dolt VS prometheus

Hi, this is my project :)

For us this package is most important as the query engine that powers Dolt:

https://github.com/dolthub/dolt

We aren't the original authors but have contributed the vast majority of its code at this point. Here's the origin story if you're interested:

https://www.dolthub.com/blog/2020-05-04-adopting-go-mysql-se...

It's a pretty good default stance, yeah.

We have been trying to convince people to use our new database [1] for several years and it's an uphill battle, because Postgres really is the best choice for most people. They really have to need our unique feature (version control) to even consider it over Postgres, and I don't blame them.

[1] https://github.com/dolthub/dolt

We implemented a query optimizer with a flexible intermediate representation in pure Go:

https://github.com/dolthub/go-mysql-server

Getting the IR correct so that it's both easy to use and flexible enough to be useful is a really interesting design challenge. Our primary abstraction in the query plan is called a Node, and is way more general than the IR type described in the article from OP. This has probably hurt us: we only recently separated the responsibility to fetch rows into its own part of the runtime, out of the IR -- originally row fetching was coupled to the Node type directly.

This is also the query engine that Dolt uses:

https://github.com/dolthub/dolt

But it has a plug-in architecture, so you can use the engine on any data source that implements a handful of Go interface.

Just want to point out that we're announcing development on the project. It's absolutely not ready for mainstream use yet! We have Dolt (https://github.com/dolthub/dolt) which is production-ready and widely in use, but it uses MySQL's syntax and wire protocol. We are building the Dolt equivalent for PostgreSQL, which is DoltgreSQL, but it's only pre-alpha.

Interesting that branching is now better supported and almost free. I wonder if merging can be simplified or whether it already is as simple and as fast as it can be?

I guess I am inspired by Dolt’s ability to branch and merge: https://github.com/dolthub/dolt

#. SQLite WAL mode

From https://www.sqlite.org/isolation.html https://news.ycombinator.com/item?id=32247085 :

> [sqlite] WAL mode permits simultaneous readers and writers. It can do this because changes do not overwrite the original database file, but rather go into the separate write-ahead log file. That means that readers can continue to read the old, original, unaltered content from the original database file at the same time that the writer is appending to the write-ahead log

#. superfly/litefs: aFUSE-based file system for replicating SQLite https://github.com/superfly/litefs

#. sqldiff: https://www.sqlite.org/sqldiff.html https://news.ycombinator.com/item?id=31265005

#. dolthub/dolt: https://github.com/dolthub/dolt

> Dolt can be set up as a replica of your existing MySQL or MariaDB database using standard MySQL binlog replication. Every write becomes a Dolt commit. This is a great way to get the version control benefits of Dolt and keep an existing MySQL or MariaDB database.

#. pganalyze/libpg_query: https://github.com/pganalyze/libpg_query :

> C library for accessing the PostgreSQL parser outside of the server environment

#. Ibis + Substrait [ + DuckDB ]

> ibis strives to provide a consistent interface for interacting with a multitude of different analytical execution engines, most of which (but not all) speak some dialect of SQL.

> Today, Ibis accomplishes this with a lot of help from `sqlalchemy` and `sqlglot` to handle differences in dialect, or we interact directly with available Python bindings (for instance with the pandas, datafusion, and polars backends).

> [...] `Substrait` is a new cross-language serialization format for communicating (among other things) query plans. It's still in its early days, but there is already nascent support for Substrait in Apache Arrow, DuckDB, and Velox.

#. benbjohnson/postlite: https://github.com/benbjohnson/postlite

> postlite is a network proxy to allow access to remote SQLite databases over the Postgres wire protocol. This allows GUI tools to be used on remote SQLite databases which can make administration easier.

> The proxy works by translating Postgres frontend wire messages into SQLite transactions and converting results back into Postgres response wire messages. Many Postgres clients also inspect the pg_catalog to determine system information so Postlite mirrors this catalog by using an attached in-memory database with virtual tables. The proxy also performs minor rewriting on these system queries to convert them to usable SQLite syntax.

> Note: This software is in alpha. Please report bugs. Postlite doesn't alter your database unless you issue INSERT, UPDATE, DELETE commands so it's probably safe. If anything, the Postlite process may die but it shouldn't affect your database.

#. > "Hosting SQLite Databases on GitHub Pages" (2021) re: sql.js-httpvfs, DuckDB https://news.ycombinator.com/item?id=28021766

#. awesome-db-tools https://github.com/mgramin/awesome-db-tools

If you are just looking for data versioning there is Dolt:

https://github.com/dolthub/dolt

And that has a user-friendly UI in DoltHub:

https://www.dolthub.com/

You wouldn't store the images themselves in Dolt, those would likely be links to S3 but al the labels and surrounding metadata could be stored in Dolt?

DISCLAIMER: I'm the CEO of DoltHub so this is self-promotion.

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.

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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.