needs, resources, and budget.
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NO SLO < Good SLO < Perfect SLO
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Pick an objective and iterate
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Capture a set of events and use a window and target percentage of 99.9% of good events in the last 30 days.
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A good SLO is barely user-happy.
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Determine an error budget that allows failure in the tradeoff to prevent progress and innovation.
What are the Observability Pillars?
Observability in software systems is typically defined by three main pillars: Logging, Metrics, and Tracing. Each plays a crucial role in understanding system behaviour, diagnosing issues, and ensuring performance.
Logging
Definition: Logging refers to the practice of recording events that occur within a system, including errors, warnings, and informational messages. Each log entry captures details about an event that occurred at a specific time, aiding in monitoring and debugging processes.
Types of Logs:
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Plain Logs: Free-form text logs that are easy to generate but may lack structure.
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Structured Logs: Typically formatted in JSON or other structured formats, making them easier to parse and analyze.
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Use logging libraries to standardize log entries.
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Aggregate logs to manage volume effectively and avoid overload.
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Store logs in persistent data stores (e.g., Elasticsearch) for long-term access and analysis.
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Utilize visualization tools like Kibana to query and visualize log data.
- Protobuf logs, MySQL bin logs for replication, system journal logs, and BSD firewall logs.
Metrics
Definition: Metrics are numerical representations of data that describe the behaviour of components or services over time. They provide insights into system performance and health.
Characteristics:
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Metrics are typically aggregated over time to identify trends and anomalies.
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They help understand standard system performance metrics such as response times, error rates, and resource utilization.
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Most programming libraries support metric collection.
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Java metrics classes can push data to designated metrics endpoints for monitoring.
Tracing
Definition: Tracing involves capturing the flow of requests through various system components. It helps understand how different services interact during a request's execution.
Key Features:
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Each trace is associated with a global identifier passed along through various services.
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Distributed tracing tools like Jaeger or Zipkin visualize the request flow, allowing developers to pinpoint bottlenecks or failures.
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OpenTelemetry provides a standardized approach for implementing tracing across different programming languages.
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Tracing can reveal performance issues at various points in the service architecture.
A non-functional type of testing that measures the performance of an application or software under a certain workload. Click to explore, Performance Testing Tools
How does Observability work?
To attain the ultimate state of observability, consider the following -Logging Process
Logging is a mechanism to collect logs from various input sources. Usually, logs are in raw format. To gain real insights, parse these logs and apply queries to gain insights quicker. Usually, logs are sent to an output tool that organizes the logs. Logging defines what to log, how it should be logged, and how logs are shipped to an external system for aggregating. Debug mode is disabled for better logging as logging everything based on a debug level will become expensive to manage, creating extra false positives, not-so-important alarms, and more difficulty in getting important data. Debug should be the default only while troubleshooting, not in real production environments
Monitoring Process
Monitoring is an activity performed by DevOps guys. It’s simply observing the state of an infrastructure/environment over a period of time. Monitor for three reasons:
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Detecting Problems— usually by alerts or looking at issues on dashboards
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Finding Resolution for Problem— finding root causes of issues and troubleshooting
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Continuous Improvement— reporting and documenting
Tracing Working
Trace the calls between various applications. Priorities are defined as different service failures, and the one with the highest priority is caught and alerted immediately. Tracing shows what either happened in the past or what is happening at present. Tracing is a very important piece of it proactively. It also suggests what code we can add to the service to provide better insights into the application. There should be transparent visibility end to end for all transactions happening in the environment.
How Alerting Works?
Alerting helps define how to notify the Ops guys when an event occurs. It is very important to remove false positives. There should be -
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Alert only to important events
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Self-healing infrastructure
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Enable analytics when something has been done manually many times
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Enable Automation to fix the problems
Monitoring of applications is the biggest aspect, As the speed and the accuracy expected is very high to address the issue on time. Click to explore about, Application Performance Management in DevOps
Benefits of Observability
Observability provides significant benefits for IT teams, organizations, and end users. Key use cases include:
Application Performance Monitoring: Accelerates issue resolution in cloud-native and microservices environments, improving efficiency and innovation.
DevSecOps and SRE: Observability is embedded in application design, helping teams build secure, resilient applications during the software delivery lifecycle.
Infrastructure, Cloud, and Kubernetes Monitoring: Enhances monitoring of on-premises, cloud, and Kubernetes environments, improving uptime, performance, and issue resolution.
End-User Experience: Improves customer satisfaction by identifying and addressing issues before users notice, optimizing the user experience with real-time feedback.
Business Analytics: Combines application performance with business context to optimize conversion, ensure software meets business goals, and meet SLAs.
DevOps Automation: Streamlines testing and CI/CD processes, enabling faster, higher-quality code releases, and improving team collaboration.
Best Practices of Observability
Logging is a mechanism for collecting logs from various input sources. Usually, logs are in raw format. To gain real insights, parse these logs and apply queries to gain insights quicker. Usually, logs are sent to an output tool that organizes them. Logging defines what to log, how it should be logged, and how logs are shipped to an external system for aggregating.
Debug mode is disabled for better logging as logging everything based on a debug level will become expensive to manage, creating extra false positives, not-so-important alarms, and more difficulty in getting important data. Debug should be the default only while troubleshooting, not in real production environments
Following Best Practices while allowing Observability for applications running in the environment -
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Do not Monitor everything.
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Monitor only things that are essential to fix when they fail.
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Do not put Alerts on everything.
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Put alerting only for critical events.
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Do not store all logs and all data.
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Store only logs that give insights about critical events.
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Don't use default graphs.
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Create a custom graph according to customer needs.
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Create alerts based on Prometheus or Grafana metrics of running applications in an environment.
List of Top Observability Tools
- Logging
- Fluentd
- Logstash
Various Monitoring Tools
- Prometheus
- Grafana
Deployment tools
- Containers and Orchestration tools such as Docker and Kubernetes.
- Log Aggregator
- AWS Cloudwatch
Alerting Tools
- Slack
- Pagerduty
More Ways to Explore Us
Observability vs Monitoring
Docker Container Architecture and Monitoring
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