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Bridging the Gap between Relational OLTP and Graph-based OLAP

on YouTube 20m
Talkat USENIX ATC '23 · 2023-07-10..07-12· [databases]

speakers

name:Sijie Shen
links:none on file
name:Zihang Yao
links:none on file
name:Lin Shi
links:none on file
name:Lei Wang
links:none on file
name:Longbin Lai
links:none on file
name:Qian Tao
links:none on file
name:Li Su
links:none on file
name:Rong Chen
links:none on file
name:Wenyuan Yu
links:none on file
name:Haibo Chen
x:HaiboChen
name:Binyu Zang
linkedin:binyu-zhang-54906b57
name:Jingren Zhou
links:none on file

resources

video:https://youtu.be/QVZfKw4XyB0
slides:none on file

summary

GART extends HTAP into HTGAP by adding a relational-to-graph mapping layer and an in-memory dynamic graph store that claims tens of milliseconds freshness and multi-order performance gains versus ETL or log‑replicated designs. It uses an efficient mutable CSR split into edge segments, a coarse-grained MVCC epoching for versions, and flexible property placement. On LDBC SNB GART beats LiveGraph by up to 4.4× and reports orders‑of‑magnitude improvement in freshness or throughput.

AI-written summary · about

chapters (11)

  1. 0:00Bridging OLTP and OLAP
  2. 2:28Dynamic graph processing solutions
  3. 4:36Hybrid transactional analytics
  4. 5:40Gut system design goals
  5. 7:06Transparent data conversion
  6. 9:38Dynamic graph storage challenges
  7. 11:22Storage optimization insights
  8. 12:26Redesigned graph storage
  9. 15:56Evaluation methodology
  10. 17:20Performance comparison
  11. 19:09Conclusion

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