aperture
cue
aperture | cue | |
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28 | 109 | |
590 | 4,765 | |
1.7% | 1.4% | |
9.8 | 9.8 | |
3 days ago | 2 days ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
aperture
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Defcon: Meta's system for preventing overload with graceful feature degradation
Anyone interested in load shedding and graceful degradation with request prioritization should check out the Aperture OSS project.
https://github.com/fluxninja/aperture
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Queues Don't Fix Overload
I agree that queues can problem especially when misconfigured. But some amount of queuing is necessary, to absorb short spikes in demand vs capacity. Also, queues can be helpful to re-order requests based on criticality which won't be possible with zero queue size - in which case we have to immediately drop a request or admit it without considering it's priority.
I think it is beneficial to re-think how we tune queues. Instead of setting a queue size, we should be tuning the max permissible latency in the queue which is what a request timeout actually is. That way, you stay within the acceptable response time SLA while keeping only the serve-able requests in the queue.
Aperture, an open-source load management platform took this approach. Each request specifies a timeout for which it is willing to stay in the queue. And weighted fair queuing scheduler then allocates the capacity (a request quota or max number of in-flight request) across requests based on the priority and tokens (request heaviness) of each request.
Read more about the WFQ scheduler in Aperture: https://docs.fluxninja.com/concepts/scheduler
Link to Aperture's GitHub: https://github.com/fluxninja/aperture
Would love to hear your thoughts on our approach!
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Kelsey Hightower's Twitter Spaces on Rate Limits & Flow Control
For those keen to dive deeper, I highly recommend exploring both the Twitter Space and Aperture: [Twitter Spaces]: https://twitter.com/kelseyhightower/status/1689355284802629633?s=20 [GitHub repo]: https://github.com/fluxninja/aperture
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Graceful Behavior at Capacity
Very interesting blog post! Our team has been working intensively in this area for the last couple of years - flow control, load shedding, controllability (PID control), and so on.
We have open-sourced our work at - https://github.com/fluxninja/aperture
We would love feedback from folks reading this blog post!
Disclaimer: I am one of the co-authors of the Aperture project. There are several interesting ideas we have built into this project and I will be happy to dive into the technical details as well.
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Why Adaptive Rate Limiting Is a Game-Changer
It's a blog on an open-source project that precisely tells you how to implement adaptive rate limiting.
Just click around a bit:
- https://github.com/fluxninja/aperture
- https://docs.fluxninja.com/use-cases/adaptive-service-protec...
Note: I am one of the authors' of this project.
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Show HN: Review GitHub PRs with AI/LLMs
At the time of writing, the first sample image on that page is this:
https://coderabbit.ai/assets/section-1-f9a48066.png
which recommends adding a "maxIterations" counter to the "for len(executedComponents) ..." loop here:
https://github.com/fluxninja/aperture/blob/26e00ea818c7c28da...
HOWEVER
- the review has failed to notice the logic using "numExecutedBefore" (around line 377) that already prevents the specific bug it is suggesting a fix for
- the suggested change decrements "maxIterations" inside the "for ... range circuit.components {" loop which means it isn't counting iterations, it's counting components
This kind of suggestion is particularly nasty because it's unlikely that the test suite populates enough components to hit "maxIterations" - so an inattentive reader could accept it, get a green build, and then deploy a production bug!
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June 25th, 2023 Deno Deploy Postmortem
The need an adaptive protection system like Aperture[0] to mitigate overloads.
[0]: https://github.com/fluxninja/aperture
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Jsonnet – The Data Templating Language
It’s customized to our policy spec. But you can learn from this and adapt it to your spec.
https://github.com/fluxninja/aperture/blob/main/scripts/json...
- Show HN: Aperture – Unified Reliability Management for Microservices
- Failure Mitigation for Microservices: An Intro to Aperture
cue
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TypeSpec: A New Language for API-Centric Development
If you are in a situation where you have a backend and you want to expose an API and then you would eventually want a client, you would need format specs as the starting point where server and clients are generated from that one source.
At the moment, OpenAPI with YAML is the only way to go but you can't easily split the spec into separate files as you would do any program with packages, modules and what not.
There are third party tools[0] which are archived and the libraries they depend upon are up for adoption.
In that space, either you can use something like cue language 1] or something like TypeSpec which is purpose built for this so yet, this seems like a great tool although I have not tried it yet myself.
[0]. https://github.com/APIDevTools/swagger-cli
[1]. https://cuelang.org/
EDIT: formating
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Show HN: Workout Tracker – self-hosted, single binary web application
Where `kube.cue` sets reasonable defaults (e.g. image is /). The "cluster" runs on a mini PC in my basement, and I have a small Digital Ocean VM with a static IP acting as an ingress (networking via Tailscale). Backups to cloud storage with restic, alerting/monitoring with Prometheus/Grafana, Caddy/Tailscale for local ingress.
[1] https://www.talos.dev/
[2] https://cuelang.org/
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Apple releases Pkl – onfiguration as code language
I've been somewhat surprised that CUE bills itself as "tooling friendly" and doesn't yet have a language server- the number one bit of tooling most devs use for a particular language.
I'm assuming it's becaus CUE is still unstable?
Anyway, if others are interested in CUE's LSP work, I think https://github.com/cue-lang/cue/issues/142 is the issue to subscribe to
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Why the fuck are we templating YAML? (2019)
This is where I usually pitch in with "Have your heard of CUELang, our lord and savior?": https://cuelang.org/
- Not turing complete
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10 Ways for Kubernetes Declarative Configuration Management
CUE: The core problem CUE solves is "type checking", which is mainly used in configuration constraint verification scenarios and simple cloud native configuration scenarios.
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Lua is a viable alternative for JSON
If you really want executable configurations please consider a newer language like https://dascript.org or https://cuelang.org which provide better type safety.
1- https://news.ycombinator.com/item?id=38030778
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Writerside – a new technical writing environment from JetBrains
Markdown and XML are nice, but what about more advanced documentation formats like OpenAPI? For one recent project, I set up automatic generation of the OpenAPI docs from (much more compact and flexible) CUE definitions (https://cuelang.org/) - which has the bonus of also being able to test the API against the definitions. JetBrains has a CUE plugin, but it's really barebones (doesn't even support jumping from the usage of a schema to its definition). Of course the possibilities when generating docs are endless (just think of the various syntaxes for doc comments, embedding examples/tests in source code etc.)...
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Show HN: Config-file-validator – CLI tool to validate all your config files
It doesn't include validators for TOML and INI, but if you're doing JSON and YAML, I would take a look at using or building upon CUE (https://cuelang.org/). It is a different take on schema definition (plus more), and is surprising terse and powerful model.
- That's a Lot of YAML
- An INI Critique of TOML
What are some alternatives?
rules_jsonnet - Jsonnet rules for Bazel
dhall-lang - Maintainable configuration files
slo-exporter - Slo-exporter computes standardized SLI and SLO metrics based on events coming from various data sources.
jsonnet - Jsonnet - The data templating language
awesome-sre-tools - A curated list of Site Reliability and Production Engineering Tools
terraform - Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
now-boltwall - Vercel lambda deployment for a Nodejs Lightning-powered Paywall
starlark-rust - A Rust implementation of the Starlark language
ai-pr-reviewer - AI-based Pull Request Summarizer and Reviewer with Chat Capabilities.
Protobuf - Protocol Buffers - Google's data interchange format
etleneum - the centralized smart contract platform
jsonnet-libs - Grafana Labs' Jsonnet libraries