$ -weight: 500;">kubectl get pods -A | grep order-processing
-weight: 500;">kubectl get pods -A | grep order-processing
-weight: 500;">kubectl get pods -A | grep order-processing
-weight: 500;">kubectl logs -f <order-processing-pod-name>
-weight: 500;">kubectl logs -f <order-processing-pod-name>
-weight: 500;">kubectl logs -f <order-processing-pod-name>
-weight: 500;">kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-kubernetes/master/production.yaml
-weight: 500;">kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-kubernetes/master/production.yaml
-weight: 500;">kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-kubernetes/master/production.yaml
-weight: 500;">kubectl port-forward -n jaeger svc/jaeger-query 16686:16686 &
-weight: 500;">kubectl port-forward -n jaeger svc/jaeger-query 16686:16686 &
-weight: 500;">kubectl port-forward -n jaeger svc/jaeger-query 16686:16686 &
# Example Kubernetes manifest for a -weight: 500;">service with Jaeger instrumentation
apiVersion: apps/v1
kind: Deployment
metadata: name: order-processing
spec: replicas: 1 selector: matchLabels: app: order-processing template: metadata: labels: app: order-processing spec: containers: - name: order-processing image: order-processing:latest env: - name: JAEGER_AGENT_HOST value: "jaeger-agent" - name: JAEGER_AGENT_PORT value: "6831"
# Example Kubernetes manifest for a -weight: 500;">service with Jaeger instrumentation
apiVersion: apps/v1
kind: Deployment
metadata: name: order-processing
spec: replicas: 1 selector: matchLabels: app: order-processing template: metadata: labels: app: order-processing spec: containers: - name: order-processing image: order-processing:latest env: - name: JAEGER_AGENT_HOST value: "jaeger-agent" - name: JAEGER_AGENT_PORT value: "6831"
# Example Kubernetes manifest for a -weight: 500;">service with Jaeger instrumentation
apiVersion: apps/v1
kind: Deployment
metadata: name: order-processing
spec: replicas: 1 selector: matchLabels: app: order-processing template: metadata: labels: app: order-processing spec: containers: - name: order-processing image: order-processing:latest env: - name: JAEGER_AGENT_HOST value: "jaeger-agent" - name: JAEGER_AGENT_PORT value: "6831"
# Example Python code for instrumenting a -weight: 500;">service with OpenTracing
from opentracing import Format
from jaeger_client import Config # Create a Jaeger configuration
config = Config( config={ 'sampler': { 'type': 'const', 'param': 1, }, 'logging': True, }, service_name='order-processing',
) # Create a tracer
tracer = config.initialize_tracer() # Use the tracer to instrument your -weight: 500;">service
def process_order(order): span = tracer.start_span('process_order') try: # Process the order span.set_tag('order_id', order.id) span.set_tag('-weight: 500;">status', 'success') except Exception as e: span.set_tag('-weight: 500;">status', 'error') span.log_exception(e) finally: span.finish()
# Example Python code for instrumenting a -weight: 500;">service with OpenTracing
from opentracing import Format
from jaeger_client import Config # Create a Jaeger configuration
config = Config( config={ 'sampler': { 'type': 'const', 'param': 1, }, 'logging': True, }, service_name='order-processing',
) # Create a tracer
tracer = config.initialize_tracer() # Use the tracer to instrument your -weight: 500;">service
def process_order(order): span = tracer.start_span('process_order') try: # Process the order span.set_tag('order_id', order.id) span.set_tag('-weight: 500;">status', 'success') except Exception as e: span.set_tag('-weight: 500;">status', 'error') span.log_exception(e) finally: span.finish()
# Example Python code for instrumenting a -weight: 500;">service with OpenTracing
from opentracing import Format
from jaeger_client import Config # Create a Jaeger configuration
config = Config( config={ 'sampler': { 'type': 'const', 'param': 1, }, 'logging': True, }, service_name='order-processing',
) # Create a tracer
tracer = config.initialize_tracer() # Use the tracer to instrument your -weight: 500;">service
def process_order(order): span = tracer.start_span('process_order') try: # Process the order span.set_tag('order_id', order.id) span.set_tag('-weight: 500;">status', 'success') except Exception as e: span.set_tag('-weight: 500;">status', 'error') span.log_exception(e) finally: span.finish()
# Example command to get the Jaeger agent logs
-weight: 500;">kubectl logs -f -n jaeger $(-weight: 500;">kubectl get pods -n jaeger | grep jaeger-agent | awk '{print $1}')
# Example command to get the Jaeger agent logs
-weight: 500;">kubectl logs -f -n jaeger $(-weight: 500;">kubectl get pods -n jaeger | grep jaeger-agent | awk '{print $1}')
# Example command to get the Jaeger agent logs
-weight: 500;">kubectl logs -f -n jaeger $(-weight: 500;">kubectl get pods -n jaeger | grep jaeger-agent | awk '{print $1}') - A basic understanding of microservices architecture and containerization (e.g., Docker)
- Familiarity with Kubernetes (or another container orchestration platform)
- Jaeger or another distributed tracing tool installed and configured
- A sample microservices application (e.g., a simple e-commerce platform) to practice with
- Basic knowledge of command-line tools (e.g., -weight: 500;">kubectl, -weight: 500;">docker) - Insufficient sampling: If you don't sample enough traces, you may not capture the issue you're trying to debug. To avoid this, make sure to configure your sampler to capture a representative sample of traffic.
- Inconsistent instrumentation: If your services are instrumented inconsistently, it can be hard to correlate traces across services. To avoid this, make sure to use a consistent instrumentation library and configuration across all services.
- Inadequate logging: If your services don't log enough information, it can be hard to diagnose issues. To avoid this, make sure to log relevant information (e.g., request IDs, user IDs) and configure your logging to capture errors and exceptions.
- Incorrect Jaeger configuration: If your Jaeger configuration is incorrect, you may not capture tracing data correctly. To avoid this, make sure to configure Jaeger correctly and test it before deploying to production.
- Overhead from tracing: If your tracing implementation introduces too much overhead, it can impact performance. To avoid this, make sure to optimize your tracing implementation and configure it to minimize overhead. - Use a consistent instrumentation library and configuration across all services
- Configure your sampler to capture a representative sample of traffic
- Log relevant information (e.g., request IDs, user IDs) and configure logging to capture errors and exceptions
- Test your Jaeger configuration before deploying to production
- Optimize your tracing implementation to minimize overhead
- Monitor your tracing data regularly to identify issues and improve observability - Service mesh: A -weight: 500;">service mesh is a configurable infrastructure layer that can help you manage -weight: 500;">service discovery, traffic management, and security in your microservices application. Tools like Istio and Linkerd can help you implement a -weight: 500;">service mesh.
- Monitoring and logging: Monitoring and logging are critical components of observability in microservices. Tools like Prometheus, Grafana, and ELK can help you monitor and log your services.
- Chaos engineering: Chaos engineering is the practice of intentionally introducing failures into your system to test its resilience. Tools like Chaos Monkey and Litmus can help you implement chaos engineering in your microservices application. - Lens - The Kubernetes IDE that makes debugging 10x faster
- k9s - Terminal-based Kubernetes dashboard
- Stern - Multi-pod log tailing for Kubernetes - Kubernetes Troubleshooting in 7 Days - My step-by-step email course ($7)
- "Kubernetes in Action" - The definitive guide (Amazon)
- "Cloud Native DevOps with Kubernetes" - Production best practices - 3 curated articles per week
- Production incident case studies
- Exclusive troubleshooting tips
