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Lifetime-Based Memory Management for Distributed Data Processing Systems

Summary: Lifetime-aware allocation derives object lifetimes from UDFs and types, grouping similarly lived data into byte arrays for bulk reclamation. Deca integrates this transparently into Spark, sharply reducing GC, memory use, spilling, and execution time. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11568
Venue
VLDB
Year
2016
Pagerank
6.2052672e-05
Overall Rank
5,476 | 62.44%
DOI
10.14778/2994509.2994522

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lu_vldb16,
        title = {{Lifetime-Based Memory Management for Distributed Data Processing Systems}},
        author = {Lu, Lu and Shi, Xuanhua and Zhou, Yongluan and Zhang, Xiong and Jin, Hai and Pei, Cheng and He, Ligang and Geng, Yuanzhen},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {12},
        pages = {936--947},
        doi = {10.14778/2994509.2994522},
        url = {https://doi.org/10.14778/2994509.2994522},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
24 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00054865648
2,265 A Platform for Scalable One-Pass Analytics using MapReduce 2011 SIGMOD 8.8398946e-05
3,719 M3R: Increased Performance for In-Memory Hadoop Jobs 2012 VLDB 7.1740814e-05
4,208 Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics 2015 VLDB 6.8319812e-05
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