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SQUIRRELS AND ELEPHANTS
Big Data and Streaming at InnoGames
LET‘S GO ON A ROADTRIP
IMAGINE
You are driving
with your
family on the
backseat
IMAGINE
There is a lot of
traffic, you
have to
concentrate
IMAGINE
And now, while driving, you are closing your eyes
Squirrels and Elephants - The InnoGames Big Data and Streaming Infrastructure
HOW DO YOU FEEL?
WOULD YOU EVER DO THAT?
NOPE
METAPHOR
The car is your company,
team or project
The passengers are your
colleagues
WOULD YOU EVER DO THAT?
NOPE
IN A METAPHORICAL SENSE
YOU HAVE TO PROCESS DATA
ON TIME AS IT HAPPENS
BATCH PROCESSING…
…might cause accidents
Because…
LIFE
DOESN’T
HAPPEN
IN BATCHES
https://mapr.com/ebooks/streaming-architecture/chapter-01-why-event-streaming.html
© Ellen Friedman, Ted Dunning
ICE CREAM
AND
GAMING?
SIMILARITIES
MAKE US HAPPY
SIMILARITIES
WHICH TASTES BETTER?
SIMILARITIES
WHICH TASTES BETTER?
SIMILARITIES
THE FIRST IMPRESSION COUNTS
The moment the customer enters
the shop or the player plays his first
session is crucial
HALO EFFECT
When one trait of a person or
thing is used to make an overall
judgment of that person or
thing
IN ORDER TO MAKE A
POSITIVE IMPACT
A RESPONSE NEEDS TO HAPPEN
QUICKLY
TIME-VALUE
OF
INFORMATION
REAL-TIME USER REPORTS
TRAFFIC
GAS PRICE
SPEED TRAPS
RESPOND TO
LIFE
AS IT
HAPPENS
STREAM PROCESSING
STREAMS OF DATA
GPS DATA WEB INTERACTION SENSOR DATA
STREAM PROCESSING
PROCESSING DATA IN MOTION
STREAM PROCESSING
YOUR
CODE
SOURCE SINKOPERATOR
STREAM PROCESSING LAKE
STREAM PROCESSING LAKE
APACHE FLINK
APACHE FLINK
Framework and distributed
process engine for stateful
computations on unbounded
and bounded data streams
STREAMS
TIME
WINDOWS
EVERYTHING IS A STREAM
UNBOUNDED STREAMS
BOUNDED STREAMS
EVERYTHING IS A STREAM
UNBOUNDED STREAMS
BOUNDED STREAMS
AKA BATCH PROCESSING
TIME IN STREAMING
EPISODE I EPISODE II EPISODE III EPISODE IV EPISODE V EPISODE VI EPISODE VII EPISODE VIII EPISODE IX
1999 2002 2005 1977 1980 1983 2015 2017 2019
The
Phantom
Menace
Attack of
the Clones
Revenge of
the Sith
A New
Hope
The Empire
Strikes Back
Return of
the Jedi
The Force
Awakens
The Last
Jedi
?
ORDERED BY EVENT TIME
PROCESSING TIME
TIME IN STREAMING
EPISODE I EPISODE II EPISODE IIIEPISODE IV EPISODE V EPISODE VI EPISODE VII EPISODE VIII EPISODE IX
1999 2002 20051977 1980 1983 2015 2017 2019
The
Phantom
Menace
Attack of
the Clones
Revenge of
the Sith
A New
Hope
The Empire
Strikes Back
Return of
the Jedi
The Force
Awakens
The Last
Jedi
?
EVENT TIME
ORDERED BY PROCESSING TIME
TUMBLING WINDOWS
9 1 3 2 6 8 1 3 9 8 4 5
9 1 3 2 6 8 1 3 9 8 4 5
15 18 26
SENSOR
SUM
SLIDING WINDOWS
9 1 3 2 6 8 1 3 9 8 4 5
9 1 3 2 6 8 1 3 9 8 4 5
9 1 3 2 6 8 1 3 9 8 4 5
15 18 26
19 21
SENSOR
SUM
BUILDING BLOCKS
DATA SOURCE TRANSFORMATION DATA SINK
API
BUILDING BLOCKS
DATA SOURCE TRANSFORMATION DATA SINK
BUILDING BLOCKS
SQL / TABLE API
DataStream API
ProcessFunction
APIs
(dynamic tables)
(streams, windows)
(events, state, time)
HIGH LEVEL
ANALYTICS API
STREAM AND BATCH
DATA PROCESSING
STATEFUL EVENT-
DRIVEN APPLICATIONS
CONCISENESS
EXPRESSIVENESS
LET‘S HAVE A CLOSER LOOK
LET‘S HAVE A CLOSER LOOK
final StreamExecutionEnvironment env = getExecutionEnvironment();
final DataStreamSource<Integer> stream = env.fromElements(1, 2, 3, 4);
stream
.map((MapFunction<Integer, Integer>) i -> i + 2)
.filter((FilterFunction<Integer>) i -> i % 2 == 0)
.print();
env.execute();
DATA SOURCE
TRANSFORMATION
DATA SINK
RUNTIME
YOUR FLINK APP
FLINK RUNTIMEDEPLOY
RUNTIME
BIG DATA AND STREAMING
AT
INNOGAMES
TEAM
TEAM
BUSINESS INTELLIGENCE DATA ENGINEERING DATA SCIENCE OPERATIONS
EVENT TRACKING
quest
build
fight invite
EVENT TRACKING
1.500.000.000EVENTS PER DAY
DATA ARCHITECTURE
DATA PIPELINE DATA PLATFORM
milliseconds, seconds, minutes hours, days, years
DATA ARCHITECTURE
SQUIRREL ELEPHANT
DATA ARCHITECTURE
EVENT
CLIENT
EVENT
CLIENT
EVENT
CLIENT
EVENTGATEWAY
EVENT BUS
STREAM
PROCESSING
DISTRIBUTED DATA STORE
DISTRIBUTED BATCH PROCESSING
BUSINESS
INTELLIGENCE
DATA ARCHITECTURE
EVENT
CLIENT
EVENT
CLIENT
EVENT
CLIENT
EVENTGATEWAY
EVENT BUS
STREAM
PROCESSING
DISTRIBUTED DATA STORE
DISTRIBUTED BATCH PROCESSING
BUSINESS
INTELLIGENCE
Squirrels and Elephants - The InnoGames Big Data and Streaming Infrastructure
DATA ARCHITECTURE
EVENT
CLIENT
EVENT
CLIENT
EVENT
CLIENT
EVENTGATEWAY
EVENT BUS
STREAM
PROCESSING
DISTRIBUTED DATA STORE
DISTRIBUTED BATCH PROCESSING
BUSINESS
INTELLIGENCE
STREAM
PROCESSING
USE CASE EVENT METRICS
Metrics.java
stream
.map(streamEvent -> new Tuple2<>(streamEvent.getEventName(), 1))
.keyBy(0)
.timeWindow(Time.minutes(1))
.sum(1)
.addSink(graphiteSink).setParallelism(1).name("event_counts");
USE CASE EVENT METRICS
USE CASE LOG00 MONITOR
KeyedStream<StreamEvent, Integer> stream = events
.filter(event -> Arrays.asList("reg", "login").contains(event.getEventName()))
.keyBy((KeySelector<StreamEvent, Integer>) StreamEvent::getPlayerId);
Log00.java
Pattern<StreamEvent, StreamEvent> pattern =
Pattern.<StreamEvent>begin("reg").where(new SimpleCondition<StreamEvent>() {
@Override
public boolean filter(StreamEvent event) {
return event.getEventName().equals("reg");
}
}).followedBy("login").where(new SimpleCondition<StreamEvent>() {
@Override
public boolean filter(StreamEvent event) {
return event.getEventName().equals("login");
}
}).within(Time.seconds(60));
Log00.java
PatternStream<StreamEvent> patternStream = CEP.pattern(stream, pattern);
DataStream<Either<PatternResult, PatternResult>> patternResultStream =
patternStream.select(
(p, ts) -> sendTimeoutToGraphite(p, ts),
p -> sendSuccessToGraphite(p)
);
Log00.java
USE CASE LOG00 MONITOR
USE CASE NEAR TIME CRM(NTCRM)
USE CASE NTCRM
EVENT BUS
EVENT
CLIENT
EVENTGATEWAY
PLAYER DATANTCRM
React to events with interstitials in less than 10 seconds
USE CASE NTCRM
Elvenar has a trading feature that sometimes
causes confusion. With NTCRM we can react to
this and show more details within interstitials
exactly when the player needs it.
JUST DO IT
DEMO TIME
Check it out on Github: https://github.com/prenomenon/codetalks-flinkdemo
GET IN TOUCH
InnoGames GmbH
Friesenstrasse 13
20097 Hamburg
https://www.innogames.com
Volker Janz
Senior Software Developer
Corporate Systems - Analytics
GET IN TOUCH
@prenomenon
feedback appreciated
LIFE
DOESN’T
HAPPEN
IN BATCHES
EAT ICE CREAM
AND
STREAM ON
Great Flink training: http://training.data-artisans.com
NEXT UP
EVENT
CLIENT
EVENT
CLIENT
EVENT
CLIENT
EVENTGATEWAY
EVENT BUS
STREAM
PROCESSING
DISTRIBUTED DATA STORE
DISTRIBUTED BATCH PROCESSING
BI
BUSINESS
INTELLIGENCE
THAT’S IT FOR NOW…
BACKUP / DETAILS
The following slides are not part of my talk but
might give the reader more insights later
COMPANY SNAPSHOT
More than
400 employees
Founded 2007
in Germany
Headquarter in
Hamburg
+160m EUR revenue
made in 2017
7 live games
>30 language versions
I AM LEGEND
OUR PORTFOLIO Simulation Strategy RPG Browser Multi-device Mobile
SQUIRREL TESTS
<dependency
<groupId>org.apache.flink</groupId>
<artifactId>flink-test-utils_2.11</artifactId>
<version>1.6.1</version>
</dependency>
WINDOWING
KEYED NON-KEYED
TASK 1
TASK N
SOURCE TASK 1SOURCE
KEY 1
KEY N
ALL DATA
STATE
SOURCE MAP
DATA SINK
SOURCE MAP
SUM(C,D)
OFFSET
OFFSET
SUM(A,B)
AB
CD
RABBIT HOLE
RUNTIME
SOURCE MAP PRINTFILTER STREAMING DATAFLOW
(CONDENSED VIEW)
OPERATOR CHAIN OPERATOR OPERATOR
TASK TASK TASK
SOURCE MAP
PRINT
FILTER
OPERATOR CHAIN OPERATOR
OPERATOR
SUBTASK SUBTASK
TASKSOURCE MAP FILTER
OPERATOR CHAIN OPERATOR
SUBTASK SUBTASK
STREAM
PARTITIONS
STREAMING DATAFLOW
(PARALLELIZED VIEW)
RUNTIME
SOURCE MAP
PRINT
FILTER
OPERATOR CHAIN OPERATOR
OPERATOR
SUBTASK SUBTASK
TASKSOURCE MAP FILTER
OPERATOR CHAIN OPERATOR
SUBTASK SUBTASK
STREAM
PARTITIONS
STREAMING DATAFLOW
(PARALLELIZED VIEW)
A Flink cluster has a JOB MANAGER and multiple
TASK MANAGERS. Each of those is a JVM.
RUNTIME
Each Task Manager can manage MULTIPLE THREADS
executing TASKS / SUBTASKS.
SOURCE MAP
PRINT
FILTER
OPERATOR CHAIN OPERATOR
OPERATOR
THREAD THREAD
THREADSUBTASK SUBTASK
TASKSOURCE MAP FILTER
OPERATOR CHAIN OPERATOR
THREAD THREAD
SUBTASK SUBTASK
STREAM
PARTITIONS
STREAMING DATAFLOW
(PARALLELIZED VIEW)
CHECKPOINTING
checkpoint
barrier n
checkpoint
barrier n-1
checkpoint n+1 checkpoint n checkpoint n-1
Consistent, incremental snapshots of distributed data stream and operator state
Based on a paper from 1985, inspired by the Chandy-Lamport-Algorithm
STATE
OPERATOR STATE KEYED STATE
Bound only to
an operator
Bound to an
operator and key
PLUGGABLE BACKEND
MULTIPLE PRIMITIVES SUPPORTED
GUARANTEED CONSISTENCY IN CASE OF A FAILURE

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