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https://fly.io
- Easy Postgres clusters (but not managed)
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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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I'm starting to consider this myself and have been looking at https://serverless-stack.com/#guide as a way to prototype and build an MVP. The guide has quite a bit in it that I believe can be repurposed to that end although it doesn't cover backups etc, more integrations with AWS services.
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I've used this in the past https://github.com/piyiotisk/fieldbot-server/blob/master/.gi...
Basically, building a docker image and server it using Docker compose. Of course you'll need NGINx as a reverse proxy running on the server.
If I do it again I'll use a service like render. Not worth managing this myself.
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Choose a platform like App Engine. It's auto-scaling. Supports bigtable. And opentelemetry. Manageable from anywhere via Cloud Shell. You can run multiple instances and partition load between them. Even includes a free tier ;)
https://github.com/GoogleCloudPlatform/golang-samples
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I'm building an open-core data IDE that runs as a desktop application or server. Since it's open source, all tests run on Github Actions for free. This includes basic e2e testing using Selenium on Windows, macOS, and Linux (e.g. [0]); and unit/integration tests.
If it were a private repo I'd still shell out for Github Actions or Circle CI most likely. I'd also consider buying a chunky-enough minipc for ~$500 and an older mac mini and set up runners on them.
For the moment private runners isn't a problem. But soon I'll need to start integration-testing proprietary code paths like querying Oracle or MS SQL Server. In that case I probably need to set up a dedicated box with all the right licenses so I can run CI jobs on it.
[0] https://github.com/multiprocessio/datastation/blob/master/.g...
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Grafana
The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
> CI
GitHub Actions
> deployments/rollbacks
Docker. Scaleway offers a container registry that's ridiculously cheap[1]. Deployments are infrequent and executed manually.
> DBs
Again, Scaleway's managed RDS[2].
Outside these, we have setup Grafana + Loki cloud[3] for monitoring and alerting. They have a generous free plan. For easy product analytics that can be derived from the database we've a self hosted instance of Metabase[4].
[1]: https://www.scaleway.com/en/container-registry/
[2]: https://www.scaleway.com/en/database/
[3]: https://grafana.com/
[4]: https://www.metabase.com/
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InfluxDB
InfluxDB high-performance time series database. Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
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Lambdas and firebase on the GCP stack for CRUD apps.
One nice thing about firebase -> each PR deploys to its own preview channel[1].
Downside: Very JS heavy. I write lambdas in python though.
[1] https://firebase.google.com/docs/hosting/github-integration
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I decided to take a few years off work to just build on what I'd like. Perhaps in a startup studio model, so I have a bias for having something that is easily reusable, and that uses tech someone else can pick up and run with easily. I'll probably be in the business of dev/infra tooling.
Currently going with a container image as the minimal deployable unit that gets put on top of a clean up to date OS. For me that's created with a Dockerfile using Alpine image variants. In a way I could see someone's rsync as an ok equivalent, but I'd do versioned symlinked directories so I can easily roll back if necessary if I went with this method. Something like update-alternatives or UIUC Encap/Epk: https://www.ks.uiuc.edu/Development/Computers/docs/sysadmin/.... Anyone remember that? I guess the modern version of Epkg with dependencies these days is https://docs.brew.sh/Homebrew-on-Linux. :-) Or maybe Nixpkgs: https://github.com/NixOS/nixpkgs?
Deployment-wise I've already done the Bash script writing thing to help a friend automate his deployment to EC2 instance. For myself I was going to start using boto3, but just went ahead and learned Terraform instead. So now my scripts are just simple wrappers for Docker/Terraform that build, push, or deploy that work with AWS ECS Fargate or DigitalOcean Kubernetes.
No CI/CD yet. DBs/backups I'll tackle next as I want to make sure I can install or failover to a new datacenter without much difficulty.
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linuxbrew-core
Discontinued 💀Formerly the core formulae for the Homebrew package manager on Linux
I decided to take a few years off work to just build on what I'd like. Perhaps in a startup studio model, so I have a bias for having something that is easily reusable, and that uses tech someone else can pick up and run with easily. I'll probably be in the business of dev/infra tooling.
Currently going with a container image as the minimal deployable unit that gets put on top of a clean up to date OS. For me that's created with a Dockerfile using Alpine image variants. In a way I could see someone's rsync as an ok equivalent, but I'd do versioned symlinked directories so I can easily roll back if necessary if I went with this method. Something like update-alternatives or UIUC Encap/Epk: https://www.ks.uiuc.edu/Development/Computers/docs/sysadmin/.... Anyone remember that? I guess the modern version of Epkg with dependencies these days is https://docs.brew.sh/Homebrew-on-Linux. :-) Or maybe Nixpkgs: https://github.com/NixOS/nixpkgs?
Deployment-wise I've already done the Bash script writing thing to help a friend automate his deployment to EC2 instance. For myself I was going to start using boto3, but just went ahead and learned Terraform instead. So now my scripts are just simple wrappers for Docker/Terraform that build, push, or deploy that work with AWS ECS Fargate or DigitalOcean Kubernetes.
No CI/CD yet. DBs/backups I'll tackle next as I want to make sure I can install or failover to a new datacenter without much difficulty.
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My web app is hosted on a server on Hetzner Cloud. I don't use Docker.
For:
* Database: PostgreSQL installed through apt in the same server: https://github.com/sirodoht/mataroa/blob/master/docs/server-...
* Backups: MinIO-upload to an S3-compatible object storage: https://github.com/sirodoht/mataroa/blob/master/backup-datab...
* CI: Github Actions + sr.ht builds: https://github.com/sirodoht/mataroa/blob/master/.github/work... + https://github.com/sirodoht/mataroa/blob/master/.build.yml
* CD: (not exactly CD but...) ssh + git pull + uWSGI reload: https://github.com/sirodoht/mataroa/blob/master/deploy.sh
* Rollbacks: git revert HEAD + ./deploy.sh
* Architecture pattern: stick to the monolith; avoid to deploy another service at all costs; eg. we need to send multiple emails? not celery, that would mean hosting a redis/rabbitmq. We already have a database so let's use that. We can also use Django management commands and cron: https://github.com/sirodoht/mataroa/blob/5bb46e05524d99c346c... + https://github.com/sirodoht/mataroa/blob/master/main/managem...
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I made my own HTTP app. server and JSON database on top of that.
The server accepts .jars with code (and files) so I can hotdeploy while developing on live on the entire cluster in real time. My turnaround is about 1 second.
The JSON database allows for schema-less simplicity, and it has all the features you need like indexes and security (and then some, like global realtime distributed while still being performant) in 2000 lines of code.
I have zero pain developing the most scalable (and energy efficient) backend in the world, yet very few seem to care or use it: https://github.com/tinspin/rupy
It has been proven on a real project with 5 years uptime and 350.000 users: https://store.steampowered.com/app/486310/Meadow/
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external-dns
Configure external DNS servers (AWS Route53, Google CloudDNS and others) for Kubernetes Ingresses and Services
Via annotations on my ingresses, and of course with this : https://github.com/kubernetes-sigs/external-dns
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I agree with most people here. Definitely keep it simple.
With that said, I run a semi resource intensive operation, so I've invested a bit into dev ops to keep our costs down. My setup is currently on AWS, primarily using ECS, RDS, Elasticache. Infra is managed via Terraform.
I felt ECS was a nice balance vs K8s, as it's much simpler to manage, while getting the benefit of maximizing resource utilization.
For CI / deployment, I use Github Actions to build an image and push, then start a rolling refresh to update the containers to the new version. It was pretty easy to setup.
On DBs, RDS handles all the backups and maintenance. For migrations, I use https://github.com/amacneil/dbmate.
Happy to answer any other questions you have, as I've learned a lot through trial and error.
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- docker-compose to spin up everything. It's super nice. Again, the deployment is done with a `rsync` then `docker-compose up -f docker-compose-prod.yml`
Eventually when deployment changes very frequent and need scale/ha I added in Kubernetes. K8S is way easiser to setup than you think and it handle all other suff(load balancer, environment variable etc).
And my deploy now become: `kubectl apply -f`
One trick I used is to use `sed` or `envsubst` to replace the image hash.
For backedup, I again, literally setup cronjob from an external server, `ssh` into database and run `pgdump`.
I also have a nice NFS server to centralize config and sync back to our git repo.
I used this whole setup to operate https://hanami.run an email forwarding service for the first 3 months before I added Kubernetes.
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In my case I'm dumping + zipping the entire database at the application level. In my case is as simple as adding a library [1], scheduling the job and transferring to AWS S3 (my main application is on DigitalOcean)
[1] https://github.com/spatie/laravel-backup
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I try to avoid any complicated tool and simplify my life with NoOps tools. Using Kubernetes or AWS from scratch is probably going to kill your startup.
In my case, I have tried MetaCall: https://metacall.io
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
