Observability: The New Frontier?

Published in

Globant

10 min readJul 26, 2023

Introduction

The scope of this article is to provide a brief introduction to Observability, an initial approach to concepts, and best practices. Recommendations about tools that can be used are mentioned in the article to provide guidance when looking at the tools to be used to complete the steps to achieve Observability in your organization.

Observability, as part of the Site Reliability Engineering (SRE) framework, can be defined as measuring a system’s internal state and operational characteristics that can be inferred from its external outputs. It encompasses the ability to understand and reason about a system’s behavior based on the information available from monitoring, instrumentation, and logging. By adopting practices that promote observability, SRE teams can gain insights into the system’s health, performance, and interactions, enabling them to identify, analyze, and resolve issues, having as an outcome an optimized system behavior. Ultimately, observability empowers SREs to maintain and improve the reliability and performance of complex systems.

Understanding and Implementing Observability

Understanding and implementing observability involves a combination of principles, practices, and tools. There are several steps you need to consider, as follows:

Define your objectives for implementing observability: Clearly articulate the specific challenges or goals you aim to address through observability, such as enhancing system performance, reducing downtime, or improving troubleshooting capabilities. Having well-defined objectives will guide your implementation approach.
Identify critical metrics and logs: Determine the key metrics, logs, and data points that are essential for monitoring and analyzing your system’s behavior. Consider factors like performance, error rates, latency, throughput, resource utilization, and user experience. Each system will have its own relevant metrics, so it’s important to identify the ones aligned with your objectives.
Instrument your system: Integrate monitoring libraries or agents into your applications, services, and infrastructure components to capture the identified metrics and logs. Instrumentation allows you to collect data at different levels, including code-level metrics, system-level metrics, and external dependencies.
Establish centralized data collection and storage: Set up a centralized platform or tool to collect, store, and analyze observability data. Choose suitable options like time-series databases (e.g., Prometheus, InfluxDB, Graphite) for metrics and log aggregation tools (e.g., Elasticsearch, Splunk) for logs. Ensure the chosen platform can handle the volume of data generated by your system.
Implement visualization and analysis: Utilize visualization tools (e.g., Grafana, Kibana, Datadog) to create informative dashboards that help you visualize the collected data, identify patterns, and detect anomalies. Develop meaningful visualizations aligned with your objectives to gain insights into system behavior, performance trends, and potential issues.
Enable alerting and notifications: Configure alerting mechanisms based on observed metrics and thresholds. Set up alerts for critical metrics or patterns indicating anomalies or potential problems. Employ tools like Prometheus Alertmanager, and PagerDuty to send notifications to relevant teams or individuals when alerts are triggered. Ensure the alerts are actionable and reach the appropriate recipients promptly.
Conduct proactive monitoring and analysis: Regularly review and analyze observability data to gain insights into system behavior and performance. Look for patterns, trends, and anomalies that may indicate areas for improvement or potential issues. Proactively monitor to identify bottlenecks, capacity constraints, or performance degradation before they impact users or critical workflows.
Continuously iterate and improve: Observability is an iterative process requiring continuous improvement. Engage SRE, development, and operations teams in analyzing the collected data, sharing insights, and implementing corrective actions. Perform post-incident reviews and utilize observability data for root cause analysis, identifying areas to enhance, and implementing preventive measures.
Embrace automation and intelligent systems: Leverage observability data to automate monitoring, alerting, and incident response. Implement automated processes and self-healing mechanisms that can take corrective actions based on observed patterns or anomalies. Employ machine learning and anomaly detection techniques to identify abnormal behavior and trigger automated responses, minimizing manual intervention.
Encourage an observability culture: Promote an observability culture within your organization. Educate teams about the significance of observability and provide training on tools and practices. Encourage collaboration between teams and facilitate knowledge sharing on observability insights and best practices.

Remember that implementing observability is an ongoing practice, so continuously evaluate and refine your observability strategy to align with evolving system requirements and business objectives.

To understand the concept of Observability, we need to talk about three key areas: Metrics, Traces, and Logs.

Metrics

Metrics play a vital role in observability by providing measurable data that represent different systems’ behavior facets, performance, and health. Here are alternative phrasings for describing metrics in observability:

Performance Indicators: These indicators focus on quantifying the performance characteristics of the system, including response time, latency, throughput, and error rates. They offer insights into the system’s efficiency, potential bottlenecks, and performance-related issues.
Availability Measures: Availability measures track the system’s uptime and downtime, reflecting its reliability and accessibility to users. These measures encompass uptime percentage, mean time between failures (MTBF), mean time to repair (MTTR), and adherence to service-level agreements (SLAs).
Resource Utilization Statistics: The statistics on resource utilization measure the efficiency of system resources, including CPU, memory, disk space, network bandwidth, and I/O operations. By analyzing these statistics, we can identify any limitations or potential overuse and plan for capacity accordingly.
Error and Exception Metrics: These metrics capture error rates, exceptions, and fault conditions that occur within the system. They provide insights into the frequency, types, and errors impact, helping to determine the problematic areas.
User Experience Indicators: User experience indicators measure user satisfaction and engagement with the system. They include metrics like page load time, conversion rates, click-through rates, bounce rates, and user feedback. These indicators provide valuable insights into how users perceive and interact with the system.
Business Metrics: Business metrics establish and define a connection between system performance and critical business objectives. They encompass revenue, customer acquisition, retention rates, conversion rates, and other performance indicators that can be bound to organizational goals.

Choosing relevant metrics aligned with the system’s objectives is crucial. By collecting and analyzing these metrics, organizations gain knowledge of their system performance, detect anomalies, troubleshoot issues, optimize resource allocation, and make data-informed decisions to improve the system.

Traces

Traces, under observability context, encompass the captured records or data that document the journey of a request or transaction moves via diverse system components. Here are alternative ways to consider as traces in observability:

Request Path Records: Request path records represent the data captured as a request or transaction across different components of a system. These records provide insights into the journey and behavior of individual requests, facilitating analysis and troubleshooting.
Transaction Tracking Data: Transaction tracking data pertains to the information gathered as a request or transaction progresses through diverse system components. This data allows for the visualization and examination of flow, performance, and specific transaction interactions.
Execution Path Documentation: Execution path documentation involves recording the sequence of operations and interactions as a request or transaction moves across system components. This documentation aids in understanding the execution flow, facilitates the identification of bottlenecks, and diagnoses issues.
Traversal Records: Traversal records capture the details of a request or transaction as it traverses different system components. These records provide a comprehensive view, enabling the performance analysis, latency, and dependencies between system elements.
Operation Journey Logs: Operation journey logs document the progression of a request or transaction as it travels through various system components. These logs offer a chronological account of the operations performed, and the behavior observed, facilitating the understanding and troubleshooting of complex system interactions.
Workflow Trace Data: Workflow trace data refers to recorded information about the steps and activities undertaken as a request or transaction progresses through the system’s workflows. This data assists in visualizing and comprehending the end-to-end behavior and performance of specific workflows.

Nowadays, a set of tools and frameworks are available for building distributed tracing solutions. Below are some popular tools:

OpenTelemetry: Observability framework for cloud-native software
Jaeger: Open-source distributed tracing solution
Zipkin: Open-source distributed tracing solution

By data retrieval and analyzing traces, organizations can gain insights into the behavior, performance, and dependencies within their systems, allowing for efficient troubleshooting, optimization, and overall improvement of system performance and reliability.

Logs

In the context of observability, the logs are records or entries containing timestamped events and information about the activities, behaviors, and occurrences within a system. Below are some additional ways to consider as logs in observability:

Event Records: Event records encompass timestamped entries that capture relevant activities, behaviors, and incidents related to a system. These records provide a historical view of events, aiding in understanding system behavior and facilitating troubleshooting.
Activity Logs: Activity logs consist of recorded events and actions occurring within a system, with each entry containing a timestamp. Logs are a valuable source of information for analyzing system behavior, identifying patterns, and diagnosing issues.
Operational Diaries: Operational diaries compile chronological records of events, activities, and behaviors as part of the system. These diaries provide a detailed account of system operations, allowing for retrospective analysis, troubleshooting, and optimization.
Timestamped Event Entries: Timestamped event entries are chronological records that capture specific events, actions, or occurrences within a system. These entries enable the reconstruction of system activities, aiding in root cause analysis, anomaly detection, and performance optimization.
Informational Journaling: Informational journaling involves the practice of recording and documenting events, behaviors, and incidents within a system. These journals serve as a repository of valuable information, facilitating system analysis, problem-solving, and performance evaluation.
Historical System Data: Historical system data refers to recorded events and information about system activities, behaviors, and incidents over time. This data is used as a reference for understanding past system states, analyzing trends, and identifying correlations between events.

By taking advantage of logs, organizations can improve their visibility into the system activities, identify anomalies, track performance, and troubleshoot issues more effectively. The logs are one of the most valuable resources in maintaining system reliability, improving performance, and enabling proactive monitoring analysis.

Maturity Levels

Observability maturity level refers to the progressive development and sophistication of observability practices within an organization. Here is a description of the observability maturity levels and a description of each one of them:

Based on the maturity level, and the deeper the processes are in place, the more IT reliability will increase.

IT reliability graph — Image by the author

Typical Approaches to Observability

There are several approaches to achieving observability in a system. Here are three commonly used techniques:

Monitoring: Monitoring refers to processes used to gather and analyze system metrics, logs, and events to gain insight into how the system operates. Includes configuring monitoring tools and agents to collect data from different system parts, including infrastructure, applications, and services. By monitoring, SRE teams can keep track of performance indicators, identify anomalies, and receive notifications or alerts when problems arise.
Instrumentation: Instrumentation involves adding code or sensors to the system’s components to gather specific data and metrics, including logging statements, distributed tracing, or custom metrics to capture relevant information about the system’s internal operations. By instrumenting different parts of the system, SRE teams can gain fine-grained visibility into its behavior, identify bottlenecks, track request flows, and understand the impact of changes on system performance.
Distributed Tracing: Distributed tracing is a technique used to trace requests as they traverse across various components of a distributed system. It involves adding unique identifiers to requests and collecting data as they pass through different services and microservices. This technique allows SREs to visualize the path and performance of individual requests, identify latency or error hotspots, and understand the end-to-end behavior of the system. Distributed tracing can be particularly useful in complex, microservices-based architectures.

These methods can be used together or in combination to achieve a comprehensive level of observability in a system. By employing effective monitoring, instrumentation, and distributed tracing techniques, SRE teams can gain insights into the system’s behavior, troubleshoot issues, optimize performance, and ensure the overall reliability of the systems.

Conclusions

Why should I implement observability?

As part of moving into SRE, observability practices used in management and system monitoring are crucial for several reasons and benefits.

Early Issue Detection: Observability enables the proactive detection of system issues, bottlenecks, and anomalies before they escalate into critical problems. Using monitoring tools and analyzing metrics, logs, and events helps organizations can identify patterns, trends, and potential issues, allowing them to take preventive measures to mitigate risks and maintain system stability.

Efficient Troubleshooting: Observability provides valuable insights into system behavior, facilitating efficient troubleshooting and root cause analysis. With comprehensive visibility into various system components, SRE teams can quickly identify the source of issues, isolate them, and minimize downtime by addressing the root causes effectively.

Performance Optimization: By closely monitoring system metrics and behavior, observability enables organizations to optimize performance and resource allocation. It allows for identifying performance bottlenecks, optimizing configurations, and making data-driven decisions to improve overall system efficiency and user experience.

Enhanced Reliability and Resilience: Observability contributes to building reliable and resilient systems. By continuously monitoring and analyzing system performance, organizations can proactively identify potential failures, implement appropriate measures and improve fault tolerance. This helps in maintaining high availability, reducing service downtime, and giving a consistent user experience.

Data-Driven/Decision-Making: Observability provides organizations with valuable data and insights that can provide invaluable information to decision-making processes. By analyzing system behavior, user interactions, and performance trends, organizations can make data-driven decisions about capacity planning, infrastructure investments, feature prioritization, and other critical aspects of system management.

Overall, observability allows organizations to have comprehensive knowledge of their systems and processes, detect and resolve issues proactively, optimize performance, and make decisions based on data. By investing in observability practices, organizations can enhance system reliability, and agility, improving user satisfaction in today’s complex and dynamic technological landscape.