Docker’s Best-Kept Secret: How Observability Saves Developers’ Sanity

Observability extends beyond traditional monitoring, offering in-depth insights into system behavior.

May 31st, 2025 10:00am by Aditya Gupta

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As software systems become increasingly complex and distributed, developers and operations teams face a daunting challenge in understanding application behavior at scale. While technologies based on containers, microservices, and cloud native architectures make it easier to deliver software, they also increase the difficulty in debugging and monitoring processes. How do you effectively diagnose problems, monitor performance in real time, and ensure reliability between services?

The answer lies in end-to-end observability. Observability extends beyond traditional monitoring, offering in-depth insights into system behavior. With the adoption of effective tracing solutions, such as OpenTelemetry and Jaeger, in Docker containers, developers can now proactively detect performance issues, increase reliability, and significantly reduce downtime.

This guide provides an overview of observability principles, clarifies the role of distributed tracing, and discusses how Docker, OpenTelemetry, and Jaeger work together to enhance operational intelligence and streamline incident responses.

Why Observability Matters More Than Ever

Modern applications increasingly feature as distributed systems made up of many interdependent services and application programming interfaces (APIs). While Docker makes scaling and deployment easier for microservices, its native complexity often leads to unclear performance problems and scaling roadblocks.

Key observability challenges include:

Distributed Systems Complexity: The challenge in distributed systems is pinpointing the cause of errors or bottlenecks within many interconnected microservices.
Latency and Performance Issues: Quickly detecting slow responses or resource contention.
Real-Time Insights: There is a need for real-time visibility into system performance rather than relying on logs or traditional monitoring tools that are time-lagged.

Troubleshooting is slow and laborious without full observability, and it dramatically adds to Mean Time To Resolution (MTTR).

In my own experience with container service performance debugging for a major cloud-scale infrastructure provider, the absence of distributed tracing meant that we relied almost exclusively on log correlation and alert-driven metrics, which succeeded 70% of the time. The balance was guesswork and long war room meetings. With trace propagation among services in that equation, MTTR plummeted, and debugging became more an exercise in navigating timelines than trawling through logs.

Why Docker-Based Environments Need Observability

I recall troubleshooting a deployment that had flaked out, where an application running in containers within a frontend application kept crashing periodically. CPU and memory were fine, logs were opaque, and autoscaling hid symptoms. We didn’t know it was an authentication service timing out downstream during high-concurrent traffic until we added trace context using OpenTelemetry and visualized dependencies in Jaeger. That kind of intelligence wasn’t possible based on metrics alone. Docker and Jaeger were game changers here.

Docker, in general, has revolutionized the software deployment landscape by enabling portability, consistency, and simplicity of scaling. However, the transient nature of containers poses some challenges:

Containers can start and stop often, thus making monitoring more complex.
Containers share resources, potentially masking performance issues.
Microservices often communicate asynchronously, obscuring tracing and visibility.

Deploying an observability solution in Docker environments allows developers and operators to gain in-depth insights into applications running in containers.

Introducing OpenTelemetry and Jaeger

OpenTelemetry

OpenTelemetry is an open CNCF standard designed for instrumentation, tracing, and metrics collection in cloud native applications. It makes it easy to provide consistent telemetry data across your applications, thus making observability easier to implement and data analysis simpler.

Jaeger

Jaeger is an open-source distributed trace system developed initially by Uber and is very effective in visualizing and analyzing trace data from OpenTelemetry. Jaeger is very helpful in offering practical advice via simple dashboards, allowing developers to quickly identify performance bottlenecks and problems.

Alternative Solutions to Jaeger

While Jaeger is a powerful tool, some other trace tools might be considered depending on specific requirements:

Zipkin is an excellent alternative that shares similar features and is OpenTelemetry compliant.
Elastic APM is a full observability solution with native support for tracing, metrics, and logging.
Datadog and New Relic are proprietary software offering deep observability features.

However, Jaeger’s open source nature and seamless integration with Docker makes it particularly well-suited for teams that need an affordable and flexible solution.

Setting Up OpenTelemetry and Jaeger in Docker

Step 1: Instrument Your Application

Consider a Node.js microservice as an example:

// server.js
const express = require('express');
const { NodeTracerProvider } = require('@opentelemetry/sdk-node');
const { registerInstrumentations } = require('@opentelemetry/instrumentation');
const { ExpressInstrumentation } = require('@opentelemetry/instrumentation-express');
const { JaegerExporter } = require('@opentelemetry/exporter-jaeger');

const provider = new NodeTracerProvider();
provider.addSpanProcessor(
  new (require('@opentelemetry/sdk-trace-base').SimpleSpanProcessor)(
    new JaegerExporter({ endpoint: 'http://jaeger:14268/api/traces' })
  )
);

provider.register();
registerInstrumentations({ instrumentations: [new ExpressInstrumentation()] });

const app = express();
app.get('/', (req, res) => res.send('Hello World'));
app.listen(3000);

// server.js

const express = require('express');

const { NodeTracerProvider } = require('@opentelemetry/sdk-node');

const { registerInstrumentations } = require('@opentelemetry/instrumentation');

const { ExpressInstrumentation } = require('@opentelemetry/instrumentation-express');

const { JaegerExporter } = require('@opentelemetry/exporter-jaeger');

const provider = new NodeTracerProvider();

provider.addSpanProcessor(

new (require('@opentelemetry/sdk-trace-base').SimpleSpanProcessor)(

new JaegerExporter({ endpoint: 'http://jaeger:14268/api/traces' })

)

);

provider.register();

registerInstrumentations({ instrumentations: [new ExpressInstrumentation()] });

const app = express();

app.get('/', (req, res) => res.send('Hello World'));

app.listen(3000);

Step 2: Dockerize Your App

FROM node:18-alpine
WORKDIR /app
COPY package.json ./
RUN npm install
COPY . .
EXPOSE 3000
CMD ["node", "server.js"]

FROM node:18-alpine

WORKDIR /app

COPY package.json ./

RUN npm install

COPY . .

EXPOSE 3000

CMD ["node", "server.js"]

Step 3: Deploying With Docker Compose

version: '3.8'
services:
  app:
    build: .
    ports:
      - "3000:3000"
    depends_on:
      - jaeger
  jaeger:
    image: jaegertracing/all-in-one:1.55
    ports:
      - "16686:16686"
      - "14268:14268"
Run your environment with:
docker compose up
Access Jaeger UI at http://localhost:16686 to explore tracing data.

version: '3.8'

services:

app:

build: .

ports:

- "3000:3000"

depends_on:

- jaeger

jaeger:

image: jaegertracing/all-in-one:1.55

ports:

- "16686:16686"

- "14268:14268"

Run your environment with:

docker compose up

Access Jaeger UI at http://localhost:16686 to explore tracing data.

Real Experience Implementing This at Scale:

Applying this configuration to many microservices in a high-traffic production system taught an important lesson: observability is not an afterthought but an integral part of infrastructure. With container orchestration offering scalability and with traces providing immense visibility into the system, each team, from the infrastructure team to the frontend team, could rely on the same trace IDs while solving edge cases; this capability wasn’t achieved earlier with disconnected logging approaches.

Practical Use Cases and Industry Examples

Jaeger is heavily used by major technology firms, including Uber, Red Hat, and Shopify, to enable real-time observability. These organizations use distributed tracing for:

Quickly detect performance degradation of microservices
Improve the end-user experience by proactively detecting latency problems.
Ensure high reliability through timely detection and resolution of incidents.

Advanced Observability Techniques

Distributed Context Propagation

Leverage OpenTelemetry auto HTTP header propagation to maintain trace context between services.

Custom Span Creation

const axios = require('axios');
app.get('/fetch', async (req, res) => {
  const result = await axios.get('http://service-b/api');
  res.send(result.data);
});

const axios = require('axios');

app.get('/fetch', async (req, res) => {

const result = await axios.get('http://service-b/api');

res.send(result.data);

});

Gain a deeper understanding of complicated functions using manual definition of spans.

const { trace } = require('@opentelemetry/api');
app.get('/compute', (req, res) => {
  const span = trace.getTracer('compute-task').startSpan('heavy-computation');
  // Compute-intensive task
  span.end();
  res.send('Done');
});

const { trace } = require('@opentelemetry/api');

app.get('/compute', (req, res) => {

const span = trace.getTracer('compute-task').startSpan('heavy-computation');

// Compute-intensive task

span.end();

res.send('Done');

});

Integrating Observability into CI/CD Pipelines

It is important to integrate observability checks into continuous integration and continuous deployment pipelines, such as GitHub Actions, to ensure that code changes meet visibility expectations.

name: CI Observability Check
on: [push]
jobs:
  check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Run Docker Compose
        run: docker compose up -d
      - name: Observability Verification
        run: curl --retry 5 --retry-delay 10 --retry-connrefused http://localhost:16686

name: CI Observability Check

on: [push]

jobs:

check:

runs-on: ubuntu-latest

steps:

- uses: actions/checkout@v2

- name: Run Docker Compose

run: docker compose up -d

- name: Observability Verification

run: curl --retry 5 --retry-delay 10 --retry-connrefused http://localhost:16686

The Future of Observability

Observability continues to evolve rapidly, especially with AI-driven analytics and predictive monitoring capabilities. Emerging trends include:

Automated anomaly detection
AI-assisted root cause analysis
Improved predictive alerting allows for early incident prevention.

OpenTelemetry and Jaeger are leading-edge technologies that allow organizations to take advantage of improved observability in future deployments.

As more teams deploy AI/ML services, observability must continue to improve. I’ve seen firsthand, through my experience integrating LLM services into container pipelines, just how opaque model behavior is becoming. OpenTelemetry and similar technologies are now starting to fill that gap, and being able to see inference, latency, and system interaction all on one timeline will be crucial in the AI-native world.

Conclusion

The integration of OpenTelemetry and Jaeger is used to significantly enhance observability in Docker environments, thereby increasing the ability to monitor and govern distributed systems more effectively. These technologies, when combined, yield real-time, actionable intelligence that promotes effective troubleshooting, boosts performance, and maintains high availability for teams. As more organizations adopt containerization and microservices, understanding observability best practices has become a vital component in achieving operational success.

Aditya Gupta is a software engineering leader with extensive experience in building secure, scalable systems across cloud infrastructure, observability, and distributed computing. He has spent the last decade working on high-throughput telemetry pipelines, privacy-conscious tracing architectures, and data platforms that...