auto scaling observability

Observability vs Monitoring

Observability vs Monitoring

In today's fast-paced digital landscape, where complex systems and applications drive businesses, it's crucial to have a clear understanding of observability and monitoring. These two terms are often used interchangeably, but they represent distinct concepts in the realm of system management and troubleshooting. In this blog post, we will delve into the differences between observability and monitoring, shedding light on their unique features and benefits.

What is Observability? Observability refers to the ability to gain insight into the internal state of a system through its external outputs. It focuses on understanding the behavior and performance of a system from an external perspective, without requiring deep knowledge of its internal workings. Observability provides a holistic view of the system, enabling comprehensive analysis and troubleshooting.

The Essence of Monitoring: Monitoring, on the other hand, involves the systematic collection and analysis of various metrics and data points within a system. It primarily focuses on tracking predefined performance indicators, such as CPU usage, memory utilization, and network latency. Monitoring provides real-time data and alerts to ensure that system health is maintained and potential issues are promptly identified.

Data Collection and Analysis: Observability emphasizes comprehensive data collection and analysis, aiming to capture the entire system's behavior, including its interactions, dependencies, and emergent properties. Monitoring, however, focuses on specific metrics and predefined thresholds, often using predefined agents, plugins, or monitoring tools.

Contextual Understanding: Observability aims to provide a contextual understanding of the system's behavior, allowing engineers to trace the flow of data and understand the cause and effect of different components. Monitoring, while offering real-time insights, lacks the contextual understanding provided by observability.

Reactive vs Proactive: Monitoring is primarily reactive, alerting engineers when predefined thresholds are exceeded or when specific events occur. Observability, on the other hand, enables a proactive approach, empowering engineers to explore and investigate the system's behavior even before issues arise.

Observability and monitoring are both crucial elements in system management, but they have distinct focuses and approaches. Observability provides a holistic and contextual understanding of the system's behavior, allowing for comprehensive analysis and proactive troubleshooting. Monitoring, on the other hand, offers real-time data and alerts based on predefined metrics, ensuring system health is maintained. Understanding the differences between these two concepts is vital for effectively managing and optimizing complex systems.

Highlights: Observability vs Monitoring

Understanding Monitoring & Observability

A: Understanding Monitoring: Monitoring collects data and metrics from a system to track its health and performance. It involves setting up various tools and agents that continuously observe and report on predefined parameters. These parameters include resource utilization, response times, and error rates. Monitoring provides real-time insights into the system’s behavior and helps identify potential issues or bottlenecks.

B: Unveiling Observability: Observability goes beyond traditional monitoring by understanding the system’s internal state and cause-effect relationships. It aims to provide a holistic view of the system’s behavior, even in unexpected scenarios. Observability encompasses three main pillars: logs, metrics, and traces. Logs capture detailed events and activities, metrics quantify system behavior over time, and traces provide end-to-end transaction monitoring. By combining these pillars, observability enables deep system introspection and efficient troubleshooting.

C: The Power of Contextual Insights: One of the key advantages of observability is its ability to provide contextual insights. Traditional monitoring may alert you when a specific metric exceeds a threshold, but it often lacks the necessary context to debug complex issues. With its comprehensive data collection and correlation capabilities, Observability allows engineers to understand the context surrounding a problem. Contextual insights help in root cause analysis, reducing mean time to resolution and improving overall system reliability.

D: The Role of Automation: Automation plays a crucial role in monitoring and observability. In monitoring, automation can help set up alerts, generate reports, and scale the monitoring infrastructure. On the other hand, observability requires automated instrumentation and data collection to handle the vast amount of information generated by modern systems. Automation enables engineers to focus on analyzing insights rather than spending excessive time on data collection and processing.

**Observability: The First Steps**

The first step towards achieving modern observability is to gather metrics, traces, and logs. From the collected data points, observability aims to generate valuable outcomes for decision-making. The decision-making process goes beyond resolving problems as they arise. Next-generation observability goes beyond application remediation, focusing on creating business value to help companies achieve their operational goals. This decision-making process can be enhanced by incorporating user experience, topology, and security data.

**Observability Platform**

A full-stack observability platform monitors every monitored host in your environment. Depending on the technologies used, an average of 500 metrics are generated per computational node. AWS, Azure, Kubernetes, and VMware Tanzu are some platforms that use observability to collect important key performance metrics for services and real-user monitored applications. 

Within a microservices environment, dozens, if not hundreds, of microservices call one another. Distributed tracing can help you understand how the different services connect and how your requests flow. 

**Pillars of Observability**

The three pillars of observability form a strong foundation for making data-driven decisions, but there are opportunities to extend observability. User experience and security details must be considered to gain a deeper understanding. A holistic, context-driven approach to advanced observability enables proactively addressing potential problems before they arise.

**The Role of Monitoring**

To understand the difference between observability and monitoring, we need first to discuss the role of monitoring. Monitoring is the evaluation that helps identify the most practical and efficient use of resources. So, the big question I put to you is what to monitor. This is the first step to preparing a monitoring strategy.

To fully understand if monitoring is enough or if you need to move to an observability platform, ask yourself a couple of questions. Firstly, consider what you should be monitoring, why you should be monitoring it, and how you should be monitoring it. 

Observability & Service Mesh

What is a Cloud Service Mesh?

A Cloud Service Mesh is a design pattern that helps manage and secure microservices interactions. Essentially, it acts as a dedicated layer for controlling the network traffic between microservices. By introducing a service mesh, developers can offload much of the responsibility for service communication from the application code itself, making the entire system more resilient and easier to manage.

### Key Benefits of Implementing a Cloud Service Mesh

#### Enhanced Security

One of the primary advantages of a Cloud Service Mesh is the enhanced security it offers. With features like mutual TLS (mTLS) for encrypting communications between services, a service mesh ensures that data is protected as it travels through the network. This is particularly important in a multi-cloud or hybrid cloud environment where services might be spread across different platforms.

#### Improved Observability

Observability is another critical benefit. A Cloud Service Mesh provides granular insights into service performance, helping developers identify and troubleshoot issues quickly. Metrics, logs, and traces are collected systematically, offering a comprehensive view of the entire microservices ecosystem.

#### Traffic Management

Managing traffic between services becomes significantly easier with a Cloud Service Mesh. Features like load balancing, traffic splitting, and failover mechanisms are built-in, ensuring that service-to-service communication remains efficient and reliable. This is particularly beneficial for applications requiring high availability and low latency.

### Popular Cloud Service Mesh Solutions

Several solutions have emerged as leaders in the Cloud Service Mesh space. Istio, Linkerd, and Consul are among the most popular options, each offering unique features and benefits. Istio, for example, is known for its robust policy enforcement and telemetry capabilities, while Linkerd is praised for its simplicity and performance. Consul, on the other hand, excels in multi-cloud environments, providing seamless service discovery and configuration.

### Challenges and Considerations

While the benefits are compelling, implementing a Cloud Service Mesh is not without its challenges. Complexity can be a significant hurdle, particularly for organizations new to microservices architecture. The additional layer of infrastructure requires careful planning and management. Moreover, there is a learning curve associated with configuring and maintaining a service mesh, which can impact development timelines.

Example Product: Cisco AppDynamics

### Real-Time Monitoring: Keeping an Eye on Your Applications

One of the standout features of Cisco AppDynamics is its real-time monitoring capabilities. By continuously tracking the performance of your applications, AppDynamics provides instant insights into any issues that may arise. This allows businesses to quickly identify and address performance bottlenecks, ensuring that their applications remain responsive and reliable. Whether it’s tracking transaction times, monitoring server health, or keeping an eye on user interactions, Cisco AppDynamics provides a comprehensive view of your application’s performance.

### Advanced Analytics: Turning Data into Actionable Insights

Data is the lifeblood of modern businesses, and Cisco AppDynamics excels at turning raw data into actionable insights. With its advanced analytics engine, AppDynamics can identify patterns, trends, and anomalies in your application’s performance data. This empowers businesses to make informed decisions, optimize their applications, and proactively address potential issues before they impact users. From root cause analysis to predictive analytics, Cisco AppDynamics provides the tools you need to stay ahead of the curve.

### Comprehensive Diagnostics: Troubleshooting Made Easy

When performance issues do arise, Cisco AppDynamics makes troubleshooting a breeze. Its comprehensive diagnostics capabilities allow you to drill down into every aspect of your application’s performance. Whether it’s identifying slow database queries, pinpointing code-level issues, or tracking down problematic user interactions, AppDynamics provides the detailed information you need to resolve issues quickly and efficiently. This not only minimizes downtime but also ensures a seamless user experience.

### Enhancing User Experiences: The Ultimate Goal

At the end of the day, the ultimate goal of any application is to provide a positive user experience. Cisco AppDynamics helps businesses achieve this by ensuring that their applications are always performing at their best. By providing real-time monitoring, advanced analytics, and comprehensive diagnostics, AppDynamics enables businesses to deliver fast, reliable, and engaging applications that keep users coming back for more. In a competitive digital landscape, this can be the difference between success and failure.

Google Cloud Monitoring

Example: What is Ops Agent?

Ops Agent is a lightweight, flexible monitoring agent explicitly designed for Compute Engine instances. It allows you to collect and analyze essential metrics and logs from your virtual machines, providing valuable insights into your infrastructure’s health, performance, and security.

To start monitoring your Compute Engine instance with Ops Agent, you must install and configure it properly. The installation process is straightforward and can be done through the Google Cloud Console or the command line. Once installed, you can configure Ops Agent to collect specific metrics and logs based on your requirements.

Ops Agent offers a wide range of metrics and logs that can be collected and monitored. These include system-level metrics like CPU and memory usage, network traffic, disk I/O, and more. Additionally, Ops Agent allows you to gather application-specific metrics and logs, providing deep insights into the performance and behavior of your applications running on the Compute Engine instance.

Options: Open source or commercial

Knowing this lets you move into the different tools and platforms available. Some of these tools will be open source, and others commercial. When evaluating these tools, one word of caution: does each tool work in a silo, or can it be used across technical domains? Silos are breaking agility in every form of technology.

Related: For pre-information, you may find the following posts helpful:

  1. Microservices Observability
  2. Auto Scaling Observability
  3. Network Visibility
  4. WAN Monitoring
  5. Distributed Systems Observability
  6. Prometheus Monitoring
  7. Correlate Disparate Data Points
  8. Segment Routing

Observability vs Monitoring

Monitoring and Distributed Systems

By utilizing distributed architectures, the cloud native ecosystem allows organizations to build scalable, resilient, and novel software architectures. However, the ever-changing nature of distributed systems means that previous approaches to monitoring can no longer keep up. The introduction of containers made the cloud flexible and empowered distributed systems.

Nevertheless, the ever-changing nature of these systems can cause them to fail in many ways. Distributed systems are inherently complex, and, as systems theorist Richard Cook notes, “Complex systems are intrinsically hazardous systems.”

Cloud-native systems require a new approach to monitoring, one that is open-source compatible, scalable, reliable, and able to control massive data growth. However, cloud-native monitoring can’t exist in a vacuum; it must be part of a broader observability strategy.

**Gaining Observability**

Key Features of Observability:

1. High-dimensional data collection: Observability involves collecting a wide variety of data from different system layers, including metrics, logs, traces, and events. This comprehensive data collection provides a holistic view of the system’s behavior.

2. Distributed tracing: Observability allows tracing requests as they flow through a distributed system, enabling engineers to understand the path and identify performance bottlenecks or errors.

3. Contextual understanding: Observability emphasizes capturing contextual information alongside the data, enabling teams to correlate events and understand the impact of changes or incidents.

Benefits of Observability:

1. Faster troubleshooting: By providing detailed insights into system behavior, observability helps teams quickly identify and resolve issues, minimizing downtime and improving system reliability.

2. Proactive monitoring: Observability allows teams to detect potential problems before they become critical, enabling proactive measures to prevent service disruptions.

3. Improved collaboration: With observability, different teams, such as developers, operations, and support, can have a shared understanding of the system’s behavior, leading to improved collaboration and faster incident response.

**Gaining Monitoring**

On the other hand, monitoring focuses on collecting and analyzing metrics to assess a system’s health and performance. It involves setting up predefined thresholds or rules and generating alerts based on specific conditions.

Key Features of Monitoring:

1. Metric-driven analysis: Monitoring relies on predefined metrics collected and analyzed to measure system performance, such as CPU usage, memory consumption, response time, or error rates.

2. Alerting and notifications: Monitoring systems generate alerts and notifications when predefined thresholds or rules are violated, enabling teams to take immediate action.

3. Historical analysis: Monitoring systems provide historical data, allowing teams to analyze trends, identify patterns, and make informed decisions based on past performance.

Benefits of Monitoring:

1. Performance optimization: Monitoring helps identify performance bottlenecks and inefficiencies within a system, enabling teams to optimize resources and improve overall system performance.

2. Capacity planning: By monitoring resource utilization and workload patterns, teams can accurately plan for future growth and ensure sufficient resources are available to meet demand.

3. Compliance and SLA enforcement: Monitoring systems help organizations meet compliance requirements and enforce service level agreements (SLAs) by tracking and reporting on key metrics.

Observability & Monitoring: A Unified Approach

While observability and monitoring differ in their approaches and focus, they are not mutually exclusive. When used together, they complement each other and provide a more comprehensive understanding of system behavior.

Observability enables teams to gain deep insights into system behavior, understand complex interactions, and troubleshoot issues effectively. Conversely, monitoring provides a systematic approach to tracking predefined metrics, generating alerts, and ensuring the system meets performance requirements.

Combining observability and monitoring can help organizations create a robust system monitoring and management strategy. This integrated approach empowers teams to quickly detect, diagnose, and resolve issues, improving system reliability, performance, and customer satisfaction.

Application Latency & Cloud Trace

A: – Latency, in simple terms, refers to the delay between sending a request and receiving a response. It can be caused by various factors, such as network congestion, server processing time, or inefficient code execution. Understanding the different components contributing to latency is essential for optimizing application performance.

B: – Google Cloud Trace is a powerful diagnostic tool provided by Google Cloud Platform. It allows developers to visualize and analyze latency data for their applications. By instrumenting code and capturing trace data, developers gain valuable insights into the performance bottlenecks and can take proactive measures to improve latency.

C: – To start capturing traces in your application, you need to integrate the Cloud Trace API into your codebase. Once integrated, Cloud Trace collects detailed latency data, including information about the various services and resources used to process a request. This data can then be visualized and analyzed through the user-friendly Cloud Trace interface.

The Starting Point: Observability vs Monitoring

You need to measure and gather the correct event information in your environment, which will be done with several tools. This will let you know what is affecting your application performance and infrastructure. As a good starting point, there are four golden signals for Latency, saturation, traffic, and errors. These are Google’s Four Golden Signals. The four most important metrics to keep track of are: 

      1. Latency: How long it takes to serve a request
      2. Traffic: The number of requests being made.
      3. Errors: The rate of failing requests. 
      4. Saturation: How utilized the service is.

So now we have some guidance on what to monitor and let us apply this to Kubernetes to, for example, let’s say, a frontend web service that is part of a tiered application, we would be looking at the following:

      1. How many requests is the front end processing at a particular point in time,
      2. How many 500 errors are users of the service received, and 
      3. Does the request overutilize the service?

We already know that monitoring is a form of evaluation that helps identify the most practical and efficient use of resources. With monitoring, we observe and check the progress or quality of something over time. Within this, we have metrics, logs, and alerts. Each has a different role and purpose.

**Monitoring: The role of metrics**

Metrics are related to some entity and allow you to view how many resources you consume. Metric data consists of numeric values instead of unstructured text, such as documents and web pages. Metric data is typically also a time series, where values or measures are recorded over some time. 

Available bandwidth and latency are examples of such metrics. Understanding baseline values is essential. Without a baseline, you will not know if something is happening outside the norm.

Note: Average Baselines

What are the average baseline values for bandwidth and latency metrics? Are there any fluctuations in these metrics? How do these values rise and fall during normal operations and peak usage? This may change over different days, weeks, and months.

If you notice a rise in these values during normal operations, this would be deemed abnormal and should act as a trigger that something could be wrong and needs to be investigated. Remember that these values should not be gathered as a once-off but can be gathered over time to understand your application and its underlying infrastructure better.

**Monitoring: The role of logs**

Logging is an essential part of troubleshooting application and infrastructure performance. Logs give you additional information about events, which is important for troubleshooting or discovering the root cause of the events. Logs will have much more detail than metrics, so you will need some way to parse the logs or use a log shipper.

A typical log shipper will take these logs from the standard out in a Docker container and ship them to a backend for processing.

Note: Example Log Shipper

FluentD or Logstash has pros and cons. The group can use it here and send it to a backend database, which could be the ELK stack ( Elastic Search). Using this approach, you can add different things to logs before sending them to the backend. For example, you can add GEO IP information. This will add richer information to the logs that can help you troubleshoot.

Understanding VPC Flow Logs

VPC Flow Logs is a feature provided by Google Cloud that captures network traffic metadata within a Virtual Private Cloud (VPC) network. This metadata includes source and destination IP addresses, protocol, port, and more. By enabling VPC Flow Logs, administrators can gain visibility into the network traffic patterns and better understand the communication flow within their infrastructure.

We can leverage data visualization tools to make the analysis more visually appealing and easier to comprehend. Google Cloud provides various options for creating interactive and informative dashboards, such as Data Studio and Cloud Datalab. These dashboards can display network traffic trends, highlight critical metrics, and aid in identifying patterns or anomalies that might require further investigation.

**Monitoring: The role of alerting**

Then we have the alerting, and it would be best to balance how you monitor and what you alert on. So, we know that alerting is not always perfect, and getting the right alerting strategy in place will take time. It’s not a simple day-one installation and requires much effort and cross-team collaboration.

You know that alerting on too much can cause alert fatigue. We are all too familiar with the problems alert fatigue can bring and the tensions it can create in departments.

To minimize this, consider Service Level Objectives (SLOs) for alerts. SLOs are measurable characteristics such as availability, throughput, frequency, and response times. They are the foundation for a reliability stack. Also, it would be best if you considered alert thresholds. If these are too short, you will get a lot of false positives on your alerts. 

Monitoring is not enough.

Even with all of these in place, monitoring is not enough. Due to the sheer complexity of today’s landscape, you need to consider and think differently about the tools you use and how you use the intelligence and data you receive from them to resolve issues before they become incidents.  That monitoring by itself is not enough.

The tool used to monitor is just a tool that probably does not cross technical domains, and different groups of users will administer each tool without a holistic view. The tools alone can take you only half the way through the journey.  Also, what needs to be addressed is the culture and the traditional way of working in silos. A siloed environment can affect the monitoring strategy you want to implement. Here, you can look at an observability platform.

The Foundation of GKE-Native Monitoring

GKE-Native Monitoring builds upon the robust foundation of Prometheus and Stackdriver, providing a seamless integration that simplifies observability within GKE clusters. By harnessing the strengths of these industry-leading monitoring solutions, GKE-Native Monitoring offers a robust and comprehensive monitoring experience.

Under the umbrella of GKE-Native Monitoring, users gain access to a rich set of features designed to enable fine-grained visibility and control. These include customizable dashboards, real-time metrics, alerts, and horizontal pod autoscaling. With these tools, developers and operators can easily monitor the health, performance, and resource utilization of their GKE clusters.

Observability vs Monitoring

When it comes to observability vs. monitoring, we know that monitoring can detect problems and tell you if a system is down, and when your system is UP, Monitoring doesn’t care. Monitoring only cares when there is a problem. The problem has to happen before monitoring takes action. It’s very reactive. So, if everything is working, monitoring doesn’t care.

On the other hand, we have an observability platform, which is a more proactive practice. It’s about what and how your system and services are doing. Observability lets you improve your insight into how complex systems work and quickly get to the root cause of any problem, known or unknown.

Observability is best suited for interrogating systems to explicitly discover the source of any problem, along any dimension or combination of dimensions, without first predicting. This is a proactive approach.

## Pillars of Observability

This is achieved by combining logs, metrics, and traces. So, we need data collection, storage, and analysis across these domains while also being able to perform alerting on what matters most. Let’s say you want to draw correlations between units like TCP/IP packets and HTTP errors experienced by your app.

The Observability platform pulls context from different sources of information, such as logs, metrics, events, and traces, into one central context. Distributed tracing adds a lot of value here.

Also, when everything is placed into one context, you can quickly switch between the necessary views to troubleshoot the root cause. Viewing these telemetry sources with one single pane of glass is an excellent key component of any observability system. 

## Known and Unknown vs Unknown and Unknown 

Monitoring automatically reports whether known failure conditions are occurring or are about to occur. In other words, it is optimized for reporting on unknown conditions about known failure modes, which are referred to as known unknowns. In contrast, Observability is centered around discovering if and why previously unknown failure modes may be occurring, in other words, to find unknown unknowns.

The monitoring-based approach of metrics and dashboards is an investigative practice that relies on humans’ experience and intuition to detect and understand system issues. This is okay for a simple legacy system that fails in predictable ways, but the instinctual technique falls short for modern systems that fail in unpredictable ways.

With modern applications, the complexity and scale of their underlying systems quickly make that approach unattainable, and we can’t rely on hunches. Observability tools differ from traditional monitoring tools because they enable engineers to investigate any system, no matter how complex. You don’t need to react to a hunch or have intimate system knowledge to generate a hunch.

Monitoring vs Observability: Working together?

Monitoring helps engineers understand infrastructure concerns, while observability helps engineers understand software concerns. So, Observability and Monitoring can work together. First, the infrastructure does not change too often, and when it fails, it will fail more predictably. So, we can use monitoring here.

This is compared to software system states that change daily and are unpredictable. Observability fits this purpose. The conditions that affect infrastructure health change infrequently and are relatively more straightforward to predict. 

We have several well-established practices to expect, such as capacity planning and the ability to remediate automatically (e.g., auto-scaling in a Kubernetes environment). All of these can be used to tackle these types of known issues. 

Monitoring and infrastructure problems

Due to its relatively predictable and slowly changing nature, the aggregated metrics approach monitors and alerts perfectly for infrastructure problems. So here, a metric-based system works well. Metrics-based systems and their associated alerts help you see when capacity limits or known error conditions of underlying systems are being reached.

Now, we need to look at monitoring the Software and have access to high-cardinality fields. These may include the user ID or a shopping cart ID. Code that is well-instrumented for Observability allows you to answer complex questions that are easy to miss when examining aggregate performance.

Observability and monitoring are essential practices in modern software development and operations. While observability focuses on understanding system behaviour through comprehensive data collection and analysis, monitoring uses predefined metrics to assess performance and generate alerts.

By leveraging both approaches, organizations can gain a holistic view of their systems, enabling proactive measures, faster troubleshooting, and optimal performance. Embracing observability and monitoring as complementary practices can pave the way for more reliable, scalable, and efficient systems in the digital era.

Summary: Observability vs Monitoring

As technology advances rapidly, understanding and managing complex systems becomes increasingly important. Two terms that often arise in this context are observability and monitoring. While they may seem interchangeable, they represent distinct approaches to gaining insights into system performance. In this blog post, we delved into observability and monitoring, exploring their differences, benefits, and how they can work together to provide a comprehensive understanding of system behavior.

Understanding Monitoring

Monitoring is a well-established practice in the world of technology. It involves collecting and analyzing data from various sources to ensure the smooth functioning of a system. Monitoring typically focuses on key performance indicators (KPIs) such as response time, error rates, and resource utilization. Organizations can proactively identify and resolve issues by tracking these metrics, ensuring optimal system performance.

Unveiling Observability

Observability takes a more holistic approach compared to monitoring. It emphasizes understanding the internal state of a system by leveraging real-time data and contextual information. Unlike monitoring, which focuses on predefined metrics, observability aims to provide a clear picture of how a system behaves under different conditions. It achieves this by capturing fine-grained telemetry data, including logs, traces, and metrics, which can be analyzed to uncover patterns, anomalies, and root causes of issues.

The Benefits of Observability

One of the key advantages of observability is its ability to handle unexpected scenarios and unknown unknowns. Capturing detailed data about system behavior enables teams to investigate issues retroactively, even those that were not anticipated during the design phase. Additionally, observability allows for better collaboration between different teams, as the shared visibility into system internals facilitates more effective troubleshooting and faster incident resolution.

Synergy between Observability and Monitoring

While observability and monitoring are distinct concepts, they are not mutually exclusive. They can complement each other to provide a comprehensive understanding of system performance. Monitoring can provide high-level insights into system health and performance trends, while observability can dive deeper into specific issues and offer a more granular view. By combining these approaches, organizations can achieve a proactive and reactive system management approach, ensuring stability and resilience.

Conclusion:

Observability and monitoring are two powerful tools in the arsenal of system management. While monitoring focuses on predefined metrics, observability takes a broader and more dynamic approach, capturing fine-grained data to gain deeper insights into system behavior. By embracing observability and monitoring, organizations can unlock a comprehensive understanding of their systems, enabling them to proactively address issues, optimize performance, and deliver exceptional user experiences.

System Observability

Distributed Systems Observability

Distributed Systems Observability

In the realm of modern technology, distributed systems have become the backbone of numerous applications and services. However, the increasing complexity of such systems poses significant challenges when it comes to monitoring and understanding their behavior. This is where observability steps in, offering a comprehensive solution to gain insights into the intricate workings of distributed systems. In this blog post, we will embark on a captivating journey into the realm of distributed systems observability, exploring its key concepts, tools, and benefits.

Observability, as a concept, enables us to gain deep insights into the internal state of a system based on its external outputs. When it comes to distributed systems, observability takes on a whole new level of complexity. It encompasses the ability to effectively monitor, debug, and analyze the behavior of interconnected components across a distributed architecture. By employing various techniques and tools, observability allows us to gain a holistic understanding of the system's performance, bottlenecks, and potential issues.

To achieve observability in distributed systems, it is crucial to focus on three interconnected components: logs, metrics, and traces.

Logs: Logs provide a chronological record of events and activities within the system, offering valuable insights into what has occurred. By analyzing logs, engineers can identify anomalies, track down errors, and troubleshoot issues effectively.

Metrics: Metrics, on the other hand, provide quantitative measurements of the system's performance and behavior. They offer a rich source of data that can be analyzed to gain a deeper understanding of resource utilization, response times, and overall system health.

Traces: Traces enable the visualization and analysis of transactions as they traverse through the distributed system. By capturing the flow of requests and their associated metadata, traces allow engineers to identify bottlenecks, latency issues, and performance optimizations.

In the ever-evolving landscape of distributed systems observability, a plethora of tools and frameworks have emerged to simplify the process. Prominent examples include:

1. Prometheus: A powerful open-source monitoring and alerting system that excels in collecting and storing metrics from distributed environments.

2. Jaeger: An end-to-end distributed tracing system that enables the visualization and analysis of transaction flows across complex systems.

3. ELK Stack: A comprehensive combination of Elasticsearch, Logstash, and Kibana, which collectively offer powerful log management, analysis, and visualization capabilities.

4. Grafana: A widely-used open-source platform for creating rich and interactive dashboards, allowing engineers to visualize metrics and logs in real-time.

The adoption of observability in distributed systems brings forth a multitude of benefits. It empowers engineers and DevOps teams to proactively detect and diagnose issues, leading to faster troubleshooting and reduced downtime. Observability also aids in capacity planning, resource optimization, and identifying performance bottlenecks. Moreover, it facilitates collaboration between teams by providing a shared understanding of the system's behavior and enabling effective communication.

In the ever-evolving landscape of distributed systems, observability plays a pivotal role in unraveling the complexity and gaining insights into system behavior. By leveraging the power of logs, metrics, and traces, along with robust tools and frameworks, engineers can navigate the intricate world of distributed systems with confidence. Embracing observability empowers organizations to build resilient, high-performing systems that can withstand the challenges of today's digital landscape.

Highlights: Distributed Systems Observability

The Role of Distributed Systems

A – Several decades ago, only a handful of mission-critical services worldwide were required to meet the availability and reliability requirements of today’s always-on applications and APIs. In response to user demand, every application must be built to scale nearly instantly to accommodate the potential for rapid, viral growth. Almost every app built today—whether a mobile app for consumers or a backend payment system—must meet these constraints and requirements.

B – Inherently, distributed systems are more reliable due to their distributed nature. When appropriately designed software engineers build these systems, they can benefit from more scalable organizational models. There is, however, a price to pay for these advantages.

C – Designing, building, and debugging these distributed systems can be challenging. A reliable distributed system requires significantly more engineering skills than a single-machine application, such as a mobile app or a web frontend. Regardless, distributed systems are becoming increasingly important. There is a corresponding need for tools, patterns, and practices to build them.

D – As digital transformation accelerates, organizations adopt multicloud environments to drive secure innovation and achieve speed, scale, and agility. As a result, technology stacks are becoming increasingly complex and scalable. Today, even the most straightforward digital transaction is supported by an array of cloud-native services and platforms delivered by various providers. To improve user experience and resilience, IT and security teams must monitor and manage their applications.

**Key Components of Observability**

Observability in distributed systems typically relies on three pillars: logs, metrics, and traces. Logs provide detailed records of events within the system, offering context for debugging issues. Metrics offer quantitative data, such as CPU usage and request rates, allowing teams to monitor system health and performance over time. Traces enable the tracking of requests as they move through the system, helping to pinpoint where latency or failures occur. Together, these components create a comprehensive picture of the system’s state and behavior.

**Challenges in Achieving Observability**

While observability is essential, achieving it in distributed systems is not without its challenges. The sheer volume of data generated by these systems can be overwhelming. Additionally, correlating data from disparate sources to form a cohesive narrative requires sophisticated tools and techniques. Moreover, ensuring that observability doesn’t introduce too much overhead or affect system performance is a delicate balancing act. Organizations must invest in the right infrastructure and expertise to tackle these challenges effectively.

**Best Practices for Enhancing Observability**

To maximize observability in distributed systems, organizations should adopt several best practices. Firstly, they should implement centralized logging and monitoring solutions that can aggregate data from all system components. Secondly, leveraging open standards like OpenTelemetry can facilitate consistent data collection and integration with various tools. Thirdly, incorporating automated alerting and anomaly detection can help teams proactively address issues before they impact users. Lastly, fostering a culture of collaboration between development and operations teams can ensure that observability is an ongoing, shared responsibility.

Cloud Service Mesh

### What is a Cloud Service Mesh?

A Cloud Service Mesh is a dedicated infrastructure layer that facilitates service-to-service communication in a microservices architecture. It abstracts the complex communication patterns between services into a manageable, secure, and observable framework. By deploying a service mesh, organizations can effectively manage the interactions of their microservices, ensuring seamless connectivity, security, and resilience.

### Key Benefits of Implementing a Cloud Service Mesh

1. **Enhanced Security**: A service mesh provides robust security features such as mutual TLS authentication, which encrypts communications between services. This ensures that data remains secure and tamper-proof as it travels across the network.

2. **Traffic Management**: With a service mesh, you can implement sophisticated traffic management policies, including load balancing, circuit breaking, and retries. This leads to improved performance and reliability of your distributed systems.

3. **Observability**: One of the standout features of a service mesh is its ability to provide deep observability into the interactions between services. Metrics, logs, and traces are collected and analyzed, offering invaluable insights into system health and performance.

### Enhancing Observability in Distributed Systems

Observability is a key concern in managing distributed systems. With the proliferation of microservices, tracking and understanding service interactions can become overwhelmingly complex. A Cloud Service Mesh addresses this challenge by offering comprehensive observability features:

– **Metrics Collection**: Collects real-time metrics on service performance, latency, error rates, and more.

– **Distributed Tracing**: Enables tracing of requests as they propagate through multiple services, helping identify bottlenecks and performance issues.

– **Centralized Logging**: Aggregates logs from various services, providing a unified view for easier troubleshooting and analysis.

These capabilities empower teams to detect issues early, optimize performance, and ensure the reliability of their applications.

### Real-World Applications and Use Cases

Several organizations have successfully implemented Cloud Service Meshes to transform their operations. For instance, financial institutions use service meshes to secure sensitive transactions, while e-commerce platforms leverage them to manage high traffic volumes during peak shopping seasons. By providing a robust framework for service communication, a service mesh enhances scalability, reliability, and security across industries.

Googles Ops Agent

Ops Agent is a lightweight agent that runs on your Compute Engine instances, collecting and forwarding metrics and logs to Google Cloud Monitoring and Logging. By installing Ops Agent on your instances, you gain real-time visibility into your Compute Engine’s performance and behavior.

To start monitoring your Compute Engine, you must install Ops Agent on your instances. The installation process is straightforward and can be done manually or through automation tools like Cloud Deployment Manager or Terraform. Once installed, the Ops Agent will automatically begin collecting metrics and logs from your Compute Engine.

Ops Agent allows you to customize the metrics and logs you want to monitor for your Compute Engine. Various options are available, allowing you to choose specific metrics and logs relevant to your application or system. By configuring metrics and logs, you can gain deeper insights and track the performance of critical components.

**Challenge: Fragmented Monitoring Tools**

Fragmented monitoring tools and manual analytics strategies challenge IT and security teams. The lack of a single source of truth and real-time insight makes it increasingly difficult for these teams to access the answers they need to accelerate innovation and optimize digital services. To gain insight, they must manually query data from various monitoring tools and piece together different sources of information.

This complex and time-consuming process distracts Team members from driving innovation and creating new value for the business and customers. In addition, many teams monitor only their mission-critical applications due to the effort involved in managing all these tools, platforms, and dashboards. The result is a multitude of blind spots across the technology stack, which makes it harder for teams to gain insights.

**Challenge: Kubernetes is Complex**

Understanding how Kubernetes adds to the complexity of technology stacks is imperative. In the drive toward modern technology stacks, it is the platform of choice for organizations refactoring their applications for the cloud-native world. Through dynamic resource provisioning, Kubernetes architectures can quickly scale services to new users and increase efficiency.

However, the constant changes in cloud environments make it difficult for IT and security teams to maintain visibility into them. To provide observability in their Kubernetes environments, these teams cannot manually configure various traditional monitoring tools. The result is that they are often unable to gain real-time insights to improve user experience, optimize costs, and strengthen security. Due to this visibility challenge, many organizations are delaying moving more mission-critical services to Kubernetes.

GKE-Native Monitoring

The Basics of GKE-Native Monitoring

GKE-Native Monitoring is a comprehensive monitoring solution provided by Google Cloud Platform (GCP) designed explicitly for GKE clusters. It offers deep insights into your applications’ performance and behavior, allowing you to proactively detect and resolve issues. With GKE-Native Monitoring, you can easily collect and analyze metrics, monitor logs, and set up alerts to ensure the reliability and availability of your applications.

One of the critical features of GKE-Native Monitoring is its ability to collect and analyze metrics from your GKE clusters. It provides preconfigured dashboards that display essential metrics such as CPU usage, memory utilization, and network traffic. Additionally, you can create custom dashboards tailored to your specific requirements, allowing you better to understand your application’s performance and resource consumption.

The Role of Megatrends

We have had a considerable drive with innovation that has spawned several megatrends that have affected how we manage and view our network infrastructure and the need for distributed systems observability. We have seen the decomposition of everything from one to many.

Many services and dependencies in multiple locations, aka microservices observability, must be managed and operated instead of the monolithic where everything is generally housed internally. The megatrends have resulted in a dynamic infrastructure with new failure modes not seen in the monolithic, forcing us to look at different systems observability tools and network visibility practices. 

Shift in Control

There has also been a shift in the point of control. As we move towards new technologies, many of these loosely coupled services or infrastructures your services depend on are not under your control. The edge of control has been pushed, creating different network and security perimeters. These parameters are now closer to the workload than a central security stack. Therefore, the workloads themselves are concerned with security.

Example Product: Cisco AppDynamics

### What is Cisco AppDynamics?

Cisco AppDynamics is an application performance management (APM) solution designed to provide real-time visibility into the performance of your applications. It helps IT professionals identify bottlenecks, diagnose issues, and optimize performance, ensuring a seamless user experience. With its powerful analytics, you can gain deep insights into your application stack, from the user interface to the backend infrastructure.

### Key Features and Capabilities

#### Real-Time Monitoring

One of the standout features of Cisco AppDynamics is its ability to monitor applications in real-time. This allows IT teams to detect and resolve issues as they occur, minimizing downtime and ensuring a smooth user experience. Real-time monitoring covers everything from user interactions to server performance, providing a comprehensive view of your application’s health.

#### End-User Experience Monitoring

Understanding how users interact with your application is crucial for delivering a high-quality experience. Cisco AppDynamics offers end-user experience monitoring, which tracks user sessions and interactions. This data helps you identify any pain points or performance issues that may be affecting user satisfaction.

#### Business Transaction Monitoring

Cisco AppDynamics takes a unique approach to monitoring by focusing on business transactions. By tracking the performance of individual transactions, you can gain a clearer understanding of how different parts of your application are performing. This level of granularity allows for more targeted optimizations and quicker issue resolution.

### Benefits of Using Cisco AppDynamics

#### Improved Application Performance

With its comprehensive monitoring and diagnostic capabilities, Cisco AppDynamics helps you identify and resolve performance issues quickly. This leads to faster load times, fewer errors, and an overall improved user experience.

#### Enhanced Operational Efficiency

By automating many of the monitoring and diagnostic processes, Cisco AppDynamics reduces the workload on your IT team. This allows them to focus on more strategic initiatives, driving greater value for your business.

#### Better Decision Making

The insights provided by Cisco AppDynamics enable better decision-making at all levels of your organization. Whether you’re looking to optimize resource allocation or plan for future growth, the data and analytics provided can inform your strategies and drive better outcomes.

### Integrations and Flexibility

Cisco AppDynamics offers seamless integrations with a wide range of third-party tools and platforms. Whether you’re using cloud services like AWS and Azure or CI/CD tools like Jenkins and GitHub, AppDynamics can integrate into your existing workflows, providing a unified view of your application’s performance.

For pre-information, you may find the following posts helpful:

  1. Observability vs Monitoring 
  2. Prometheus Monitoring
  3. Network Functions

Distributed Systems Observability

Distributed Systems

Today’s world of always-on applications and APIs has availability and reliability requirements that would have been needed of solely a handful of mission-critical services around the globe only a few decades ago. Likewise, the potential for rapid, viral service growth means that every application has to be built to scale nearly instantly in response to user demand.

Finally, these constraints and requirements mean that almost every application made—whether a consumer mobile app or a backend payments application—needs to be a distributed system. A distributed system is an environment where different components are spread across multiple computers on a network. These devices split up the work, harmonizing their efforts to complete the job more efficiently than if a single device had been responsible.

**The Key Components of Observability**

Observability in distributed systems is achieved through three main components: monitoring, logging, and tracing.

1. Monitoring: Monitoring involves continuously collecting and analyzing system metrics and performance indicators. It provides real-time visibility into the health and performance of the distributed system. By monitoring various metrics such as CPU usage, memory consumption, network traffic, and response times, engineers can proactively identify anomalies and make informed decisions to optimize system performance.

2. Logging: Logging involves recording events, activities, and errors within the distributed system. Log data provides a historical record that can be analyzed to understand system behavior and debug issues. Distributed systems generate vast amounts of log data, and effective log management practices, such as centralized log storage and log aggregation, are crucial for efficient troubleshooting.

3. Tracing: Tracing involves capturing the flow of requests and interactions between different distributed system components. It allows engineers to trace the journey of a specific request and identify potential bottlenecks or performance issues. Tracing is particularly useful in complex distributed architectures where multiple services interact.

**Benefits of Observability in Distributed Systems**

Adopting observability practices in distributed systems offers several benefits:

1. Enhanced Troubleshooting: Observability enables engineers to quickly identify and resolve issues by providing detailed insights into system behavior. With real-time monitoring, log analysis, and tracing capabilities, engineers can pinpoint the root cause of problems and take appropriate actions, minimizing downtime and improving system reliability.

2. Performance Optimization: By closely monitoring system metrics, engineers can identify performance bottlenecks and optimize system resources. Observability allows for proactive capacity planning and efficient resource allocation, ensuring optimal performance even under high loads.

3. Efficient Change Management: Observability facilitates monitoring system changes and their impact on overall performance. Engineers can track changes in metrics and easily identify any deviations or anomalies caused by updates or configuration changes. This helps maintain system stability and avoid unexpected issues.

What are VPC Flow Logs?

VPC Flow Logs is a feature offered by Google Cloud that captures and records network traffic information within Virtual Private Cloud (VPC) networks. Each network flow is logged, providing a comprehensive view of the traffic traversing. These logs include valuable information such as source and destination IP addresses, ports, protocol, and packet counts.

Once the VPC Flow Logs are enabled and data is being recorded, we can start leveraging the power of analysis. Google Cloud provides several tools and services for analyzing VPC Flow Logs. One such tool is BigQuery, a scalable and flexible data warehouse. By exporting VPC Flow Logs to BigQuery, we can perform complex queries, visualize traffic patterns, and detect anomalies using industry-standard SQL queries.

**How This Affects Failures**

The primary issue I have seen with my clients is that application failures are no longer predictable, and dynamic systems can fail creatively, challenging existing monitoring solutions. But, more importantly, the practices that support them. We have a lot of partial failures that are not just unexpected but not known or have never been seen before. For example, if you recall, we have the network hero. 

**The network Hero**

It is someone who knows every part of the network and has seen every failure at least once. These people are no longer helpful in today’s world and need proper Observation. When I was working as an Engineer, we would have plenty of failures, but more than likely, we would have seen them before. And there was a system in place to fix the error. Today’s environment is much different.

We can no longer rely on simply seeing a UP or Down, setting static thresholds, and then alerting based on those thresholds. A key point to note at this stage is that none of these thresholds considers the customer’s perspective.  If your POD runs at 80% CPU, does that mean the customer is unhappy?

When monitoring, you should look from your customer’s perspectives and what matters to them. Content Delivery Network (CDN) was one of the first to realize this game and measure what matters most to the customer.

Distributed Systems Observability

The different demands

So, the new, modern, and complex distributed systems place very different demands on your infrastructure and the people who manage it. For example, in microservices, there can be several problems with a particular microservice:

    • The microservices could be running under high resource utilization and, therefore, slow to respond, causing a timeout
    • The microservices could have crashed or been stopped and is, therefore, unavailable
    • The microservices could be fine, but there could be slow-running database queries.
    • So we have a lot of partial failures. 

Consequently, We can no longer predict

The significant shift we see with software platforms is that they evolve much quicker than the products and paradigms we use to monitor them. As a result, we need to consider new practices and technologies with dedicated platform teams and sound system observability. We can’t predict anything anymore, which puts the brakes on some traditional monitoring approaches, especially the metrics-based approach to monitoring.

I’m not saying that these monitoring tools are not doing what you want them to do. But, they work in a siloed environment, and there is a lack of connectivity. So we have monitoring tools working in silos in different parts of the organization and more than likely managed by other people trying to monitor a very dispersed application with multiple components and services in various places. 

Relying On Known Failures

Metric-Based Approach

A metrics-based monitoring approach relies on having previously encountered known failure modes. The metric-based approach relies on known failures and predictable failure modes. So, we have predictable thresholds that someone is considered to experience abnormal.

Monitoring can detect when these systems are either over or under the predictable thresholds that were previously set. Then, we can set alerts, and we hope that these alerts are actionable. This is only useful for variants of predictable failure modes.

Traditional metrics and monitoring tools can tell you any performance spikes or notice that a problem occurs. But they don’t let you dig into the source of the issues and let us slice and dice or see correlations between errors. If the system is complex, this approach is more challenging in getting to the root cause in a reasonable timeframe.

Google Cloud Trace

Example: Application Latency & Cloud Trace

Before we discuss Cloud Trace’s specifics, let’s establish a clear understanding of application latency. Latency refers to the time delay between a user’s action or request and the corresponding response from the application. It includes network latency, server processing time, and database query execution time. By comprehending the different factors contributing to latency, developers can proactively optimize their applications for improved performance.

Google Cloud Trace is a powerful diagnostic tool offered by Google Cloud Platform (GCP) that enables developers to identify and analyze application latency bottlenecks. It provides detailed insights into the flow of requests and events within an application, allowing developers to pinpoint areas of concern and optimize accordingly. Cloud Trace integrates seamlessly with other GCP services and provides a comprehensive view of latency across various components of an application stack.

Traditional style metrics systems

With traditional metrics systems, you had to define custom metrics, which were always defined upfront. This approach prevents us from starting to ask new questions about problems. So, it would be best to determine the questions to ask upfront.

Then, we set performance thresholds, pronounce them “good” or “bad, ” and check and re-check those thresholds. We would tweak the thresholds over time, but that was about it. This monitoring style has been the de facto approach, but we don’t now want to predict how a system can fail. Always observe instead of waiting for problems, such as reaching a certain threshold before acting.

**Metrics: Lack of connective event**

The metrics did not retain the connective event, so you cannot ask new questions in the existing dataset. These traditional system metrics could miss unexpected failure modes in complex distributed systems. Also, the condition detected via system metrics might be unrelated to what is happening.

An example of this could be an odd number of running threads on one component, which might indicate garbage collection is in progress or that slow response times are imminent in an upstream service.

**Users experience static thresholds**

User experience means different things to different sets of users. We now have a model where different service users may be routed through the system in other ways, using various components and providing experiences that can vary widely. We also know now that the services no longer tend to break in the same few predictable ways over and over.  

We should have a few alerts triggered by only focusing on symptoms that directly impact user experience and not because a threshold was reached.

The Challenge: Can’t reliably indicate any issues with user experience

If you use static thresholds, they can’t reliably indicate any issues with user experience. Alerts should be set up to detect failures that impact user experience. Traditional monitoring falls short in this regard. With traditional metrics-based monitoring, we rely on static thresholds to define optimal system conditions, which have nothing to do with user experience.

However, modern systems change shape dynamically under different workloads. Static monitoring thresholds can’t reflect impacts on user experience. They lack context and are too coarse.

Required: Distributed Systems Observability

Systems observability and reliability in distributed systems are practices. Rather than just focusing on a tool that logs, metrics, or alters, Observability is all about how you approach problems, and for this, you need to look at your culture. So you could say that Observability is a cultural practice that allows you to be proactive about findings instead of relying on a reactive approach that we are used to in the past.

Nowadays, we need a different viewpoint and want to see everything from one place. You want to know how the application works and how it interacts with the other infrastructure components, such as the underlying servers, physical or server, the network, and how data transfer looks in a transfer and stale state. 

Levels of Abstraction

What level of observation is needed to ensure everything performs as it should? What should you look at to obtain this level of detail?

Monitoring is knowing the data points and the entities from which we gather information. On the other hand, Observability is like putting all the data together. So monitoring is collecting data, and Observability is putting it together in one single pane of glass. Observability is observing the different patterns and deviations from the baseline; monitoring is getting the data and putting it into the systems. A vital part of an Observability toolkit is service level objectives (slos).

**Preference: Distributed Tracing**

We have three pillars of Systems Observability. There are Metrics, Traces, and Logging. So, defining or viewing Observability as having these pillars is an oversimplification. But for Observability, you need these in place. Observability is all about connecting the dots from each of these pillars.

If someone asked me which one I prefer, it would be distributed tracing. Distributed tracing allows you to visualize each step in service request executions. As a result, it doesn’t matter if services have complex dependencies. You could say that the complexity of the Dynamic systems is abstracted with distributed tracing.

**Use Case: Challenges without tracing**

For example, latency can stack up if a downstream database service experiences performance bottlenecks, resulting in high end-to-end latency. When latency is detected three or four layers upstream, it can be complicated to identify which component of the system is the root of the problem because now that same latency is being seen in dozens of other services.

**Distributed tracing: A winning formula**

Modern distributed systems tend to scale into a tangled knot of dependencies. Therefore, distributed tracing shows the relationships between various services and components in a distributed system. Traces help you understand system interdependencies. Unfortunately, those inter-dependencies can obscure problems and make them challenging to debug unless their relationships are clearly understood.

In distributed systems, observability is vital in ensuring complex architectures’ stability, performance, and reliability. Monitoring, logging, and tracing provide engineers with the tools to understand system behavior, troubleshoot issues, and optimize performance. By adopting observability practices, organizations can effectively manage their distributed systems and provide seamless and reliable services to their users.

Summary: Distributed Systems Observability

In the vast landscape of distributed systems, observability is crucial in ensuring their reliable and efficient functioning. This blogpost aims to delve into the critical components of distributed systems observability and shed light on their significance.

Telemetry

Telemetry forms the foundation of observability in distributed systems. It involves collecting, processing, and analyzing various metrics, logs, and traces. By monitoring and measuring these data points, developers gain valuable insights into the performance and behavior of their distributed systems.

Logging

Logging is an essential component of observability, providing a detailed record of events and activities within a distributed system. It captures important information such as errors, warnings, and informational messages, which aids in troubleshooting and debugging. Properly implemented logging mechanisms enable developers to identify and resolve issues promptly.

Metrics

Metrics are quantifiable measurements that provide a high-level view of the health and performance of a distributed system. They offer valuable insights into resource utilization, throughput, latency, error rates, and other critical indicators. By monitoring and analyzing metrics, developers can proactively identify bottlenecks, optimize performance, and ensure the smooth operation of their systems.

Tracing

Tracing allows developers to understand the flow and behavior of requests as they traverse through a distributed system. It provides detailed information about the path a request takes, including the various services and components it interacts with. Tracing is instrumental in diagnosing and resolving performance issues, as it highlights potential latency hotspots and bottlenecks.

Alerting and Visualization

Alerting mechanisms and visualization tools are vital for effective observability in distributed systems. Alerts notify developers when certain predefined thresholds or conditions are met, enabling them to take timely action. Visualization tools provide intuitive and comprehensive representations of system metrics, logs, and traces, making identifying patterns, trends, and anomalies easier.

Conclusion

In conclusion, the key components of distributed systems observability, namely telemetry, logging, metrics, tracing, alerting, and visualization, form a comprehensive toolkit for monitoring and understanding the intricacies of such systems. By leveraging these components effectively, developers can ensure their distributed systems’ reliability, performance, and scalability.