VLDB 2023

Iām excited to be part of seven different papers, demos, and workshop papers at VLDB in Vancouver this year!
AdaChain
Every permissioned blockchain architecture has different performance characteristics. AdaChain automatically switches between architectures to optimize performance online, compensating for changes in workload and network conditions.
- AdaChain: A Learned Adaptive Blockchain
- Chenyuan Wu
- Bhavana Mehta
- Mohammad Javad Amiri
- Ryan Marcus
- Boon Thau Loo
VLDB '23 (pdf) (doi)
AutoSteer
Bringing a learned steering optimizer to a new database can be difficult, since optimizers can have 1000s of knobs. AutoSteer automatically finds a good set, and optimizes your queries as well! We tested a large deployment of AutoSteer at Meta.
- AutoSteer: Learned Query Optimization for Any SQL Database
- Christoph Anneser
- Nesime Tatbul
- David Cohen
- Zhenggang Xu
- Prithvi Pandian
- Nikolay Leptev
- Ryan Marcus
VLDB '23 (pdf) (doi)
QO-Insight
Alongside AutoSteer, we developed a tool called QO-Insight to help DBAs understand the decisions of learned query optimizers. We will present a demo of our tool which enables side-by-side query plan analysis!
- QO-Insight: Inspecting Steered Query Optimizers Demo.
- Christoph Anneser
- Mario Petruccelli
- Nesime Tatbul
- David Cohen
- Zhenggang Xu
- Prithviraj Pandian
- Nikolay Laptev
- Ryan Marcus
- Alfons Kemper
VLDB '23 (pdf) (doi)
Robust cardinality estimation
Query-driven cardinality estimators learn powerful, workload-tailored strategies, but have a hard time dealing with data drift. We show robust techniques that can tune a learned cardinality estimator online, as data changes.
- Robust Query Driven Cardinality Estimation under Changing Workloads
- Parimarjan Negi
- Ziniu Wu
- Andreas Kipf
- Nesime Tatbul
- Ryan Marcus
- Sam Madden
- Tim Kraska
- Mohammad Alizadeh
VLDB '23 (pdf) (doi)
SageDB
The culmination of several years of work on instance-optimized system, SageDB is a prototype analytic database combining together several learned techniques at once for the first time.
(Technically, SageDB was published in VLDB ā22 proceedings, but the presentation is happening this year!)
- SageDB: An Instance-Optimized Data Analytics System
- Jialin Ding
- Ryan Marcus
- Andreas Kipf
- Vikram Nathan
- Aniruddha Nrusimha
- Kapil Vaidya
- Alexander van Renen
- Tim Kraska
VLDB '22 (pdf) (doi)
RLShard
Almost every distributed transactional database today can tolerate crashes, but not Byzantine failures. Here, we take a first look at building a distributed, sharded database that can tolerate ā and adapt to ā Byzantine adversaries.
- Towards Adaptive Fault-Tolerant Sharded Databases
- Bhavana Mehta
- Neelesh Chinnakonda Ashok Kumar
- Prashanth S Iyer
- Mohammad Javad Amiri
- Boon Thau Loo
- Ryan Marcus
AIDB@VLDB '23 (pdf)
Learned query superoptimization
Modern analytics databases frequently run the same query multiple times. Could it be worth spending a long time ā hours ā optimizing such queries? I argue that doing so might allow DBMSes to capture some of the performance of bespoke systems.
- Learned Query Superoptimization
- Ryan Marcus
AIDB@VLDB '23 (pdf)