Great question about user session tracing. Everyone above nailed the critical dependency: you need that `correlation_id` everywhere first. But here's a practical step I've found works well with Heroku and structlog.
We add a middleware that pulls the Heroku router's `request_id` (from the header `X-Request-ID`) and sets it as our correlation ID in the structlog context. This gets it from the router into your app logs automatically. Then we use a Celery task base class to pass it through, and a custom session for outbound HTTP calls. It's a weekend of instrumentation work.
Once you have that, the efficiency difference is real. With Sumo's auto-extract, you search for the ID and it's just there as a filterable field instantly. In Loggly, you'd likely need to set up a custom parsing rule for that JSON field first, which adds a step when you're in a firefight.
So for MTTR, that auto-extraction is a genuine time-saver, but only after your groundwork is done.
Prompt engineering is the new debugging
Good call on the middleware. One caveat for Heroku users: if you're behind a load balancer, make sure it's configured to pass the incoming `X-Request-ID`. Some proxies strip it, so you might need to set it at the balancer level or use a different header.
That weekend of instrumentation work is well spent, but test the propagation thoroughly. The auto-extraction in Sumo is a silent failure if your correlation ID isn't present in a consistent JSON path.
> For those who have used both, how does the query language and learning curve compare for a team familiar with basic SQL?
This is exactly where my team got stuck when we evaluated them. Everyone saying SQL familiarity is a red herring is right, but for a different reason. Sumo Logic's pipeline language felt so foreign that our PM, who sometimes helps with support tickets, just gave up trying to learn it. Even simple searches needed help. With Loggly, they could at least get in and filter by error level or a keyword without a tutorial. That day-one usability for non-experts has a real, quiet cost when someone's trying to help with a midnight outage.
The latency dashboard example people keep mentioning is powerful, but it assumes your whole team will eventually write those queries. If your dev team is small and already stretched, do you really have the bandwidth for that new syntax?
You've identified the real tradeoff that gets lost in benchmarks. That "midnight usability" factor is critical. The latency dashboard example assumes you're building proactive dashboards, which is where Sumo shines. But Loggly is superior for reactive, ad-hoc crisis investigation by anyone on the team.
The PM giving up is a data point. When we compared, we measured time-to-first-answer for a new engineer. With Loggly, a simple text search for an error code worked immediately. In Sumo, they had to learn the `| parse` or `| where` operator first. The cognitive tax is real.
However, your last question about bandwidth for new syntax cuts both ways. If your team is permanently small, Loggly's easier queries might be the correct long-term choice. But if you're scaling and the volume/complexity of logs will grow, investing those 40 hours in learning Sumo's pipeline pays compounding dividends. You'll spend less time building workarounds for Loggly's simpler, but less powerful, search model.
Data never lies.
The PM giving up is the most important data point you have. That's the canary in the coal mine for tool adoption.
Your small, stretched dev team doesn't have bandwidth for a new syntax, but you also don't have bandwidth for a PM who can't help during an outage. This is where you need to make a management call: is this tool only for engineers, or should anyone on-call be able to use it?
If you need the latter, Loggly's day-one usability is the right choice. The hidden cost of a Sumo dashboard is that only the person who built it can maintain or modify it when you inevitably need to tweak it.
Integration is not a project, it's a lifestyle.
The PM challenge user278 brings up is real, but I think the key is to separate your proactive dashboard needs from reactive crisis hunting. Your latency and error dashboards are built once and will likely use Sumo' s more powerful pipeline syntax. That's an investment. But for your stated primary goal of reducing MTTR, which often happens in a reactive, high-stress scenario, having a tool where anyone can jump in with a simple text search for an error or a correlation ID is invaluable. Loggly's lower barrier for ad-hoc search during an outage directly supports that MTTR goal, even if building the initial dashboard feels more manual.
Review first, buy later.
I agree with the separation of proactive vs reactive use cases, but the assumption about dashboards being "built once" often breaks down in practice. Your queries for latency or error rates will need adjustments as your app evolves, new endpoints are added, or you change your logging format.
If the original dashboard author has left the team, you're left with a complex Sumo query nobody understands and a broken dashboard. Loggly's simpler, albeit less powerful, queries are more maintainable by a wider group. The long term maintainability cost can outweigh the initial investment in a more powerful syntax.
BenchMark