Accordion: Elastic Scalability for Database Systems

Supporting Distributed Transactions

!Marco Serafini

Qatar Computing Research Institute !

joint work with: Essam Mansour, Ashraf Aboulnaga Qatar Computing Research Institute Kenneth Salem University of WaterlooTaha Rafiq Amazon.com Umar Farooq Minhas IBM Research Almaden

Leveraging the Cloud

P1P2P3

P4P5P6

P7P8

Applications cannot always leverage the cloud

Make partitioned DBMSes scale out and in!

Cloud layer

DBMS layer

Leveraging the Cloud

P1P2

P3P4P5 P6

P7P8

Applications cannot always leverage the cloud

Make partitioned DBMSes scale out and in!

Cloud layer

DBMS layer

Online Solution

Online = Handle workload with unanticipated skews

Partitions suddenly become hot

Overall database load grows/shrinks

Skews change over time

No prior knowledge, no workload trace analysis

Accordion

Goal: run a partitioned DBMS on a variable set of servers

DBMS supports ACID distributed transactions

Necessary in many OLTP workloads

Major performance bottleneck

Problems we address: Where & When to move data

New!

Accordion Architecture

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The dangers of scaling out

with distributed transactions

Before scale out: After:

Full DB

1/2 DB

1/2 DB

Does scaling out increase throughput?

Scaling Out: How Effective is it?

0 5000

10000 15000 20000 25000 30000 35000

8 16 32 64

Serv

er c

apac

ity (t

ps)

Overall number of partitions

16 partitions per server8 partitions per server

No Distributed Transactions

YCSB Max throughput

per server constant

Overall throughput

grows linearlyN nodes

2*N nodesBars: Smaller DB <-

Larger DB ->

Distributed Transactions

0 5000

10000 15000 20000 25000 30000

8 16 32 64

Serv

er c

apac

ity (t

ps)

Overall number of partitions

16 partitions per server8 partitions per server

TPC-C Max throughput

per server decreases

Overall throughput

can still increase N nodes

2*N nodesBars: Smaller DB <-

Larger DB ->

Bin Packing Partition Placement

Bin Server

Volume of Bin Maximum throughput of server

Item Database partition

Volume of Item Transaction rate of partition

Distributed Transactions = Circular Dependency

Bin Volume - not constant!

Packing

Constraints Determines

Two Problems

1. Model for server capacity (maximum throughput)

Capacities varies based on placement

Learn model online

2. Partition placement using this model

1: Server Capacity Models

Affinity: Likelihood of co-access among partitions

!

Max throughput capacity per server depends on affinity

Null affinity (YCSB) -> Constant capacity

Uniform affinity (TPC-C) -> f (# local partitions)

Arbitrary affinity -> Must model affinity explicitly

Aff(P1,P2) = Prob (P2 accessed | P1 accessed)

2: Partition Placement

Minimize data migration

s.t. servers are not overloaded

Server capacity function Can be nonlinear

Accordion’s planner uses linear programming

Evaluation

Setup

Target DBMS: H-Store

Horizontally partitioned

Single-partition transactions execute sequentially

Distributed transactions use 2PC

Three benchmarks

YCSB (null affinity)

TPC-C (uniform affinity)

Clustered TPC-C (arbitrary affinity)

Cost Reduction (TPC-C)

Arbitrary Affinity up to 9x cost reduction

Uniform Affinity up to 1.7x cost reduction

0 5

10 15 20 25 30

64 256 1024

Num

ber o

f ser

vers

use

d

Number of partitions

AccordionKairos-SP

GreedyStatic

0 5

10 15 20 25 30 35 40

64 256 1024

Num

ber o

f ser

vers

use

dNumber of partitions

AccordionKairos-SP

GreedyStatic

Impact on Migration (TPC-C)

Shorter reconfiguration time, fewer servers

0 5000

10000 15000 20000 25000 30000 35000 40000

0 10 20 30 40 50

Tran

sact

ions

per

sec

Time (min)

AccordionKairos-SP

Cold

Thank you! !

Marco Serafini mserafini@qf.org.qa