1.
Reactive Application Design
For High-Volume Multi-
dimensional Temporal Data
Series
@pidster

2.
InfoQ.com: News & Community Site
• 750,000 unique visitors/month
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Portuguese)
• Post content from our QCon conferences
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Watch the video with slide
synchronization on InfoQ.com!
http://www.infoq.com/presentations
/reactive-app-design-spring

3.
Presented at QCon London
www.qconlondon.com
Purpose of QCon
- to empower software development by facilitating the spread of
knowledge and innovation
Strategy
- practitioner-driven conference designed for YOU: influencers of
change and innovation in your teams
- speakers and topics driving the evolution and innovation
- connecting and catalyzing the influencers and innovators
Highlights
- attended by more than 12,000 delegates since 2007
- held in 9 cities worldwide

4.
Stuart Williams == ‘Pid’
• Lead Engineer, RTI
• SpringSource / VMware / Pivotal
• A little OSS: Apache, Eclipse
@pidster

5.
Mumbling Isn’t a Sign of
Laziness — It’s a Clever
Data-Compression Trick
Source: http://nautil.us/blog/mumbling-isnt-a-sign-of-lazinessits-a-clever-
data_compression-trick

6.
Practical
• We built a real-time* application (RTI)
– Using Spring (yes! I know!)
– Share some lessons
– Do some demos
– Reveal a secret go-faster switch!
* for some definition of real-time

7.
Built with…
• Spring IO Platform
– Boot, Data, Integration, Reactor, AMQP,
SpEL, Shell (and a little Groovy)
• GemFire, RabbitMQ
• C24

8.
Questions…
• Do you know your system load & input rates?
– No.
– Yes!
• Up to 1k/s?
• Up to 5k/s?
• Up to 10k/s?
• Up to 100k/s?
• Up to 1M/s?
• Above 1M/s?

9.
Questions…
• Heard of Spring Integration?
– Tried it?
– Used it in production?
• Heard of Spring Reactor?
– Tried it?
– Used it in production?

10.
DESIGN
Some discussion about…

11.
Design Goals & Challenges
• High throughput
– versus
• Low latency
• Enable low-impact analysis on live streams
– We don’t know in advance what this will be…
– User accessible API for analytics

12.
Many whiteboards later…

13.
BIG PICTURE!
Show me the…

14.
The Big Picture
Ingester Ingest Grid Distribution
Analytics
Stream
AMQP
Metrics
Firehose
HTTPHTTP
Databases
Analytics
Queries
Queries
- Unstructured
- Structured
Query, Adapt, React
feedback loop
Reactive
expressions
End User
Applications
Ingestion
& Filtering
Analytics &
Distribution
End-user /
Consumers

15.
Input Data Rates
RTI
 100k/s baseline
 ~120k/s daily peak
 >1M/s annual peak
Twitter*
 6k/s average
 9k/s daily peak
 30k/s large events
**Source @catehstn
twitter.com/catehstn/status/494918021358813184
OK, so Twitter’s internal fan-out & timeline access
rates & storage problems are vastly different!
(see also Redis…)

16.
Load Characteristics
 Low numbers of inbound connections
 High rates, micro-bursts
 Occasional peaks of nearly 2x, rare peaks of 10x
 Variable payload size (200B – 300KB)
 Internal fan-outs multiply event rates

17.
More statistics…
 100k/s order of magnitude
– 8,640,000,000 (per day)
– An Integer based counter will ‘roll over’ in ~3 days
 400Mbps of raw data
– 10Gbps NICs required to support traffic peaks
– Logging! Verbose errors can fill a disk quickly
– Queues backing up == #fail
 Upcoming 10x existing rates!

18.
REACTIVE APPLICATIONS?
What’s all this fuss about…

19.
Reactive Applications
www.reactivemanifesto.org
Responsive
ResilientScalable
Event (or message) driven
Depends on
Depends on

20.
Reactive Streams
• Collaboration between key industry players
18

21.
Reactive Streams: Specification
19
• Semantics
– Single document listing all rules
– Open enough to allow for various patterns
• 4 API interfaces
• TCK to verify implementation behaviour

22.
Reactive Streams
github.com/reactive-streams
org.reactivestreams.Processor
org.reactivestreams.Publisher
org.reactivestreams.Subscriber
org.reactivestreams.Subscription

23.
REACTIVE STREAMS API
A quick look at the…

24.
Spring Reactor
LMAX Disruptor – a RingBuffer
reactor.bus.EventBus
reactor.core.Dispatcher
reactor.rx.Stream

25.
REACTOR API
A quick look at the…

26.
Spring Integration
Enterprise Integration Patterns
See http://www.enterpriseintegrationpatterns.com/ by Hohpe & Woolf
Messages
Channels
Endpoints
(pipes & filters architecture)

27.
SI Pipeline Example

28.
SPRING INTEGRATION JAVA DSL
A quick look at the…

29.
Spring Integration Performance
• 3.x
– Take out all the SpEL
• 4.x
– 4.0
• Improved
– 4.1 (Q4 2014)
• Put back all the SpEL
– 4.2 (late 2015)
• Rather good

30.
BIG PICTURE AGAIN
Back to the…

31.
The Big Picture
Ingester Ingest Grid Distribution
Analytics
Stream
AMQP
Metrics
Firehose
HTTPHTTP
Databases
Analytics
Queries
Queries
- Unstructured
- Structured
Query, Adapt, React
feedback loop
Reactive
expressions
End User
Applications
Ingestion
& Filtering
Analytics &
Distribution
End-user /
Consumers
Key Reactor usages

32.
Reactor Usage
• UDP/TCP Servers (or clients)
• Outputs – batching
• Dispatchers & Streams
– Expression evaluation engine

33.
Spring Integration + Reactor
• Batching
– Adaptive sizing
• Routing
– With … batching

34.
SPRING INTEGRATION + REACTOR
An example or two…

35.
TEMPORAL DATA SERIES?
But what about the…

36.
Temporal Data
RingBuffer
New DataOld Data
Expressions

37.
SECRET ‘GO-FASTER’ SWITCH?
And there was something about a…

38.
Spring Expression Language
(SpEL)
• Powerful expression language
• Supports querying and manipulating an object
graph at runtime
• Similar to Unified EL
– Additional features, include method invocation
and string templating.

39.
SpEL is slow!


40.
Enter SpEL Compilation
• 3 modes
– Immediate
– Mixed
– Off
• -Dspring.expression.compiler.mode=mixed

41.
SPEL DEMO
And now for a quick

42.
SpEL is fast!


43.
Fin
And relax

44.
QUESTIONS?
And now for some…
@pidster @smaldini

45.
Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/reactive-
app-design-spring