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

CWIAmsterdam

The Netherlands

{S.Manegold,M.Kersten}@cwi.nl

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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

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CPU Speed vs. Memory Speed

Moore’s Law:CPU speed doublesevery 3 years

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Memory Access in Hierarchical Systems

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Simple Scan Experiment

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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)

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Vertical Decomposition in Monet

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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)

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Partitioned Joins: Straightforward Clustering

• Problem:Number of clusters exceeds number of– TLB entries ==> TLB trashing– Cache lines ==> cache trashing

• Solution:Multi-pass radix-cluster

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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)

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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

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

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Accurate Cost Modeling: Radix-Cluster

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Monet Experiments: Partitioned Hash-Join

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Monet Experiments: Radix-Join

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Monet Experiments: Overall Performance

(64,000,000 tuples)

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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