Ryan Marcus

Query optimization benchmarks too easy? Gremlin generates simple select-project-join queries that are up to 800x slower on DuckDB and PostgreSQL than a known plan.

Gremlin simultaneously searches for both an adversarial input and matching fast plan to reveal perf bugs in QOs.

Our group is part of two @VLDBconf papers on query opt. and execution:

1) Ultron, a system for building history-based optimization rules, deployed at @databricks

2) Global hash tables strike back!(?), an experimental analysis of fully concurrent group by aggregation. ⬇️

Most database teams optimize what they see in workload logs. But those very optimizations change what users choose to run!

In our CIDR paper, we argue that industrial workloads exhibit 𝐬𝐮𝐫𝐯𝐢𝐯𝐨𝐫𝐬𝐡𝐢𝐩 𝐛𝐢𝐚𝐬: logs reflect a negotiation between users and the platform.

OLAP workloads are dominated by repetitive queries -- how can we optimize them?

A promising direction is to do 𝗼𝗳𝗳𝗹𝗶𝗻𝗲 query optimization, allowing for a much more thorough plan search.

Two new SIGMOD papers! 🧵

Pair(akeet) programming.