Flyway VS prometheus

The session logs show that the application loads configurations and establishes a connection with a PostgreSQL database. This involves initializing a HikariCP connection pool and Flyway for database migrations. The logs confirm that the database schema validation and migration checks were successful. The startup of the Jetty HTTP server follows, and the server becomes operational and ready to accept requests on the specified port.

Also RedGate, but Flyway has some reasons to recommend it over RedGate Deploy depending on your DBAs/workflows: https://flywaydb.org/

(Though I don't think it is "complete" or "perfect", either.)

EF Migrations are in a really good place now if you like/don't mind C# as a language (and you can easily embed SQL inside the C#, too, but there are benefits to being able to also run high level C# code). With today's tooling you can package your migration "runner application" as a single deployable executable for most platforms. You can build the executable once and run it in all your environments. (The same tool that updates your QA and Staging updates your Prod, testably running the same migrations.) Given the single executable deployable I might even consider using it for projects not themselves written in C#.

There is a bit of tooling needed but is already around. For Java for example I had very good experience with a combination of flyway [1] for migrations, testcontainers [2] for making integration tests as easy as unit tests and querydsl [3] for a query and mapping layer.

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

[2] https://java.testcontainers.org/modules/databases/postgres/

[3] https://github.com/querydsl/querydsl

When software starts using a database, it's advisable to have version control, just as we have Github to control our source code. This is all to be sure about what was executed for that specific version. For Java and Spring boot, we have the Flyway framework that aims to resolve this situation, free of charge.

If you're looking for tools, like https://www.liquibase.com/ or https://flywaydb.org/, which are database-state-based schema migration toolkits - it might be relatively straightforward to build similar ones using Databricks SQL drivers.

Honestly I kind of wish there was a Lukas Eder database migration library. Call it whatever jooq-migration. At least I would have more insight of what is going on (<-- seriously look at the commit history).

This is a 4th option, which should play nice with ArgoCD. The following example runs flyway as a k8s job. The desired migration changes are recorded as files within the chart. This helm chart can be integrated with your application (Using hooks to determine when the migration job is run) or run manually.

By using an opinionated framework within the app/service (like Flyway, Migrate, Diesel, etc). Schema migrations happen on app/service start-up.

Depending on the language or platform there are libraries you can use to manage this, such as Prisma on node and Flyway for Java/JVM.

It's probably overkill but I've used github plus flyway at a couple places in the past which is pretty nice tool for tracking changes to a variety of db's, it's also very helpful if you ever need to replicate a db in a new region/environment.

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.

wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz

Prometheus: Open-source monitoring system. Often used together with Grafana.