When a team moves from a monolith into microservices and event-driven, asynchronous systems, it inherits a class of problems that used to be someone else's: work that fails halfway through, steps that must not run twice, calls that return before the work is done. Temporal is a durable execution engine that handles a lot of this - you define a multi-step process, and it guarantees the process runs to completion... - Source: dev.to / 20 days ago

Durable execution — the pattern implemented by Temporal, Inngest, Rivet Actors, and now Cloudflare Workflows — treats waiting as a continuation rather than a loop:. - Source: dev.to / 25 days ago

Two specific moves stand out in Duncan's account. The first is durable execution, via Temporal — Mercury replaced fragile cron-and-database state machines with workflow code whose failure semantics are platform-handled (replay, retry, timeout, cancellation). Mercury open-sourced its hs-temporal-sdk, which wraps Temporal's official Rust Core SDK via FFI and provides a Haskell-native API. The dovetail with Haskell's... - Source: dev.to / about 2 months ago

We picked Temporal as the first reference engine on purpose. Temporal has the strictest execution model we know of – a V8 sandbox, determinism constraints, replay-driven recovery. If our port contract holds up against that, easier engines – an in-memory test double, a BullMQ queue, or JSON-first platforms like Inngest or Restate – plug in through the same two interfaces. We're shipping Temporal first; the rest is... - Source: dev.to / 3 months ago

The trick is to find whatever metadata channel the queue already gives you and use that and thankfully, almost every mature queue has one (probably because of this scenario). SQS has message attributes, Temporal has context propagators built into the SDK, and Hatchet (which we use to run our workflows) has a metadata field called additionalMetadata. - Source: dev.to / 4 months ago

These "benchmarks" are useless, they're not testing anything real world except the performance of uWebsockets. There are copy errors all over the place. And then an advertisement: https://github.com/cirospaciari/socketify.py#briefcase-comme... Is this a professional framework that produces proper, real-world benchmarks and... - Source: Hacker News / over 3 years ago

After starting the project called socketify.py at https://github.com/cirospaciari/socketify.py, I got pretty good results and reviews, but many people asked if socketify.py could be used to create a WSGI and ASGI server. WSGI and ASGI have a lot of overhead, that's is why I choose not to use them in the first place, but adding an ASGI and WSGI server allows a lot of code already written to run faster! Source: over 3 years ago