Selective Late Materialization in Modern Analytical Databases
Summary: Classic late-materialization loses value in vectorized columnar engines; this paper redefines LM and proposes Selective Late Materialization (SLM), choosing per-attribute, per-query materialization points to minimize cost. Implemented in DuckDB, SLM outperforms early/late materialization on JOB by 14.7%/8.9% average (up to 76.7% per query) and shows similar TPC‑DS gains. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yihao Liu
- 2. Shaoxuan Tang
- 3. Yulong Hui
- 4. Hangrui Zhou
- 5. Huanchen Zhang
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