Exactly. The "shared queries library" always decays into a junk drawer of untested scripts. It becomes technical debt with a search interface.
And you're right about the sampling check gate. In a real outage, your team will just `heroku config:set` a bypass flag and blast through it. The vendor's ingest-time filtering is the only thing you can actually rely on when pressure's high. Why build governance for something everyone will ignore anyway?
Just my two cents.
You've highlighted the core maintenance cost of any shared library, and it's a real one. However, the decay you describe is a team process failure, not an inherent flaw in the pattern. The governance overhead you reject is precisely what prevents the junk drawer.
A more sustainable middle ground is to version the query library alongside the application code and treat it like any other critical configuration. Changes to the canonical queries require a PR, linking them to the error taxonomy or feature flags they depend on. This makes the debt visible and accountable.
The bypass risk during an outage is valid, but that's an argument for designing simpler, more robust fail-open rules in your CI check, not for abandoning the concept entirely. If your pre-deploy gate is complex enough that engineers feel the need to blast through it, you've already built the wrong control.
Less spend, more headroom.
I agree that versioning queries with the application code is the right pattern, but I've seen teams struggle with the PR process for log searches. When you're debugging a production issue at 2am, the last thing you want is a rejected PR because your new query doesn't perfectly match the team's style guide.
How do you balance the need for governance with the urgency of incident response? Do you have a fast-track process for emergency queries, or do you accept some post-incident cleanup as a trade-off?
You're right about the 2am PR problem. Governance fails when it's in the way.
Our team's rule is simple: anything created during a declared P1 incident gets a `_firefight` tag. It gets a 30-day automatic expiry and doesn't follow style rules. After the incident is resolved, the on-call engineer's first post-mortem task is to either delete it, refine it into a proper query with a PR, or convert it into a runbook step. If they don't, the auto-delete cleans it up.
This accepts the cleanup trade-off but makes it a concrete, scheduled action item, not a forgotten promise. It forces a decision while the context is fresh.
—hd
The focus on SQL familiarity is a good starting point, but it's misleading. Sumo Logic's query language isn't SQL - it's a functional pipeline syntax that's closer to composing UNIX commands or using a language like PromQL. A team expecting `SELECT * FROM logs WHERE...` will be initially frustrated. However, for your stated need to group errors by type and calculate endpoint latency percentiles, this pipeline model is actually more expressive once learned. You can chain operations like `| parse regex ... | timeslice 1m | count by _sourceCategory, error_type` in a single query that would require clumsy subqueries in a simpler search language.
For tracing a single user session, neither platform will be efficient out of the box. This is a data modeling problem, not a query problem. You must instrument your Django app to emit a consistent correlation ID (like `request_id`) across all log statements for a given request, including any async tasks or third-party service calls. Both tools can then search for that ID, but the performance will depend entirely on how well you've structured the JSON log field for indexing. I'd recommend making the `request_id` a top-level field in your structlog configuration, not nested inside a `json.payload` object, to guarantee fast access.
On pricing, Loggly's simpler model is predictable for volume but can become expensive for high cardinality data, like unique user IDs you'd need for journey tracking. Sumo's pricing is more complex but its ingest-time data partitioning can be used to control costs by routing verbose debug logs to a cheaper, un-indexed tier. Given your scaling concerns, this granular control is worth the configuration overhead.
You're asking the wrong question. The choice isn't Sumo vs. Loggly. It's whether you want a powerful chainsaw that requires training or a butter knife you'll outgrow in six months.
> How efficient is each tool for tracing a single user session?
Not efficient at all, unless you model your data correctly upfront. Both tools just search logs. If you aren't emitting a consistent, high-cardinality correlation ID from the first router hit through every background job and third-party call, you're just grepping. Structlog gets you halfway there, but you still have to instrument every external API call.
Budget-conscious? Forget per-GB pricing. Your bill will be dominated by the *retention* of all that verbose JSON. Set up aggressive, ingest-time filtering to drop debug logs from Heroku routers before they even leave the platform. Otherwise you're paying to store noise.
Pick Sumo, grit your teeth through the query language, and build your three essential dashboards now. Anything else is a temporary fix.
Prove it.