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Transcript of Predicting Replicated Database Scalability Sameh Elnikety, Microsoft Research Steven Dropsho, Google...
Predicting Replicated Database Scalability
Sameh Elnikety, Microsoft Research Steven Dropsho, Google Inc.Emmanuel Cecchet, Univ. of Mass.Willy Zwaenepoel, EPFL
• Environment– E-commerce website– DB throughput is 500 tps
• Is 5000 tps achievable?– Yes: use 10 replicas– Yes: use 16 replicas – No: faster machines needed
• How tx workload scales on replicated db?
Motivation
SingleDBMS
2
Read Tx
Replica 2
Replica 1
Replica 3
Load Balancer
T
5
Read tx does not change DB state
Read tx does not change DB state
Update Tx
Replica 2
Replica 1
Replica 3
CertLoad
Balancer
TTwsws wswswswswsws
6
Update tx changesDB state
Update tx changesDB state
Additional Replica
Replica 2
Replica 1
Replica 3
Load Balancer T wsws
Replica 3
7
Replica 4
Cert
wswswsws
• Standalone DBMS– Service demands
• Multi-master system– Service demands– Queuing model
• Experimental validation
Coming Up …
8
• Required– readonly tx: R – update tx: W
• Transaction load– readonly tx: R
– update tx: W / (1 - A1)
Standalone DBMS
SingleDBMS
Abort probability is A1 Submit W / (1 - A1) update tx
Commited tx: WAborted tx: W ∙ A1 / (1- A1)
Abort probability is A1 Submit W / (1 - A1) update tx
Commited tx: WAborted tx: W ∙ A1 / (1- A1) 9
Standalone DBMS
SingleDBMS
1
(1)(1 )
WLoad R rc wc
A
10
• Required– readonly tx: R – update tx: W
• Transaction load– readonly tx: R
– update tx: W / (1 - A1)
• Required (whole system of N replicas)– Readonly tx: N ∙ R – Update tx: N ∙ W
• Transaction load per replica– Readonly tx: R
– Update tx: W / (1 - AN)
– Writeset: W ∙ (N - 1)
Multi-Master with N Replicas
( 1)(1 )
( )N
MM
WR rc wc W N ws
ALoad N
12
MM Service Demand
( 1)(1 )
( )N
MM
WR rc wc W N ws
ALoad N
( )(1 )
1)N
MM
PwN Pr rc wc Pw ws
AD N
13Explosive cost!
Compare: Standalone vs MM
( )(1 )
1)N
MM
PwN Pr rc wc Pw ws
AD N
Explosive cost!
1
(1)(1 )
PwD Pr rc wc
A
14
• Standalone:
• Multi-Master:
Readonly Workload
( )(1 )
1)N
MM
PwN Pr rc wc Pw ws
AD N
Explosive cost!
1
(1)(1 )
PwD Pr rc wc
A
15
• Standalone:
• Multi-Master:
Update Workload
( )(1 )
1)N
MM
PwN Pr rc wc Pw ws
AD N
Explosive cost!
1
(1)(1 )
PwD Pr rc wc
A
16
• Standalone:
• Multi-Master:
Closed-Loop Queuing Model
Replica i
LB
LB
LB
...
CPU
Disk
TT
TT
TT
Cert
Cert
Cert
Think time
Load balancer
& network
delay
Certifier delay
Pw..
.
...
N replicas
17
• Standard algorithm
• Iterates over the number of clients
• Inputs:– Number of clients– Service demand at service centers– Delay time at delay centers
• Outputs:– Response time– Throughput
Mean Value Analysis (MVA)
18
Using the Model
Replica i
LB
LB
LB
...
CPU
Disk
TT
TT
TT
Cert
Cert
Cert
Think time
Load balancer
& network
delay
Certifier delay
Pw..
.
...
N replicas
19
• Copy of database
• Log all txs, (Pr : Pw)
• Python script replays txs– Readonly (rc)– Updates (wc)
• Writesets– Instrument db with triggers– Play txs to log writesets– Play writesets (ws)
Standalone Profiling (Offline)
20
Using the Model
Replica i
LB
LB
LB
...
CPU
Disk
TT
TT
TT
Cert
Cert
Cert
Think time
Load balancer
& network
delay
Certifier delay
Pw..
.
...
N replicas
# clients, think time
1.5 ∙ fsync()
1 ms
23
• Compare– Measured performance vs model predictions
• Environment– Linux cluster running PostgreSQL
• TPC-W workload– Browsing (5% update txs)– Shopping (20% update txs)– Ordering (50% update txs)
• RUBiS workload– Browsing (0% update txs)– Bidding (20% update txs)
Experimental Validation
24
• Database system– Snapshot isolation– No hotspots– Low abort rates
• Server system– Scalable server (no thrashing)
• Queuing model & MVA– Exponential distribution for service demands
Model Assumptions
29
• Models– Single-Master– Multi-Master
• Experimental results– TPC-W– RUBiS
• Sensitivity analysis– Abort rates– Certifier delay
Checkout the Paper
30
Urgaonkar, Pacifici, Shenoy, Spreitzer, Tantawi.
“An analytical model for multi-tier internet services and its applications.” Sigmetrics 2005.
Related Work
31
• Derived an analytical model– Predicts workload scalability
• Implemented replicated systems– Multi-master– Single-master
• Experimental validation– TPC-W– RUBiS– Throughput predictions match within 15%
Conclusions
32