Our Sanny Days

So, you want to get into our sanny days stuff?

I've been around this long enough to see it come and go a few times. The short version is that our sanny days is basically a workflow or tracking method that some people swear by, and others find completely unnecessary. I'll walk you through how it actually works in practice, because the documentation out there tends to gloss over the parts that trip people up.

What our sanny days actually is

At its core, it's a system for logging, categorizing, and reviewing periodic cycles — most commonly used by developers and ops teams who need to track recurring events across distributed systems. Think of it as a lightweight time-series annotation layer on top of whatever monitoring stack you already have. It doesn't replace your Prometheus or Datadog; it just gives you a way to tag and reason about those recurring patterns without writing custom queries every time. The idea sounds simple, but the implementation has a few wrinkles. Here's what I learned the hard way.

How to set it up (the part nobody explains well)

First, you need to decide on your data source. our sanny days pulls from whatever log pipeline you point it at — Fluent Bit, Vector, even raw file tails. I prefer Vector because it handles backpressure better, but that's just personal preference. The config looks something like this: Define your sources in vectors.toml, then create a transform that normalizes timestamps into the format our sanny days expects (ISO 8601 with timezone). This is where most people hit their first wall. If your logs have inconsistent timezone handling — and they will, trust me — the sanny day boundaries get misaligned and your cycles look wrong.

The fix is to normalize at ingestion time, not at query time. Add a mutate_block in Vector that strips and re-adds timezone info consistently. It adds about 200 microseconds per event, which is nothing compared to the debugging session you'd otherwise spend trying to figure out why your Friday deployments show up on Thursday.

Downloading and installing

You can grab the current release from the official repo. For Linux, the .deb and .rpm packages work straight out of the box on most distributions. macOS users can go through Homebrew. Windows is... adequate. It runs, but if you're on Windows for production workloads, you're already making choices that deserve a second look. After installation, run the init command to create your default config skeleton. It'll drop everything into ~/.our-sanny-days/. Don't skip this step — the defaults are reasonable but they assume you're starting from scratch, which most of you aren't.

A real problem I ran into (and how I fixed it)

Last year I was integrating our sanny days into a pipeline that processed events from three different cloud providers, each with slightly different event timestamp semantics. AWS uses server receipt time, GCP uses event time when available, and Azure... well, Azure has its own fun way of handling this. The result was that our sanny days was creating phantom cycles — grouping events that had nothing to do with each other just because their timestamps happened to fall into the same rolling window. The workaround was to use the source_fingerprint field in the config. Instead of letting our sanny days group purely by timestamp, I added a fingerprint that accounted for the provider and the event type. This meant cycles were grouped by actual logical units rather than just calendar proximity. It took about an hour to reconfigure and rerun the backfill, but it eliminated about 90 percent of the noise. The tradeoff is that you lose some of the automatic cross-source correlation that the default setup gives you, but honestly, that correlation was mostly false positives anyway.

Things the documentation doesn't tell you

Here are a couple of counter-intuitive bits that caught me off guard: More sources don't mean better cycles. I initially thought piling on every possible log source would give me richer cycle detection. It didn't. It gave me noise. I ended up cutting my sources down to the three that actually mattered for the cycles I was trying to track, and the signal quality jumped significantly. The system isn't designed for brute-force ingestion.

The review mode is where the value actually is. Most people treat our sanny days as a set-and-forget thing. The whole point of the tool is the review workflow — the ability to go back through historical cycles, annotate what happened, and build up a knowledge base. If you're not spending time in the review interface, you're only getting half the utility out of it.

When it breaks (and what to do)

Let me be straight about the limitations. our sanny days struggles with high-cardinality event streams. If you're pushing more than about 50,000 distinct event types per day through it, the cycle grouping starts to degrade. The memory footprint grows linearly with cardinality, and you'll hit OOM errors before you hit performance issues. I've seen it crash on streams with heavy IoT telemetry — thousands of device IDs generating unique event patterns. If that's your use case, you're better off pre-aggregating your data before it hits our sanny days. Roll up device-level events into hourly summaries, then feed the summaries in. This usually cuts the processing time from around 45 minutes per cycle to about eight, and it keeps memory usage reasonable.

Another hard limit: our sanny days doesn't handle clock skew well. If your systems have more than a few seconds of drift between them, your cycles will be misaligned. NTP fixes help, but they don't eliminate the problem entirely. I've had cases where a 12-second skew caused what looked like two separate cycles when it was actually one. The workaround is to allow a tolerance window in the config, but that introduces its own false-positive risk.

Bottom line

our sanny days is useful if you have a moderate-volume, moderately-complex environment and you need to track recurring patterns over time. It's not going to solve everything, and it's definitely not plug-and-play for heterogeneous multi-cloud setups without some config work. But for the right workload, it saves you from writing custom aggregation logic that you'd otherwise maintain forever. Start small. Get one source working cleanly. Add complexity gradually. And for the love of whatever you respect, normalize your timestamps at ingestion.