electric VS hatchet

I'm interested in this problem also!

I think there is a large overlap with projects that market/focus on offline-first experiences.

AFAIK this problem can be solved by:

1) Considering a client-side copy of the database that gets synced with the remote DB. This is an approach [PowerSync](https://www.powersync.com/) and [ElectricSql](https://electric-sql.com/) and [rxdb](https://rxdb.info/) take!

Yeah I agree with it being an exaggeration. They are certainly riding the admittedly dated perception that realtime is so hard it's only available to the Googles and Figmas. But there's now some amazing open source solutions available like Y.js and ElectricSQL[1]. The barrier has certainly come down.

[1] https://github.com/electric-sql/electric

https://github.com/electric-sql/electric :

> ElectricSQL is a local-first software platform that makes it easy to develop high-quality, modern apps with instant reactivity, realtime multi-user collaboration and conflict-free offline support.

> Local-first is a new development paradigm where your app code talks directly to an embedded local database and data syncs in the background via active-active database replication. Because the app code talks directly to a local database, apps feel instant. Because data syncs in the background via active-active replication it naturally supports multi-user collaboration and conflict-free offline

"SQLedge: Replicate Postgres to SQLite on the Edge" (2023)

Yup, this is correct.

We have had some requests / discussions around adding hooks to the sync service that will support custom logic on the write path (as per https://github.com/electric-sql/electric/discussions/565). This seems like a good idea but they don't exist yet.

Hatchet (https://hatchet.run) | New York City | Full-time

We're hiring a founding engineer to help us with development on our open-source, distributed task queue: https://github.com/hatchet-dev/hatchet.

We recently launched on HN, you can check out our launch here: https://news.ycombinator.com/item?id=39643136. We're two second-time YC founders in this for the long haul and we are just wrapping up the YC W24 batch.

As a founding engineer, you'll be responsible for contributing across the entire codebase. We'll compensate accordingly and with high equity. It's currently just the two founders + a part-time contractor. We're all technical and contribute code.

Stack: Typescript/React, Go and PostgreSQL.

To apply, email alexander [at] hatchet [dot] run, and include the following:

1. Tell us about something impressive you've built.

2. Ask a question or write a comment about the state of the project. For example: a file that stood out to you in the codebase, a Github issue or discussion that piqued your interest, a general comment on distributed systems/task queues, or why our code is bad and how you could improve it.

Can you explain why you chose every function to take in context? https://github.com/hatchet-dev/hatchet/blob/main/python-sdk/...

This seems like a lot of boiler plate to write functions with to me (context I created http://github.com/DAGWorks-Inc/hamilton).

Hello HN, we're Gabe and Alexander from Hatchet (https://hatchet.run), we're working on an open-source, distributed task queue. It's an alternative to tools like Celery for Python and BullMQ for Node.js, primarily focused on reliability and observability. It uses Postgres for the underlying queue.

Why build another managed queue? We wanted to build something with the benefits of full transactional enqueueing - particularly for dependent, DAG-style execution - and felt strongly that Postgres solves for 99.9% of queueing use-cases better than most alternatives (Celery uses Redis or RabbitMQ as a broker, BullMQ uses Redis). Since the introduction of SKIP LOCKED and the milestones of recent PG releases (like active-active replication), it's becoming more feasible to horizontally scale Postgres across multiple regions and vertically scale to 10k TPS or more. Many queues (like BullMQ) are built on Redis and data loss can occur when suffering OOM if you're not careful, and using PG helps avoid an entire class of problems.

We also wanted something that was significantly easier to use and debug for application developers. A lot of times the burden of building task observability falls on the infra/platform team (for example, asking the infra team to build a Grafana view for their tasks based on exported prom metrics). We're building this type of observability directly into Hatchet.

What do we mean by "distributed"? You can run workers (the instances which run tasks) across multiple VMs, clusters and regions - they are remotely invoked via a long-lived gRPC connection with the Hatchet queue. We've attempted to optimize our latency to get our task start times down to 25-50ms and much more optimization is on the roadmap.

We also support a number of extra features that you'd expect, like retries, timeouts, cron schedules, dependent tasks. A few things we're currently working on - we use RabbitMQ (confusing, yes) for pub/sub between engine components and would prefer to just use Postgres, but didn't want to spend additional time on the exchange logic until we built a stable underlying queue. We are also considering the use of NATS for engine-engine and engine-worker connections.

We'd greatly appreciate any feedback you have and hope you get the chance to try out Hatchet.

This is a great question, thanks for asking.

We are testing workflows internally that use orchestration software like Hatchet/Temporal to allow the framework to robustly handle 100s of GBs of upload data from parsing to chunking to embedding to storing [1][2]. The goal is to build durable execution at each step, because even steps like PDF extraction can be expensive / time consuming. We are targeting an prelim. release of these features in < 1 month.

Logging is built natively into the framework with postgres or sqlite options. We ship a GUI that leverages these logs and the application flow to allow developers to see queries, search results, and RAG completions in realtime.

We are planning on adding more features here to help with evaluation / insight as we get further feedback.

On the A/B, slow rollout, and analytics side, we are still early but suspect there is a lot of value to be had here, particularly because human feedback is pretty crucial in optimizing any RAG system. Developer feedback will be particularly important here since there are a lot of paths to choose between.

[1] https://hatchet.run/

Absolutely -- just created an issue if you'd like to follow along or provide feedback!