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Reactive Supply to 
Changing Demand 
Building Elastic Reactive Systems 
Jonas Bonér 
Typesafe 
CTO & co-founder 
@jboner
Outline 
1. Introduction 
2. Scale Up 
3. Scale Out 
4. Bring It Together 
2
Outline 
1. Introduction 
2. Scale Up 
3. Scale Out 
4. Bring It Together 
3
The rules of the game 
have changed
5 
Yesterday Today
5 
Yesterday Today 
Single machines
5 
Yesterday Today 
Single machines Clusters of machines
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk 
Slow networks
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk 
Slow networks Fast networks
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk 
Slow networks Fast networks 
Few concurrent users
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk 
Slow networks Fast networks 
Few concurrent users Lots of concurrent users
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk 
Slow networks Fast networks 
Few concurrent users Lots of concurrent users 
Small data sets
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk 
Slow networks Fast networks 
Few concurrent users Lots of concurrent users 
Small data sets Large data sets
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk 
Slow networks Fast networks 
Few concurrent users Lots of concurrent users 
Small data sets Large data sets 
Latency in seconds
5 
Yesterday Today 
Single machines Clusters of machines 
Single core processors Multicore processors 
Expensive RAM Cheap RAM 
Expensive disk Cheap disk 
Slow networks Fast networks 
Few concurrent users Lots of concurrent users 
Small data sets Large data sets 
Latency in seconds Latency in milliseconds
Cost Gravity is at Work 
7
Cost Gravity is at Work 
7
The Principles of Reactive Systems
The Principles of Reactive Systems
Elastic 
“Capable of ready change or easy expansion or contraction” 
- Merriam Webster
Why do we need to 
Scale on Demand?
what is 
scalability?
12 
“Capable of being easily expanded or upgraded on demand” 
- Merriam Webster Dictionary
13 
“The house in which Amdahl wakes up very early each day and 
rules with an iron fist.” 
- Martin Thompson (originally Gil Tene)
13 
“The house in which Amdahl wakes up very early each day and 
rules with an iron fist.” 
- Martin Thompson (originally Gil Tene)
14 
“A service is said to be scalable if when we increase the 
resources in a system, it results in increased performance 
in a manner proportional to resources added.” 
- Werner Vogels
Scalability 
vs 
Performance
Outline 
1. Introduction 
2. Scale Up 
3. Scale Out 
4. Bring It Together 
16
UP Scale
UP Scale 
and down
18 
Modern CPU architecture
18 
Modern CPU architecture
18 
Modern CPU architecture
18 
Modern CPU architecture
The CPU is gambling—taking bets 
19
Maximize Locality of Reference
Minimize 
Contention
Common points of 
22 
contention 
Physical Application
23 
Never
23 
Neevveerr
Block 
23 
Neevveerr
24 
GO
Async 
24 
GO
25 
NOTHING share
Divide & Conquer 
26
27 
Single Writer Principle
27 
Single Writer Principle 
Producers IO device 
SERIAL & 
CONTENDED
27 
Single Writer Principle 
Producers IO device 
SERIAL & 
CONTENDED 
Producers Actor or Queue IO device 
BATCHED & 
UNCONTENDED
28
29 
Needs to be async and non-blocking 
all the way down
29 
Needs to be async and non-blocking 
all the way down
The Role of Immutable State 
30
The Role of Immutable State 
30
The Role of Immutable State 
• Great to represent facts 
• Messages and Events 
• Database snapshots 
• Representing the succession of time 
30
The Role of Immutable State 
• Great to represent facts 
• Messages and Events 
• Database snapshots 
• Representing the succession of time 
• Mutable State is ok if local and contained 
• Allows Single-threaded processing 
• Allows single writer principle 
• Feels more natural 
• Publish the results to the world as Immutable State 
30
on Demand Scale
Outline 
1. Introduction 
2. Scale Up 
3. Scale Out 
4. Bring It Together 
32
Scale 
OUT
Scale 
OUT 
and in
• Mobile 
• Cloud Services, REST etc. 
• NOSQL DBs 
• Big Data 
• etc 
34 
Distributed Computing is the new normal
What is the essence of 
distributed computing? 
35 
To try to overcome that
What is the essence of 
distributed computing? 
1. Information travels at the speed of light 
2. Independent things fail independently 
35 
To try to overcome that
36
Node Node Node 
36 
Node
Middleware 
Node Node Node 
36 
Node
Cluster/Rack/Datacenter 
Cluster/Rack/Datacenter 
Cluster/Rack/Datacenter 
Middleware 
Node Node Node 
36 
Node
1. The network is reliable 
2. Latency is zero 
3. Bandwidth is infinite 
4. The network is secure 
5. Topology doesn't change 
6. There is one administrator 
7. Transport cost is zero 
8. The network is homogeneous 
37 
Peter Deutsch’s 
8 Fallacies of 
Distributed 
Computing
The Graveyard of Distributed Systems 
• Distributed Shared Mutable State 
• ⇒ EVIL (where N is number of nodes) 
• Serializable Distributed Transactions 
• Synchronous RPC 
• Guaranteed Delivery 
• Distributed Objects 
• “Sucks like an inverted hurricane” - Martin Fowler 
38 
N
Maximize Locality of Reference
Strong 
Consistency
41 
Linearizability 
“Under linearizable consistency, all operations appear to have 
executed atomically in an order that is consistent with the 
global real-time ordering of operations.” 
- Herlihy & Wing 1991
Strong Consistency Protocols
Minimize 
Contention 
(Coordination in the Cluster)
44 
CAP Theorem
44 
CAP Theorem
Eventual 
Consistency
46 
CRDT CvRDTs/CmRDTs
Life beyond Distributed Transactions: 
47 
an Apostate’s Opinion 
“In general, application developers simply do not 
implement large scalable applications assuming 
distributed transactions.” 
-­‐ 
Pat Helland
48 
Domain Events 
“State transitions are an important part of our problem 
space and should be modeled within our domain.” 
- Greg Young
49 
The Event Log 
"The database is a cache of a subset of the log.” 
- Pat Helland
The Event Log 
• Append-Only Logging 
• Database of Facts 
• Two models: 
• One single Event Log 
• Strong Consistency 
• Multiple sharded Event Logs 
• Strong + Eventual Consistency 
50
Outline 
1. Introduction 
2. Scale Up 
3. Scale Out 
4. Bring It Together 
51
NOTHING 
52 
share
TRANSPARENCY 
53 
location
54
54
55
CPU 
L1/CPU 
L2 
Cache 
Core 
CPU 
CPU 
L1/CPU 
L2 
Cache 
Core 
Socket 
ThreTahreadd CPUCPU CPU 
MachMiacnhinee NodNeode ClustCleusterr JVMJVM Data 
Center 
55 
Data 
Socket 
Center
56 
Scaling Up and Out is essentially 
the same thing
56 
Scaling Up and Out is essentially 
the same thing
Elasticity requires a 
Message-Driven 
Architecture
Questions?
Reactive Supply to 
Changing Demand 
Building Elastic Reactive Systems 
Jonas Bonér 
Typesafe 
CTO & co-founder 
@jboner

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