cockroach VS Trino

You absolutely can write very high performance software in Go, that's kind of the point. You can efficiently interface with C libraries. You can create the sort of software everyone says should be done in Rust, like databases and web servers and system orchestration and games and every other goddamn thing that people will say isn't the right choice for Go.

PostgreSQL Parser separated from CockroachDB, a distributed DB.

Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Here’s my analytical pipeline for Github stars data using Nifi, Kafka and Druid.

One of the most popular distributed DB is built in Go: https://www.cockroachlabs.com/

Recently, I worked on such an integration with Splunk. The Splunk dashboard files that emulate the DB Console are now available in our repo for everyone's benefit.

Technically not postgres, but could be worth checking out cockroach db, it can use any postgres driver from programming languages and is build for distribution. I recently moved from postgres to cockroach (with golang postgres driver) and didnt need to change a single query

Trino or Hive for SQL querying. Get Trino/Hive to talk to Nessie.

One place to look are the projects repo's and docs, once you have a good idea of how the system is architected poking around pieces of the codebase can be helpful in letting you really understand their internals. I personally enjoy going through spark repo and trino repo and the documentation for both projects is decent and can answer many of your questions.

However, once your data reaches a certain size or you reach the limits of vertical scaling, it may be necessary to distribute your queries across a cluster, or scale horizontally. This is where distributed query engines like Trino and Spark come in. Distributed query engines make use of a coordinator to plan the query and multiple worker nodes to execute them in parallel.

So, there are a lot of people involved which is why you try to find some common ground regarding coding style, documentation, programming language version, … Most larger project have some guidelines you can read to see what is necessary for a PR to be approved: Trino’s Guidelines or dbt.

Trino, a distributed SQL engine (https://trino.io)

Storing and Querying Analytical Data With Trino

For analytics, you can access Aerospike data on Trino with the Aerospike Trino Connector.