Database pushdown is a widely adopted technique under compute-storage disaggregation. The rising network and I/O speeds, coupled with stagnated compute and memory subsystems of a disaggregated storage architecture in the past decade, render state-of-the-art policy-driven pushdown designs ineffective. This is because the query performance bottleneck has shifted from network and I/O to compute, where computing power at the storage layer becomes scarce. This paper rethinks pushdown database design via a systematic characterization and identifies three root causes, i.e., table structure agnostic, lower interference tolerance, and lack of operator scheduling. Based on the gathered insights, we build TapDB, a new pushdown database that targets emerging storage disaggregation. Driven by two key ideas (i.e., lazy evaluation and trading network and I/O for compute), TapDB introduces four new mechanisms: a table-aware operator cost estimator based on in-situ meta-learning and cardinality estimation, an admission control scheme to limit execution concurrency, a ballooning-based DRAM-SSD hybrid table, and a critical path-driven operator scheduler. Our prototype shows 1.3-2.3× speedups compared with prior solutions when running SSB and TPCH benchmarks.

@inproceedings{ZhangLBL26,
  title = {Understanding and Optimizing Database Pushdown on Disaggregated Storage},
  author = {Hua Zhang and Xiao Li and Yuebin Bai and Ming Liu},
  booktitle = {ASPLOS},
  year = {2026},
  doi = {10.1145/3779212.3790243}
}