Jaeger 1.22: Distributed Tracing for Kubernetes

Introduction

When a request crosses a dozen services, logs and metrics tell you something is wrong, but tracing tells you where the time went and which dependency actually broke. That’s why distributed tracing tends to become essential as Kubernetes architectures move from “a few services” to “a service graph”.

Jaeger 1.22, released on November 20, 2020, focuses on the practical scaling concerns that show up next: query performance, storage backends, and smoother Kubernetes deployment/operations so traces remain usable under real traffic.

Practical adoption notes

• Start with one critical request path and instrument it end-to-end before chasing “trace everything”.
• Plan retention deliberately: traces are high-volume; backend choice and indexing strategy matter.
• Operationalize the UI: make it easy for on-call engineers to pivot from an alert to a trace quickly.

Performance Improvements

• Query performance optimizations reduce latency for trace queries, especially those involving large time ranges.
• Storage improvements enable better handling of high-volume trace data.
• Index enhancements accelerate trace lookups and filtering.
• Memory management optimizations reduce resource usage during trace processing.

Storage Backend Enhancements

1. Elasticsearch integration improvements provide better performance and reliability.
2. Cassandra support enhancements enable better scalability for large deployments.
3. Badger improvements provide efficient local storage for development and testing.
4. Object storage support enables cost-effective long-term trace retention.

Kubernetes Integration

• Operator improvements simplify deployment and management of Jaeger in Kubernetes.
• Service discovery enhancements automatically discover services and collect traces.
• OpenTelemetry integration provides better compatibility with the observability standard.
• Helm charts simplify installation and configuration.

Observability Features

• Trace analysis improvements provide better insights into request flows and dependencies.
• Service map visualization shows service-to-service communication patterns.
• Metrics integration enables generating metrics from trace data.
• Alerting support enables alerting on trace patterns and anomalies.

Getting Started

kubectl create namespace observability
kubectl apply -n observability -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.22.0/jaeger-operator.yaml

Create a Jaeger instance:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: my-jaeger
spec:
  strategy: production
  storage:
    type: elasticsearch
    elasticsearch:
      nodeCount: 3

Summary

Jaeger 1.22 continues to evolve as a leading distributed tracing solution, providing teams with powerful tools for understanding and debugging distributed applications.