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. 1. 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. 1. 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.