skeema VS prometheus

If a tool blindly drops columns, that's just a bad tool! It doesn't mean the concept is flawed.

Thousands of companies successfully use declarative schema management. Google and Facebook are two examples at a large scale, but it's equally beneficial at smaller scales too. As long as the workflow has sufficient guardrails, it's safe and it speeds up development time.

Some companies use it to auto-generate migrations (which are then reviewed/edited), while others use a fully declarative flow (no "migrations", but automated guardrails and human review).

I'm the author of Skeema (https://github.com/skeema/skeema) which has provided declarative flow for MySQL and MariaDB since 2016. Hundreds of companies use it, including GitHub, SendGrid, Cash App, Wix, Etsy, and many others you have likely heard of. Safety is the primary consideration throughout all of Skeema's design: https://www.skeema.io/docs/features/safety/

Meanwhile a few declarative solutions that support Postgres include sqldef, Migra, Tusker (which builds on Migra), and Atlas.

I’d argue the obvious answer is address the lack of great answers for declarative schema migration in PostgreSQL. There is Skeema https://github.com/skeema/skeema but it doesn’t support Postgres and Prisma iirc forces you into an ORM, atlas looks perfect but has a nonstandard license.

Ahh I see now, you've founded https://github.com/skeema/skeema which is great!

Keep it up!

Using tmpfs for MySQL/MariaDB's data directory helps tremendously. If you're using Docker natively on Linux, use `docker run --tmpfs /var/lib/mysql ...` and that'll do the trick. Only downside is each container restart is slightly slower due to having to re-init the database instance from scratch.

Tuning the database server settings can help a lot too. You can add overrides to the very end of your `docker run` command-line, so that they get sent as command-line args to the database server. For example, use --skip-performance-schema to avoid the overhead of performance_schema if you don't need it in your test/CI environment.

For MySQL 8 in particular, I've found a few additional options help quite a lot: --skip-innodb-adaptive-hash-index --innodb-log-writer-threads=off --skip-log-bin

A lot of other options may be workload-specific. My product Skeema [1] can optionally use ephemeral containerized databases [2] for testing DDL and linting database objects, so the workload is very DDL-heavy, which means the settings can be tuned pretty differently than a typical DML-based workload.

[1] https://github.com/skeema/skeema/

[2] https://www.skeema.io/docs/options/#workspace

Atlas hasn’t made it on my radar until now — surprising considering how many stars it has. Based on the description, it looks like it can do something similar to skeema except it isn’t limited to one flavor of sql like skeema. I’m looking forward to trying it out in my next postgres project.

VARCHAR(N) can store N characters. So with utf8mb3, that's a max of 3N bytes worst-case. But with utf8mb4, it's now 4N bytes, which (with a high N) may exceed internal limits such as maximum length of an index key.

IIRC, there were additional problems in older versions of MySQL, situations where sort buffers were sized to a fixed length equal to the value's worst-case size or something like that. So sorting a large number of utf8mb4 values would use a lot more memory than utf8mb3 values (again, iirc, I might be wrong on this).

So the safer and more backwards-compatible approach was to introduce utf8mb4 as a new separate charset, and allow users to choose. MySQL 8 is now transitioning towards deprecating utf8mb3, and will finally make the utf8 alias point to utf8mb4 sometime in the near future.

That said, there are still a bunch of unpleasant uses of utf8mb3 internally in things like information_schema. I develop schema management tooling and recently lost a week to some of the more obscure ones in https://github.com/skeema/skeema/commit/bf38edb :)

I realize my paradigm on schema driven projects comes probably from my background. I found a very similar tool by chance when reading through my latest feeds and found this tool: https://github.com/skeema/skeema

It's like Prometheus, but for logs. Okay it's not really to do with the Norse or Greek gods, instead Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by the open source project Prometheus. Built by Grafana Labs, Loki is designed for ease of use. Instead of indexing the contents of the logs, Loki provides a set of labels for each log stream. The latest update includes query acceleration with Bloom filters, native OTel support, Helm charts, and more. Check out the changelog for all the major changes and deprecations.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.