A few things I've been thinking about recently:

- we have authentication everywhere in our stack, so I've started including the user id on every log line. This makes getting a holistic view of what a user experienced much easier.

- logging an error as a separate log line to the request log is a pain. You can filter for the trace, but it makes it hard to surface "show me all the logs for 5xx requests and the error associated" - it's doable, but it's more difficult than filtering on the status code of the request log

- it's not enough to just start including that context, you have to educate your coworkers that it's now present. I've seen people making life hard for themselves because they didn't realize we'd added this context

Also worth pointing out that you can implement this method with a lot of tools these days. Both structured Logs or Traces lend itself to capture wide events. Just make sure to use a tool that supports general query patterns and has rich visualizations (time-series, histograms).

I worked with enterprise message bus loggers in semiconductor manufacturing context wherein we had thousands of participants on the message bus. It generated something like 300-400 megabytes per hour. Despite the insane volume we made this work really well using just grep and other basic CLI tools.

The logs were mere time series of events. Figuring out the detail about specific events (e.g. a list of all the tools a lot visited) required writing queries into the Oracle monster. You could derive history from the event logs if you had enough patience & disk space, but that would have been very silly given the alternative option. We used them predominantly to establish a casual chain between events when the details are still preliminary. Identifying suspects and such. Actually resolving really complicated business usually requires more than a perfectly detailed log file.

Too bad that all of this effort is spent arguing something which can be summarised as "add structured tags to your logs"

Generally speaking my biggest gripe with wide logs (and other "innovative" solutions to logging) is that whatever perceived benefit you argue for doesn't justify the increased complexity and loss of readability.

We're throwing away `grep "uid=user-123" application.log` to get what? The shipping method of the user attached to every log? Doesn't feel an improvement to me...

P.S. The checkboxes in the wide event builder don't work for me (brave - android)

Taking slow requests as an example, a dependency gets slower and now your log volume suddenly goes up 100x. Can your service handle that? Are you causing a cascading outage due to increased log volumes?

Recovery is easier if your service is doing the same or less work in a degraded state. Increasing logging by 20-100x when degraded is not that.

Logs are fine. The job of local logs is to record the talk of a local process. They are doing this fine. Local logs were never meant to give you a picture of what's going on some other server. For such context, you need a transaction tracing that can stitch the story together across all processes involved.

Usually, looking at the logs at right place should lead you to the root cause.

"Bug parts" (as in "acceptable number of bug parts per candy bar") logging should include the precursors of processing metrics. I think what he calls "wide events" I call bug parts logging in order to emphasize that it also may include signals pertaining to which code paths were taken, how many times, and how long it took.

Logging is not metrics is not auditing. In particular processing can continue if logging (temporarily) fails but not if auditing has failed. I prefer the terminology "observables" to "logging" and "evaluatives" to "metrics".

In mature SCADA systems there is the well-worn notion of a "historian". Read up on it.

A fluid level sensor on CANbus sending events 10x a second isn't telling me whether or not I have enough fuel to get to my destination (a significant question); however, that granularity might be helpful for diagnosing a stuck sensor (or bad connection). It would be impossibly fatiguing and hopelessly distracting to try to answer the significan question from this firehose of low-information events. Even a de-noised fuel gauge doesn't directly diagnose my desired evaluative (will I get there or not?).

Does my fuel gauge need to also serve as the debugging interface for the sensor? No, it does not. Likewise, send metrics / evaluatives to the cloud not logging / observables; when something goes sideways the real work is getting off your ass and taking a look. Take the time to think about what that looks like: maybe that's the best takeaway.

A common problem in a log aggregation is the question if you query for user.id, user_id, userID, buyer.user.id, buyer.id, buyer_user_id, buyer_id, ... Every log aggregation ends up being plagued by this. You need standard field names there, or it becomes a horrible mess.

And for a centralized aggregation, I like ECS' idea of "related". If you have a buyer and a seller, both with user IDs, you'd have a `related.user.id` with both id's in there. This makes it very simple to say "hey, give me everything related to request X" or "give me everything involving user Y in this time frame" (as long as this is kept up to date, naturally)

But does it? Or is it bad logging, or excessive logging, or unsearchable logs?

A client of mine uses SnapLogic, which is a middleware / ETL that's supposed run pipelines in batch mode to pass data around between systems. It generates an enormous amount of logs that are so difficult to access, search and read that they may as well don't exist.

We're replacing all of that with simple Python scripts that do the same thing and generate normal simple logs with simple errors when something's truly wrong or the data is in the wrong format.

Terse logging is what you want, not an exhaustive (and exhausting) torrent of irrelevant information.

I do think "logs are broken" is a bit overstated. The real problem is unstructured events + weak conventions + poor correlation.

Brilliant write up regardless

Also if you're going to log wide events, for the sake of the person querying them after you, please don't let your schema be an ad hoc JSON dict of dicts, put some thought into the schema structure (and better have a logging system that enforces the schema).

Gonna go on a tangent here. Why the single purpose domain? Especially since the author has a blog. My blog is full of links to single post domains that are no longer.

If a user request is hitting that many things, in my view, that is a deeply broken architecture.

There isn't anything radical about his proposed solutions either. Most log storage can be set with a rule where all warning logs or above can be retained, but only a sample of info and debug logs.

The "key insight" is also flawed. The reason why we log at every step is because sometimes your request never completes and it could be for 1000 reasons but you really need to know how far it got in your system. Logging only a summary at the end is happy path thinking.

The framing is not, though. Why does it have to sound so dramatic and provocative? It’s insulting to its audience. Grumpiness, in the long term, is a career-limiting attitude.

Where I've had problems it's usually been one of:

There wasn't anything logged in the error block. A comment saying "never happens" is often discovered later :)

Too much was logged and someone mandated dialing the logging down to save costs. Sigh.

A new thread was started and the thread-local details including the correlation ID got lost, then the error occurred downstream of that. I'd like better solutions for that one.

Edit: Incidentally a correlation ID is not (necessarily) the same thing as a request ID. An API often needs to allow for the caller making multiple calls to achieve an objective; 5 request IDs might be tied to a single correlation ID.

The best way to equip logs to tell the truth is to have other parts of the system consume them as their source of truth.

Firstly: "what the system does" and "what the logs say" can't be two different things.

Secondly: developers can't put less info into the logs than they should, because their feature simply won't work without it.

Point #1 isn't true, auto instrumentation exists and is really good. When I integrate OTel I add my own auto instrumentors wherever possible to automatically add lots of context. Which gets into point #2.

Point #2 also isn't true. It can add business context in a hierarchal manner and ship wide events. You shouldn't have to tell every span all the information again. Just where it appears naturally the first time.

Point #3 also also isn't true because OTel libs make it really annoying to just write a log message and very strongly pushes you into a hierarchy of nested context managers.

Like the author's ideal setup is basically using OTel with Honeycomb. You get the querying and everything. And unlike rawdogging wide events all your traces are connected, can span multiple services and do timing for you.