toil
neoq
| toil | neoq | |
|---|---|---|
| 2 | 5 | |
| 870 | 243 | |
| 0.3% | - | |
| 9.2 | 8.3 | |
| 3 days ago | 10 days ago | |
| Python | Go | |
| Apache License 2.0 | MIT License |
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toil
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Show HN: Hatchet – Open-source distributed task queue
a little late now, but I wonder if https://github.com/DataBiosphere/toil might meet your requirements
- Lessons Learned from Running Apache Airflow at Scale
neoq
- Show HN: Hatchet – Open-source distributed task queue
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Choose Postgres Queue Technology
I just want to commend OP - if they’re here - for choosing an int64 for job IDs, and MD5 for hashing the payload in Neoq, the job library linked [0] from the article.
Especially given the emphasis on YAGNI, you don’t need a UUID primary key, and all of its problems they bring for B+trees (that thing RDBMS is built on), nor do you need the collision resistance of SHA256 - the odds of you creating a dupe job hash with MD5 are vanishingly small.
As to the actual topic, it’s fine IFF you carefully monitor for accumulating dead tuples, and adjust auto-vacuum for that table as necessary. While not something you’d run into at the start, at a modest scale you may start to see issues. May. You may also opt to switch to Redis or something else before that point anyway.
[0]: https://github.com/acaloiaro/neoq
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Ask HN: Tell us about your project that's not done yet but you want feedback on
Neoq (https://github.com/acaloiaro/neoq) is a background job processor for Go.
Yes, another one. It began from my desire to have a robust Postgres-backed job processor. What I quickly realized was that the interface in front of the queue was what was really important. This allowed me to add both in-memory and Redis (provided by asynq) backends behind the same interface. Which allows dependent projects to switch between different backends in different settings/durable requirements. E.g. in-memory for testing/development, postgres when you're not running Google-scale jobs, and Redis for all the obvious use cases for a Redis-backed queue.
This allows me to swap out job queue backends without changing a line of job processor code.
I'm familiar with the theory that one shouldn't implement queues on Postgres, and to a large extent, I disagree with those theories. I'm confident you can point out a scenario in which one shouldn't, and I contend that those scenarios are the exception rather than the rule.
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Examples of using task scheduler with Go?
I created a background processor called Neoq (https://github.com/acaloiaro/neoq) that is likely to interest you.
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SQL Maxis: Why We Ditched RabbitMQ and Replaced It with a Postgres Queue
This is exactly the thesis behind neoq: https://github.com/acaloiaro/neoq
What are some alternatives?
common-workflow-language - Repository for the CWL standards. Use https://cwl.discourse.group/ for support 😊
starqueue
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
oban - 💎 Robust job processing in Elixir, backed by modern PostgreSQL and SQLite3
direktiv - Serverless Container Orchestration
tembo - Monorepo for Tembo Operator, Tembo Stacks, and Tembo CLI
windmill - Open-source developer platform to turn scripts into workflows and UIs. Fastest workflow engine (5x vs Airflow). Open-source alternative to Airplane and Retool.
Asynq - Simple, reliable, and efficient distributed task queue in Go
states-language-cadence - States Language on Cadence
pgtt - PostgreSQL extension to create, manage and use Oracle-style Global Temporary Tables and the others RDBMS
cronitor-airflow - Cronitor integration for Airflow
pgjobq - Atomic low latency job queues running on Postgres