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Database Architecture Optimized for the New Bottleneck: Memory Access

Database Architecture Optimized for the New Bottleneck: Memory Access. Peter Boncz Data Distilleries B.V. Amsterdam The Netherlands P.Boncz@ddi.nl. Stefan Manegold Martin Kersten CWI Amsterdam The Netherlands {S.Manegold,M.Kersten}@cwi.nl. Contents. How Memory Access works

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Database Architecture Optimized for the New Bottleneck: Memory Access

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  1. Database Architecture Optimized for the New Bottleneck: Memory Access Peter Boncz Data Distilleries B.V. Amsterdam The Netherlands P.Boncz@ddi.nl Stefan Manegold Martin Kersten CWI Amsterdam The Netherlands {S.Manegold,M.Kersten}@cwi.nl

  2. Contents • How Memory Access works • Simple Scan Experiment • Consequences for DBMS • Data Structures: vertical decomposition • Algorithms: tune random memory access • Partitioned Join Algorithms • Monet Experiments • Accurate Cost Models • Conclusion

  3. CPU Speed vs. Memory Speed Moore’s Law: CPU speed doubles every 3 years

  4. Memory Access in Hierarchical Systems

  5. Simple Scan Experiment

  6. Consequences for DBMS • Memory access is a bottleneck • Prevent cache & TLB misses • Cache lines must be used fully • DBMS must optimize • Data structures • Algorithms (focus: join)

  7. Vertical Decomposition in Monet

  8. Partitioned Joins • Cluster both input relations • Create clusters that fit in memory cache • Join matching clusters • Two algorithms: • Partitioned hash-join • Radix-Join (partitioned nested-loop)

  9. Partitioned Joins: Straightforward Clustering • Problem: Number of clusters exceeds number of • TLB entries ==> TLB trashing • Cache lines ==> cache trashing • Solution: Multi-pass radix-cluster

  10. Partitioned Joins: Multi-Pass Radix-Cluster • Multiple clustering passes • Limit number of clusters per pass • Avoid cache/TLB trashing • Trade memory cost for CPU cost • Any data type (hashing)

  11. Monet Experiments: Setup • Platform: • SGI Origin2000 (MIPS R10000, 250 MHz) • System: • Monet DBMS • Data sets: • Integer join columns • Join hit-rate of 1 • Cardinalities: 15,625 - 64,000,000 • Hardware event counters • to analyze cache & TLB misses

  12. Monet Experiments: Radix-Cluster (64,000,000 tuples)

  13. Accurate Cost Modeling: Radix-Cluster

  14. Monet Experiments: Partitioned Hash-Join

  15. Monet Experiments: Radix-Join

  16. Monet Experiments: Overall Performance (64,000,000 tuples)

  17. Conclusion • Problem: • Memory access is increasingly the most important bottleneck for database performance • Solutions: • Vertical decomposition improves column-wise data access • Radix-algorithms optimize join performance • General: • Algorithms can be tuned to achieve optimal memory access • Detailed and accurate estimation of memory cost is possible Monet homepage: www.cwi.nl/~monet

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