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Adaptively Parallelizing Distributed Range Queries

This paper explores the challenges of executing range queries in distributed databases at web-scale, where low latency and high throughput are crucial. We demonstrate how adaptively parallelizing queries can enhance performance by optimizing the balance between server resources and client demands. Utilizing the PNUTS architecture, we investigate factors influencing parallelism benefits, such as range size, query selectivity, and client speed, while showcasing an experimental testbed that illustrates adaptive server allocation strategies to maximize query efficiency.

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Adaptively Parallelizing Distributed Range Queries

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  1. AdaptivelyParallelizingDistributed Range Queries Ymir Vigfusson Adam Silberstein Brian Cooper Rodrigo Fonseca

  2. Introduction • Wellunderstooddatabasetasks are challengingagainat web-scale • Millions of users • Terabytes of data • Lowlatency essential • Shared-nothingdatabases • Scale by adding servers with local disks

  3. Introduction • PNUTS • Workload: • Analogous to OLTP • Randomreads and updates of individualrecords • Online applications

  4. Range Queries • Important for web workloads • Time ordered data New items

  5. Range Queries • Important for web workloads • Time ordered data • Secondary indexes Index Base Table

  6. Range Queries • Important for web workloads • Time ordered data • Secondary indexes • etc. • Could scan large fraction of the table • Short time to first resultis crucial • Possibly a large number of results • Predicatemightbeselective FIND ITEMS UNDER $100 WHERE Category =‘Cars’ AND Quantity>0 FIND ITEMS OVER $100

  7. Executing Range Queries • Makessense to parallelizequeries • Tables are partitioned over many servers • How parallelshould a querybe? • Too sequential: Slow rate of results • Too parallel: Client overloaded, wasting server resources

  8. Executing Range Queries • Makessense to parallelizequeries • Tables are partitionedover many servers • How parallelshould a querybe? • Too sequential: Slow rate of results • Too parallel: Client overloaded, wasting server resources

  9. PNUTS 101 get set delete Clients Partition map Partition Controller Load balancer Routers Server monitor Storage Units

  10. PNUTS + Range scans get set delete getrange Clients = query Routers SCAN = idealparallelismlevel = idealserver concurrency Storage Units

  11. Range queryparameters • Equalsnumber of disks (experimentally) • In ourtestbed, • Whendoesparallelism help? • range size: longer = more benefit • queryselectivity: fewerresults = more benefit • client speed:faster = more benefit • Diminishingreturns = idealparallelismlevel = idealserver concurrency

  12. Determining • As shown, manyinfluentialfactors • End-to-end insight: match rate of new results to client’suptake rate % Client Capacity

  13. Determining • As shown, manyinfluentialfactors • End-to-end insight: match rate of new results to client’suptake rate • Hard to measure client rate directly % Client Capacity

  14. Adaptive server allocation (ASA) • Measure client uptake speed • If higher, increase K by 1 and repeat Optimal Client saturated, enter next phase Periodically, flip a coin K= 1 Time • On Heads • Increase K by 1 • Improvement? • Keep increasing K • No? • Revert to old K • On Tails • Decrease K by 1 • Performance same? • Keep decreasing K • No? • Revert to oldK

  15. Adaptive server allocation (ASA) • Measure client uptake speed • If higher, increase K by 1 and repeat Real ExperimentalTestbed Client saturated, enter next phase Clients x 4 K= 1 Time 20M records 1024 partitions 100MB/partition 100 partitions/server • Periodically, flip a biased coin w.p. 2/3 • On heads, increase or decrease K by 1 • If no improvement, revert to old K • Otherwise, keep K and go in the same direction • next time we change it. Routers x 1 Storage Units x 10

  16. Adaptive server allocation (ASA) x 1

  17. Adaptive server allocation (ASA) x 4

  18. Adaptive server allocation (ASA) x 4

  19. Adaptive server allocation (ASA) x2 K=5 K=5 K=2 K=8

  20. Single Client Clients Routers SCAN Storage Units

  21. Multiple Clients Clients Routers SCAN SCAN Storage Units

  22. Multiple Clients Clients Scheduler Routers SCAN SCAN Storage Units

  23. Scheduler Service • Scan engine shares query information with central scheduler • Servers which hold needed partitions • Parallelism level • Scheduler decides on who-should-go-where-when to minimize contention • How do we schedule multiple queries?

  24. Scheduler Service • Scan engine shares query information with central scheduler • Servers which hold needed partitions • Parallelism level • Scheduler decides on who-should-go-where-when to minimize contention • How do we schedule multiple queries?

  25. SchedulingWishlist • All queriesshould finish quickly • Givepreference to higherpriorityqueries • Prefersteady rate of results over bursts

  26. SchedulingWishlist • All queriesshould finish quickly • Givepreference to higherpriorityqueries • Prefersteady rate of results over bursts = Minimize makespan Thm:The makespan of any greedy heuristic is at most 2∙OPT

  27. SchedulingHeuristic • All queriesshould finish quickly • Givepreference to higherpriorityqueries • Prefersteady rate of results over bursts Current idle period Priority preference parameter Greedilyschedule queries in order by

  28. Scheduler - Priority Priority=1 Priority=2 Priority=4

  29. Scheduler – Resultstream Priority=1 Priority=4

  30. Scheduler • All queriesshould finish quickly • Givepreference to higherpriorityqueries • Prefersteady rate of results over bursts

  31. Conclusion Supporting range queries at web-scale is challenging • Too little: client suffers, toomuch: servers suffer • Adaptive server allocationalgorithm • Greedy algorithm run by central scheduler • Provably short makespan • Respects priorities and provides a steady stream of results Our solution is entering production soon. How parallelshould a querybe? How do we schedule multiple queries?

  32. Adaptive server allocation (ASA) x 1 K=2 K=3 K=5 K=8

  33. SchedulingWishlist • All queriesshould finish quickly • Givepreference to higherpriorityqueries • Prefersteady rate of results over bursts = Minimize makespan Thm:The makespan of any greedy heuristic is at most 2∙OPT Thm:OPT is exactly

  34. SchedulingWishlist • All queriesshould finish quickly • Givepreference to higherpriorityqueries • Prefersteady rate of results over bursts Priority bias parameter Greater penalty for long idle time E.g., gold/silver/bronze customers Or minimum completion time = OPT Set of idle time periods

  35. Impact of parallelism • Range size: Longer queries = more benefitfromparallelism. • Diminishingreturns • QuerySelectivity:Fewerresults = more benefitfromparallelism • Client Rate: Faster clients = more benefitfromparallelism • Saturated clients do not benefitfrom extra servers

  36. Scheduler - Priority Query lengths 1,2,4,8% Max. completion time Avg. completion time Time of first result Current idle period Greedilyschedule queries in order by

  37. Scheduler - Priority Query lengths 1,2,4,8% Different priorities Greedilyschedule queries in order by

  38. Road map • System and problemoverview • Adaptive server allocation • Multi-queryscheduling • Conclusion

  39. Experimental test bed Clients Clients x 4 20M records 1024 partitions 100MB/partition 100 partitions/server Routers x 1 Storage Units x 10

  40. Adaptive server allocation (ASA) x 4

  41. PNUTS 101 Clients Partition map Tablet Controller Load balancer Routers Server monitor Storage Units

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