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Ryan Marcus
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@RyanMarcus

Ryan Marcus

@RyanMarcus
Assistant prof @CIS_Penn. Machine learning for systems, databases. Also discuss.systems/@ryanmarcus
Philadelphia, PA
ryanmarc.us
Joined March 2009
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  • @RyanMarcus
    Ryan Marcus
    @RyanMarcus
    Aug 31
    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.
    2
  • @RyanMarcus
    Ryan Marcus
    @RyanMarcus
    Aug 28
    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. ⬇️
    2
  • @RyanMarcus
    Ryan Marcus
    @RyanMarcus
    Dec 26, 2025
    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.
    A four-step cycle diagram showing feedback between database users and engineers.
① User submits their workload to the system: A square grid of colored squares represents the workload (3 green, 2 purple).
② Engineers observe properties of the workload: A database cylinder leads to a chart showing workload composition: purple 30%, green 60%. A speech bubble from an engineer character says, “Most queries are green. I’ll trade lower purple performance for higher green performance.”
③ Engineers identify hotspots and optimize their system.
④ Users optimize their workloads based on their platform: A speech bubble from a user character says, “Our platform is good at green, but bad at purple — send more green!” An arrow shows users adjusting the workload and feeding it back into the system.

Overall, the diagram illustrates a feedback loop where system optimizations influence user behavior, which then shapes the workload engineers observe.
    1
  • @RyanMarcus
    Ryan Marcus
    @RyanMarcus
    Jun 3, 2025
    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! 🧵
    1
  • @RyanMarcus
    Ryan Marcus
    @RyanMarcus
    Feb 15, 2025
    Pair(akeet) programming.
    00:00