I still remember the days when debugging a latency spike in a microservices architecture took hours. It wasn't until I set up distributed tracing that I realized how blind we were. With distributed tracing, you can pinpoint the root cause in minutes.
The trace context problem is a major hurdle in distributed tracing. Without it, every request needs to carry the required context through each hop. The W3C Trace Context specification, finalized in 2020, defines the standard header format, making it a vendor-agnostic solution.
One of the challenges I've seen in production is dealing with the sheer volume of spans generated by microservices. For instance, a payment processing service might generate hundreds of thousands of spans per second when handling a surge in transactions. To put this into perspective, a single span can consume around 200 bytes of storage space, so storing all of them for an extended period becomes a significant storage requirement.
Tracing every single request at high volumes gets expensive fast. You need a sampling strategy in place. Head-based sampling is simple but will leave you missing the latency spikes in the tail. Priority sampling and tail-based sampling are better alternatives, and OpenTelemetry Collector supports the latter.
In my experience, choosing the right sampling strategy can be a trade-off between capturing relevant data and keeping storage costs under control. For example, if you're dealing with a high-volume service, you might want to use tail-based sampling to capture the latency spikes in the tail. However, this might require more storage to store the additional spans.
Jaeger's all-in-one deployment is perfect for development and testing, but production requires a more robust setup. You'll need to separate the collector, query service, and durable backend to handle high-throughput span ingestion. Once you hit hundreds of thousands of spans per second, storage becomes your bottleneck.
The magic moment in debugging microservices is jumping from a trace in Jaeger directly to the matching log lines. To make this work, inject the trace ID into your structured logs, and use a logging framework like Serilog or NLog. Then, in Grafana, click a trace and jump to the correlated logs – it's a game-changer.