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Relational Cloud: A Database-as-a-Service for the Cloud

Relational Cloud: A Database-as-a-Service for the Cloud. Carlo Curino , Evan Jones, Raluca Ada Popa , Nirmesh Malaviya , Eugene Wu, Sam Madden, Hari Balakrishnan , Nickolai Zeldovich. Presented by Arka Bhattacharya (for CS 294,Berkeley) (some slides are taken from the CIDR ‘11 talk).

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Relational Cloud: A Database-as-a-Service for the Cloud

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  1. Relational Cloud: A Database-as-a-Service for the Cloud Carlo Curino, Evan Jones, Raluca Ada Popa, NirmeshMalaviya, Eugene Wu, Sam Madden, HariBalakrishnan, NickolaiZeldovich Presented by Arka Bhattacharya(for CS 294,Berkeley) (some slides are taken from the CIDR ‘11 talk)

  2. THE STARTUP STORY

  3. Motivation • Why move to the cloud ? • Economies of scale (hw & licensing costs) • Pay per use & lower administrative costs • Present players : • Amazon RDS (MySQL on EC2) • Microsoft SQL Azure

  4. Problems ! • Problems arising : • Efficient Multi-tenancy (Provider) • Elastic scalability (Provider) • Privacy (User) • Note : Relational Cloud is mainly for OLTP workloads & DAS architectures , consistency guarantees

  5. 1. Efficient Multi-tenancy – Placement & Migrations • Problem : Consolidate databases into smallest number of servers, balancing load and without affecting performance • Solution : Kairos , SIGMOD ’11 • Upto 17:1 consolidation • Key insight : Single database server per machine + logical databases ; (as opposed to DB in VM , or multiple DB servers per machine ) • Reduces redundant work, group commits, lower RAM wastage, code sharing, cheaper context switches

  6. Kairos ….cntd • Measure RAM,CPU & Disk usage of a database, and estimate combined load • RAM : Probe table to gauge working set size ; additive • Disk : Deduce model by testing DBMS with different write rates & working set size & measuring amount of IO • CPU : additive • Frame optimization problem (non-linear programming) • Solving takes time • After lots of heuristics, optimization solutions terminate in 8 minutes for 20 servers & 100 workloads !

  7. 2. Elastic ScalabilityDatabase Partitioning • Problem : Partition an OLTP database into N chunks so as to maximize performance • Solution : Schism , VLDB 2010 • Close to optimal • Key insight : Minimize number of distributed transactions • Advantage over Hashing, round-robin • Use workload trace to find good partitions

  8. Schism …cntd

  9. Schism …. cntd • Use a classifier to capture partitioning in compact form , for efficient query routing • Lots of heuristics to choose good workload sample • Sampling, blanket state filtering, etc • Graph Partitioning in fast ( < 40 sec ) • Achieves almost linear scalability !

  10. 3. Privacy • Problem : • Prevent DBA from snooping on data • ensure data security during application and DBMS server compromise • Solution : CryptDB , SOSP 2011 • Low overhead ~ 22.5% • Key insight : Adjustable security

  11. CrpytDB …Onions RND DET : equality selection RND DET : equality join OPE : inequality select OPE-inequality join HOM Any value Any value int value Onion 1 Onion 2 Onion 3

  12. Overall architecture DB stats Partitions & placements

  13. Relational Cloud • Advantages : • Unmodified DB backends • Workload-aware consolidation • Workload-aware sharding • High availability via replication of front-end servers • SQL over encrypted data

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