[kwillets]

Another chapter of the slowly-reimplementing-Vertica saga.

It's becoming clear that merge trees and compaction need to be addressed next, after delete vectors brought them onstage.

Vertica will actually look up the equality keys in a relevant projection if it exists, and then use the column values in the matching rows to equality-delete from the other projections; it's fairly good at avoiding table scans.

[datadrivenangel]

Data processing tools had a pricing problem. The Big Data Revolution was google and other companies realizing that commodity hardware had gotten so good that you could throw 100x as much compute at a processing job and it would still be cheaper than Oracle, Vertica, Teradata, and SQL Server.

As an industry, we keep forgetting these things and reinventing the wheel because there is more money to be made squeezing enterprises than providing widely available sustainable software for a fair price and than losing mindshare to the next generation of tool and eventually getting sold for parts. It's a sad dynamic

[kwillets]

Vertica runs on commodity hardware, and there's no license cost for cpu, so it's very economical for large workloads. The quickest way to 10x your costs is to move a Vertica workload to Snowflake (last I heard my old job is now up to 40x). I'll have numbers on Databricks in a few months.

It's true that Vertica sales are optimized for large enterprises -- they just don't have the VC cash to hire 3000 sales people to sell it to the low end, so it doesn't appear on many people's radar.