Observability Fundamentals Beyond Traditional Monitoring

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Observability and Performance: The Precipice of Building Highly Performant Software Systems.

Gone are the days when health metrics like CPU usage, memory, and disk space were all that we needed. Traditional monitoring and observability, although still valuable, fall short in the full-stack arena.

In today's distributed computing world, where Kubernetes and microservices are becoming increasingly popular, the term "back end" rarely refers to one large application with a single large database. It often refers to a collection of smaller, interconnected services and databases that work together to handle specific functions. Orchestrated through platforms like Kubernetes, full-stack monitoring requires more than logging a single large database.

At an infrastructure level as well as at an application level, distributed tracing, logs, and metrics are necessary to get a clearer picture of your system's health. We should, at a minimum, continuously collect and analyze this data to establish baselines and thresholds that help identify deviations from the norm.

Key Components of Full-Stack Observability

The four basic components of full-stack observability are metrics, logs, traces, and user experience. Example use cases and tools for each component are depicted in Table 1:

Table 1. Components of full-stack observability and monitoring

Metrics, traces, logs, and user experience monitoring are very important to get a clear picture of your full-stack performance. For example, consider a streaming service provider that uses metrics to track system load:

• Metrics could be defined as video buffering times and bandwidth usage.
• When customers report playback issues, logs record detailed events of CDN failures and content delivery problems.
• Traces are used to follow a video stream request across multiple services (content recommendation, CDN, playback engine); this way, they can pinpoint which service caused delays.
• User experience tools monitor user interactions to detect how buffering or playback issues impact user retention and satisfaction. This can help optimize content delivery strategies.

The goals of full-stack observability and monitoring are to:

• Quickly identify and resolve issues across the stack
• Understand complex system behaviors across the stack
• Make data-driven decisions about system design and optimization
• Improve the overall reliability and performance at both infrastructure and application levels

Correlating Data for Full-Stack Observability and Monitoring

By correlating data from sources, such as the key components above, full-stack observability allows you to gain a holistic understanding of system behavior. This leads to faster root cause analysis, proactive performance management, and improved decision making in a complex and distributed computing environment.

Here are some ways that correlating data can achieve full-stack observability:

• By discovering and visualizing the relationships between different components of your system, dependency mapping will give insight on the impact of changes. This way, troubleshooting complex issues will be more effective.
• Using distributed observability data to continuously improve system performance may involve identifying slow database queries, optimizing API calls, or refining caching strategies.
• Setting up intelligent alerting systems that can correlate multiple data sources can reduce the time we spend on irrelevant or less critical issues and focus on the critical ones.
• Integrating security-related data into your observability platform can help users detect and respond quickly to potential security threats. Otherwise, your response time could be too slow or the threats could go entirely undetected.
• Using observability data to ensure compliance with regulatory requirements can be done by maintaining distributed audit trails for security and operational changes.
• Correlating technical metrics with business KPIs can help summarize how the overall system performance affects the bottom line. Your technical improvements can now be prioritized based on their potential business impact.

Step-by-Step Full-Stack Observability and Monitoring

Implementing a full-stack observability and monitoring strategy involves several steps. From setting objectives to choosing the right tools and defining the right processes, here's a way to get started.

Step 1: Set Clear Objectives

Before you start implementing an observability and monitoring strategy, define your specific goals. By establishing clear objectives, you can focus your efforts on collecting the most relevant data and insights.

Example objectives of a full-stack strategy might include:

• Reducing mean time to recovery by quickly identifying and resolving incidents to minimize downtime
• Improving application performance by identifying and optimizing slow-performing services or components
• Enhancing the user experience by monitoring real user interactions to ensure seamless performance
• Following compliance and security standards by monitoring access and anomaly patterns

Step 2: Choose the Right Tools

Selecting the right tools is crucial for effective full-stack observability and monitoring. You need platforms that support various aspects of observability, including metrics collection, log aggregation, distributed tracing, and user monitoring. A list of popular tools can be found in Table 1.

Step 3: Instrument Applications and Infrastructure With OpenTelemetry

To implement full-stack observability, you can start by instrumenting your applications and infrastructure using OpenTelemetry. The following steps demonstrate, in Python, how to instrument a Flask application using OpenTelemtry and Jaeger.

1. Install OpenTelemetry SDKs:

pip install opentelemetry-api opentelemetry-sdk opentelemetry-instrumentation
pip install opentelemetry-instrumentation-flask
pip install opentelemetry-exporter-jaeger

2. Initialize and set up the basic OpenTelemetry configuration:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.resources import Resource
from opentelemetry.semconv.resource import ResourceAttributes

# Create a resource with service name
resource = Resource(attributes={
ResourceAttributes.SERVICE_NAME: "my-flask-service"
})

# Create a tracer provider
tracer_provider = TracerProvider(resource=resource)

# Set the tracer provider as the global default
trace.set_tracer_provider(tracer_provider)

# Get a tracer
tracer = trace.get_tracer(__name__)

3. Set up data export. Set up the Jaeger exporter and configure it to send data:

from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
# Create a Jaeger exporter
jaeger_exporter = JaegerExporter(
agent_host_name="localhost",
agent_port=6831,
)

# Create a BatchSpanProcessor and add the exporter to it
span_processor = BatchSpanProcessor(jaeger_exporter)

# Add the SpanProcessor to the TracerProvider
tracer_provider.add_span_processor(span_processor)

4. Integrate OpenTelemetry with your chosen observability platform. In this example, we're integrating with Jaeger by instrumenting a Flask application. Most platforms offer built-in support for OpenTelemetry, simplifying data collection and analysis.

from flask import Flask
from opentelemetry.instrumentation.flask import FlaskInstrumentor

# Create and instrument a Flask application
app = Flask(__name__)
FlaskInstrumentor().instrument_app(app)

@app.route('/')
def hello():
return "Hello, OpenTelemetry with Jaeger!"

@app.route('/api/data')
def get_data():
with tracer.start_as_current_span("get_data"):
# Simulate some work
import time
time.sleep(0.1)
return {"data": "Some important information"}

if __name__ == '__main__':
app.run(debug=True)

To use the steps above, make sure you have a Jaeger back end running. Run your Flask application and generate some traffic by accessing http://localhost:5000 and http://localhost:5000/api/data in your browser or using a tool like cURL. Open the Jaeger UI to view your traces.

Step 4: Implement Logging and Log Aggregation

Logs provide detailed records of the events occurring within your applications and infrastructure. For comprehensive observability, centralize logs from various services, applications, and infrastructure components using a log aggregation tool.

Best practices for log management include:

• Using a structured format (e.g., JSON) for logs to make parsing and searching easier
• Aggregating logs in a centralized storage system like Elasticsearch for easier access and analysis
• Implementing automated log rotation to manage storage and performance efficiently
• Using trace IDs to correlate logs with distributed traces, which provide contextual information for debugging

Step 5: Set Up Distributed Tracing for Visibility Across Services

Distributed systems may require end-to-end visibility for latency and bottlenecks. One way to achieve end-to-end visibility is by tracing requests as they traverse across services. Distributed tracing is especially useful in microservices and cloud-native environments, where understanding inter-service communication is critical.

To implement distributed tracing:

• Use OpenTelemetry to instrument your services for tracing. OpenTelemetry provides APIs and SDKs to create and propagate trace contexts across service boundaries. 
• Collect and export trace data to a back end for analysis. 
• Use your observability platform's tracing dashboard to visualize request flows, identify bottlenecks, and perform root cause analysis.

Step 6: Implement Real User Monitoring for User Experience Insights

Real user monitoring (RUM) tracks real users' interactions with your application, offering insights into their experience. By collecting data on page load times, user interactions, and errors, RUM helps identify performance issues that impact user satisfaction.

To integrate RUM: 

• Select a RUM tool that integrates seamlessly with your observability stack.
• Instrument your application by adding RUM tracking code to your front-end application to start collecting user interaction data. 
• Use the RUM dashboard to analyze user sessions, identify trends, and detect performance issues that affect the user experience.

Step 7: Define and Implement Alerts and Automation

Alerts are crucial for proactive observability. Set up automated alerts based on predefined thresholds or anomalies detected by your observability platform. 

For effective alerting: 

• Define clear alert criteria based on key metrics (e.g., latency, error rates) relevant to your observability objectives. 
• Use AI/ML-based anomaly detection to identify unusual patterns in real time. 
• Integrate with incident response tools to automate responses to critical alerts.

Step 8: Scale and Optimize Observability Processes

As systems grow, scaling observability processes becomes vital. Scaling involves optimizing data collection, storage, and analysis to handle increasing telemetry data volumes. 

As a quick start:

• Use sampling to reduce the amount of trace data collected while retaining meaningful insights.
• Create centralized observability dashboards to monitor key metrics and logs. This will ensure quick access to critical information.
• Periodically review your observability processes to ensure they align with changing system architectures and business objectives.

Conclusion

There are multiple observability and monitoring trends that may evolve in the future in interesting ways. A major improvement in full-stack observability would be to shift from reactive problem solving to proactive system optimization, driven by advancements in AI to detect anomalies across the stack and machine learning to predict potential issues before they occur.

Data convergence and cross-stack correlation is another evolving trend that can support the shift from reactive to proactive optimization. Metrics, logs, traces, and user experience data will be more tightly integrated, providing a holistic view of system health. Platforms are expected to automatically correlate events across different layers of the stack, from infrastructure to application code to user interactions. More sophisticated auto-instrumentation techniques will reduce the need for manual code changes. Observability data will feed directly into automation systems, enabling automatic problem resolution in many cases.

Full-stack observability and monitoring are crucial practices that should be kept up to date with progressing trends. This is especially true for organizations seeking to maintain optimal performance, reliability, and user experience in distributed and complex software environments.

This article highlighted the steps to achieve scalable full-stack observability and monitoring by leveraging OpenTelemetry; integrating metrics, logs, traces, and real user monitoring; and adopting a proactive alerting strategy. The insights gained from the outlined steps will help you identify and troubleshoot issues efficiently, and they will empower your teams to make data-driven decisions for continuous improvement and innovation.

Related Refcards:

• Full-Stack Observability Essentials by Joana Carvalho, DZone Refcard 
• Getting Started With OpenTelemetry by Joana Carvalho, DZone Refcard
• Getting Started With Prometheus by Colin Domoney,  DZone Refcard
• Getting Started With Log Management by John Vester, DZone Refcard

This is an excerpt from DZone's 2024 Trend Report, Observability and Performance: The Precipice of Building Highly Performant Software Systems.

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