That one time I used Go panics for flow control
The article discusses a situation where a key service in a support infrastructure became overloaded due to high query demand. The author explains how they implemented a workaround using Go's panic mechanism for flow control to handle cancellation of long-running sort operations. This approach, while not typical, allowed them to avoid unnecessary processing when queries were abandoned.
- ▪A support service was overloaded due to high query demand and retries from upstream services.
- ▪The sorting phase of queries was consuming most of the CPU time, and traditional context cancellation was not applicable.
- ▪The author used Go's panic mechanism to implement a non-local control flow for handling query cancellations.
Lobsters files mainly under programming. We currently carry 187 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Noncrab |
| Canonical URL | https://noncrab.net/posts/panic-as-flow-control/ |
| Publication time | Sat, 23 May 2026 04:57:28 -0500 |
| Retrieval time | 2026-05-23T10:07:26.306Z |
| Last seen | 2026-05-23T10:07:26.306Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | RrCyTSikxyky |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
How our protagonist discovered that a key service that powers our support was absurdly vulnerable to overload, and what we did to fix it. Part of our support infrastructure at work is an in-memory datastore, that allows us to query our outstanding support work over various dimensions, such as work type, whether it's been put on hold for some reason, etc. It's functionally equivalent to a single table in an SQL database, where you have a single dataset, boolean filters and configurable sorting. At work, we have an in-memory datastore that powers part of our support infrastructure. Its kind of analgous to having bitmap filters with post-hoc filtering, so any use of sort/limit will sort the entire result set.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Noncrab.
Discussion
0 commentsMore from Noncrab
