SlideShare a Scribd company logo
1 of 42
Disruptor –
Ultrafast communication
March 2012
#theedge2012
Guy Raz Nir
Disruptor
» Introduction
» The problem …
» The (not so good) alternatives
» Architecture
» Summary
Agenda
Disruptor
Hida !
(Quiz!)
Disruptor
Introduction
Disruptor
» QUEUE !
» Communication facility between threads.
Disruptor
Disruptor
» London Multi Asset Exchange platform
» Can handle up to 6,000,000 TPS
▪ Dual socket, 3GHz quad-core Nehalem processors.
» 98% transactions under 38ms.
» Average transaction length: 9.22ms
LMAX
Disruptor
» To learn about LMAX Disruptor abilities and
usability.
» To practice “different thinking” in order to
solve complex problems.
Why are we here ?
Disruptor
"Any intelligent fool can make things bigger,
more complex, and more violent.
It takes a touch of genius,
and a lot of courage
to move in the opposite direction.“
Albert Einstein
Disruptor
Disruptor
57.3 MB/s
-20%
Disruptor
“Mechanical Sympathy”
Hardware and software working together in harmony *
* Martin Thompson’s blog
Disruptor
The problem …
Disruptor
» Test case: execution 10,000,000 primitive
increments.
» Single thread execution: ~ 7ms
▪ No concurrency
Multi-threading test
long value = 0;
while (value < 10000000L) {
value++;
Disruptor
» Synchronized approach:
» Mutual exclusion approach:
» AtomicLong:
java.util.concurrent.locks.ReentrantLock lock = …
lock.lock();
value++;
lock.unlock();
synchronized (syncObj) {
value++;
AtomicLong value = new AtomicLong(0);
long result = value.incrementAndGet();
Disruptor
» Single thread, bare execution (value++)
▪ About ~7 milliseconds
» Single thread, AtomicLong
▪ 68 milliseconds (x8.5)
» Single thread with lock
▪ 125 milliseconds (x15.5)
» Single thread with synchronized approach
▪ 450 milliseconds (x56)
Disruptor
» Single thread, bare execution (value++)
▪ About ~7 milliseconds
» Two threads, AtomicLong
▪ 270 milliseconds (x33.7)
» Two thread with lock
▪ 298 milliseconds (x37.5)
Disruptor
Concurrent execution latency (increment)
Time(ms)
Number of threads
Synchronized CAS Lock
Disruptor
Concurrent execution latency (PI calculation)
Time(ms)
Number of threads
Synchronized Lock
“A good preliminary design
overcomes any lastly patch”
Guy (Raz) Nir, The Edge 2012
Disruptor
Core #1
L1 cache
Core #2
L1 cache
Core #3
L1 cache
Core #4
L1 cache
L2 cache L2 cache L2 cache L2 cache
L3 cache
32KB inst.
32KB data
256KB
2M – 16M
64-bit
registers
Model CPU architecture
Disruptor
Non-volatile vs volatile
Non-volatile Volatile
7
Time(ms)
Disruptor
The (not so good) alternatives
Disruptor
» Linked-list based queue
▪ Requires re-allocation of units
▪ Memory fragmentation
▪ Garbage collection
▪ Bad contention
#0 #1 #2 #3 #4
Disruptor
» Cyclic array-based queue
▪ Bad contention
#0 #1 #2 #3 #4
Head
Tail
Disruptor
java.util.concurrent.ArrayBlockingQueue
// Put new element in the queue.
public boolean offer(E e, long timeout, TimeUnit unit) {
// Consume ‘lock’ for writing.
final ReentrantLock lock = this.lock;
lock.lock();
}
// Take one element from the queue.
public E poll() {
// Consume ‘lock’ for reading.
final ReentrantLock lock = this.lock;
lock.lock();
}
Sun (Oracle) JDK 1.7.0_u2
Disruptor
» General purpose assumptions:
▪ Multiple readers, multiple writers
▪ Queues can run as big as memory
▪ Other operations that degrade design
» No regards to hardware
Other problems
Disruptor
Architecture
Disruptor
» Barriers
» Ring buffer
» Sequences
Main components
Disruptors
Barriers
Disruptor
The ring buffer
1 2
3
4
MyDataType[] buffer = ...;
int offset = sequence % buffer.length;
Next read
sequence
Available
sequence
Disruptor
» Array-based cyclic buffer.
▪ Fast index-based accessed.
» Allow us to allocate all entries in advance
▪ Save GC time
▪ Continuous block allocation
▪ Save new costs at runtime.
The ring buffer
1 2
3
4
5
Ring Buffer
Disruptor
Barriers
1 2
3
4
5
Producer Consumer
sequence
nextSequence
Disruptor
public class StandardProducer {
public void offer(Object o) {
// ...
}
}
public class DisruptorProducer {
private RingBuffer buffer;
public void addMessage(String message, long timestamp) {
int seq = buffer.writeSequenceNumber++;
buffer.data[seq].msg = message;
buffer.data[seq].timestamp = timestamp;
buffer.availableSequenceNumber = seq;
}
}
X
Disruptor
public class DisruptorConsumer {
private RingBuffer buffer;
int nextSequenceNumber;
public Object take() {
while (nextSequenceNumber < buffer.sequenceNumber) { .. }
return buffer.get(nextSequenceNumber++);
}
}
Buffer.sequenceNumber
My
sequence
number
Ring Buffer
Disruptor
Multi consumers
1 2
3
4
5
Consumer
sequence
nextSequence = 2
Sequence
barrier
Consumer
nextSequence = 3
Consumer
nextSequence = 4
Disruptor
» Allow us to fetch multiple elements.
» Using event processors
▪ Callbacks
Batches & Events
Disruptor
Code sample – Create ring buffer
//
// Create a new ring buffer.
//
RingBuffer<MyEvent> ringBuffer = new RingBuffer<MyEvent>(
new MyOwnFactory(),
new SingleThreadedClaimStrategy(sizeOfRing),
new SleepingWaitStrategy());
Disruptor
Code sample - Producer
// Request the next available sequence number.
long sequence = buffer.next();
// Fetch the object at the that location.
MyEvent event = buffer.get(sequence);
//
// ... do something with the event.
//
// Notify the rest of the world this event is ready to be consumed.
buffer.publish(sequence);
Disruptor
Code sample - Consumer
// Extract a consumer's barrier.
SequenceBarrier barrier = ringBuffer.newBarrier();
// Wait for an event to come.
barrier.waitFor(nextSequence);
// Take the event (data).
MyEvent event = ringBuffer.get(nextSequence);
Disruptor
» Disruptor is a smart Queue.
» Latest release is 2.8
» Exploit hardware acceleration points.
» Won the Duke’s 2011 award for innovation !
Summary
Disruptor
» Google code:
▪ http://code.google.com/p/disruptor/
» Technical paper:
▪ http://disruptor.googlecode.com/files/Disruptor-1.0.pdf
» Martin Thompson’s blog:
▪ http://mechanical-sympathy.blogspot.com
» Trisha Gee’s blog:
▪ http://mechanitis.blogspot.com/
» InfoQ on Disruptor (session video):
▪ http://www.infoq.com/presentations/LMAX
References
Guy Raz Nir
guyn@alphacsp.com

More Related Content

What's hot

Practical non blocking microservices in java 8
Practical non blocking microservices in java 8Practical non blocking microservices in java 8
Practical non blocking microservices in java 8Michal Balinski
 
SOLID & Design Patterns
SOLID & Design PatternsSOLID & Design Patterns
SOLID & Design PatternsGrokking VN
 
Zero-Copy Event-Driven Servers with Netty
Zero-Copy Event-Driven Servers with NettyZero-Copy Event-Driven Servers with Netty
Zero-Copy Event-Driven Servers with NettyDaniel Bimschas
 
Event Driven-Architecture from a Scalability perspective
Event Driven-Architecture from a Scalability perspectiveEvent Driven-Architecture from a Scalability perspective
Event Driven-Architecture from a Scalability perspectiveJonas Bonér
 
Grokking Techtalk #40: Consistency and Availability tradeoff in database cluster
Grokking Techtalk #40: Consistency and Availability tradeoff in database clusterGrokking Techtalk #40: Consistency and Availability tradeoff in database cluster
Grokking Techtalk #40: Consistency and Availability tradeoff in database clusterGrokking VN
 
Java Concurrency Gotchas
Java Concurrency GotchasJava Concurrency Gotchas
Java Concurrency GotchasAlex Miller
 
Low level java programming
Low level java programmingLow level java programming
Low level java programmingPeter Lawrey
 
Producer Performance Tuning for Apache Kafka
Producer Performance Tuning for Apache KafkaProducer Performance Tuning for Apache Kafka
Producer Performance Tuning for Apache KafkaJiangjie Qin
 
Grokking Techtalk #37: Data intensive problem
 Grokking Techtalk #37: Data intensive problem Grokking Techtalk #37: Data intensive problem
Grokking Techtalk #37: Data intensive problemGrokking VN
 
KafkaConsumer - Decoupling Consumption and Processing for Better Resource Uti...
KafkaConsumer - Decoupling Consumption and Processing for Better Resource Uti...KafkaConsumer - Decoupling Consumption and Processing for Better Resource Uti...
KafkaConsumer - Decoupling Consumption and Processing for Better Resource Uti...confluent
 
Low latency in java 8 v5
Low latency in java 8 v5Low latency in java 8 v5
Low latency in java 8 v5Peter Lawrey
 
Asynchronous processing in big system
Asynchronous processing in big systemAsynchronous processing in big system
Asynchronous processing in big systemNghia Minh
 
Introduction to memcached
Introduction to memcachedIntroduction to memcached
Introduction to memcachedJurriaan Persyn
 
From cache to in-memory data grid. Introduction to Hazelcast.
From cache to in-memory data grid. Introduction to Hazelcast.From cache to in-memory data grid. Introduction to Hazelcast.
From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky
 
[Outdated] Secrets of Performance Tuning Java on Kubernetes
[Outdated] Secrets of Performance Tuning Java on Kubernetes[Outdated] Secrets of Performance Tuning Java on Kubernetes
[Outdated] Secrets of Performance Tuning Java on KubernetesBruno Borges
 
Apache Kafka Architecture & Fundamentals Explained
Apache Kafka Architecture & Fundamentals ExplainedApache Kafka Architecture & Fundamentals Explained
Apache Kafka Architecture & Fundamentals Explainedconfluent
 
Reactive Programming for a demanding world: building event-driven and respons...
Reactive Programming for a demanding world: building event-driven and respons...Reactive Programming for a demanding world: building event-driven and respons...
Reactive Programming for a demanding world: building event-driven and respons...Mario Fusco
 

What's hot (20)

Practical non blocking microservices in java 8
Practical non blocking microservices in java 8Practical non blocking microservices in java 8
Practical non blocking microservices in java 8
 
SOLID & Design Patterns
SOLID & Design PatternsSOLID & Design Patterns
SOLID & Design Patterns
 
Zero-Copy Event-Driven Servers with Netty
Zero-Copy Event-Driven Servers with NettyZero-Copy Event-Driven Servers with Netty
Zero-Copy Event-Driven Servers with Netty
 
Event Driven-Architecture from a Scalability perspective
Event Driven-Architecture from a Scalability perspectiveEvent Driven-Architecture from a Scalability perspective
Event Driven-Architecture from a Scalability perspective
 
Apache ZooKeeper
Apache ZooKeeperApache ZooKeeper
Apache ZooKeeper
 
Grokking Techtalk #40: Consistency and Availability tradeoff in database cluster
Grokking Techtalk #40: Consistency and Availability tradeoff in database clusterGrokking Techtalk #40: Consistency and Availability tradeoff in database cluster
Grokking Techtalk #40: Consistency and Availability tradeoff in database cluster
 
Java Concurrency Gotchas
Java Concurrency GotchasJava Concurrency Gotchas
Java Concurrency Gotchas
 
Low level java programming
Low level java programmingLow level java programming
Low level java programming
 
Mutiny + quarkus
Mutiny + quarkusMutiny + quarkus
Mutiny + quarkus
 
Producer Performance Tuning for Apache Kafka
Producer Performance Tuning for Apache KafkaProducer Performance Tuning for Apache Kafka
Producer Performance Tuning for Apache Kafka
 
Grokking Techtalk #37: Data intensive problem
 Grokking Techtalk #37: Data intensive problem Grokking Techtalk #37: Data intensive problem
Grokking Techtalk #37: Data intensive problem
 
Bizweb Microservices Architecture
Bizweb Microservices ArchitectureBizweb Microservices Architecture
Bizweb Microservices Architecture
 
KafkaConsumer - Decoupling Consumption and Processing for Better Resource Uti...
KafkaConsumer - Decoupling Consumption and Processing for Better Resource Uti...KafkaConsumer - Decoupling Consumption and Processing for Better Resource Uti...
KafkaConsumer - Decoupling Consumption and Processing for Better Resource Uti...
 
Low latency in java 8 v5
Low latency in java 8 v5Low latency in java 8 v5
Low latency in java 8 v5
 
Asynchronous processing in big system
Asynchronous processing in big systemAsynchronous processing in big system
Asynchronous processing in big system
 
Introduction to memcached
Introduction to memcachedIntroduction to memcached
Introduction to memcached
 
From cache to in-memory data grid. Introduction to Hazelcast.
From cache to in-memory data grid. Introduction to Hazelcast.From cache to in-memory data grid. Introduction to Hazelcast.
From cache to in-memory data grid. Introduction to Hazelcast.
 
[Outdated] Secrets of Performance Tuning Java on Kubernetes
[Outdated] Secrets of Performance Tuning Java on Kubernetes[Outdated] Secrets of Performance Tuning Java on Kubernetes
[Outdated] Secrets of Performance Tuning Java on Kubernetes
 
Apache Kafka Architecture & Fundamentals Explained
Apache Kafka Architecture & Fundamentals ExplainedApache Kafka Architecture & Fundamentals Explained
Apache Kafka Architecture & Fundamentals Explained
 
Reactive Programming for a demanding world: building event-driven and respons...
Reactive Programming for a demanding world: building event-driven and respons...Reactive Programming for a demanding world: building event-driven and respons...
Reactive Programming for a demanding world: building event-driven and respons...
 

Similar to LMAX Disruptor as real-life example

Low-latency Multi-threaded Ensemble Learning for Dynamic Big Data Streams
Low-latency Multi-threaded Ensemble Learning for Dynamic Big Data StreamsLow-latency Multi-threaded Ensemble Learning for Dynamic Big Data Streams
Low-latency Multi-threaded Ensemble Learning for Dynamic Big Data StreamsDiego Marrón Vida
 
Real-Time Big Data with Storm, Kafka and GigaSpaces
Real-Time Big Data with Storm, Kafka and GigaSpacesReal-Time Big Data with Storm, Kafka and GigaSpaces
Real-Time Big Data with Storm, Kafka and GigaSpacesOleksii Diagiliev
 
Hs java open_party
Hs java open_partyHs java open_party
Hs java open_partyOpen Party
 
Designs, Lessons and Advice from Building Large Distributed Systems
Designs, Lessons and Advice from Building Large Distributed SystemsDesigns, Lessons and Advice from Building Large Distributed Systems
Designs, Lessons and Advice from Building Large Distributed SystemsDaehyeok Kim
 
Nibiru: Building your own NoSQL store
Nibiru: Building your own NoSQL storeNibiru: Building your own NoSQL store
Nibiru: Building your own NoSQL storeEdward Capriolo
 
Building your own NSQL store
Building your own NSQL storeBuilding your own NSQL store
Building your own NSQL storeEdward Capriolo
 
Nibiru: Building your own NoSQL store
Nibiru: Building your own NoSQL storeNibiru: Building your own NoSQL store
Nibiru: Building your own NoSQL storeEdward Capriolo
 
There's no magic... until you talk about databases
 There's no magic... until you talk about databases There's no magic... until you talk about databases
There's no magic... until you talk about databasesESUG
 
A Deep Dive into Apache Cassandra for .NET Developers
A Deep Dive into Apache Cassandra for .NET DevelopersA Deep Dive into Apache Cassandra for .NET Developers
A Deep Dive into Apache Cassandra for .NET DevelopersLuke Tillman
 
Communicating State Machines
Communicating State MachinesCommunicating State Machines
Communicating State Machinessrirammalhar
 
SignalFx Kafka Consumer Optimization
SignalFx Kafka Consumer OptimizationSignalFx Kafka Consumer Optimization
SignalFx Kafka Consumer OptimizationSignalFx
 
Threads and multi threading
Threads and multi threadingThreads and multi threading
Threads and multi threadingAntonio Cesarano
 
Flink Forward Berlin 2017: Robert Metzger - Keep it going - How to reliably a...
Flink Forward Berlin 2017: Robert Metzger - Keep it going - How to reliably a...Flink Forward Berlin 2017: Robert Metzger - Keep it going - How to reliably a...
Flink Forward Berlin 2017: Robert Metzger - Keep it going - How to reliably a...Flink Forward
 
5 Pitfalls to Avoid with MongoDB
5 Pitfalls to Avoid with MongoDB5 Pitfalls to Avoid with MongoDB
5 Pitfalls to Avoid with MongoDBTim Callaghan
 
Optimal Strategies for Large-Scale Batch ETL Jobs
Optimal Strategies for Large-Scale Batch ETL JobsOptimal Strategies for Large-Scale Batch ETL Jobs
Optimal Strategies for Large-Scale Batch ETL JobsEmma Tang
 
Optimal Strategies for Large Scale Batch ETL Jobs with Emma Tang
Optimal Strategies for Large Scale Batch ETL Jobs with Emma TangOptimal Strategies for Large Scale Batch ETL Jobs with Emma Tang
Optimal Strategies for Large Scale Batch ETL Jobs with Emma TangDatabricks
 
Everything We Learned About In-Memory Data Layout While Building VoltDB
Everything We Learned About In-Memory Data Layout While Building VoltDBEverything We Learned About In-Memory Data Layout While Building VoltDB
Everything We Learned About In-Memory Data Layout While Building VoltDBjhugg
 
Clojure ♥ cassandra
Clojure ♥ cassandra Clojure ♥ cassandra
Clojure ♥ cassandra Max Penet
 

Similar to LMAX Disruptor as real-life example (20)

Low-latency Multi-threaded Ensemble Learning for Dynamic Big Data Streams
Low-latency Multi-threaded Ensemble Learning for Dynamic Big Data StreamsLow-latency Multi-threaded Ensemble Learning for Dynamic Big Data Streams
Low-latency Multi-threaded Ensemble Learning for Dynamic Big Data Streams
 
Disruptor
DisruptorDisruptor
Disruptor
 
Real-Time Big Data with Storm, Kafka and GigaSpaces
Real-Time Big Data with Storm, Kafka and GigaSpacesReal-Time Big Data with Storm, Kafka and GigaSpaces
Real-Time Big Data with Storm, Kafka and GigaSpaces
 
Hs java open_party
Hs java open_partyHs java open_party
Hs java open_party
 
Designs, Lessons and Advice from Building Large Distributed Systems
Designs, Lessons and Advice from Building Large Distributed SystemsDesigns, Lessons and Advice from Building Large Distributed Systems
Designs, Lessons and Advice from Building Large Distributed Systems
 
Nibiru: Building your own NoSQL store
Nibiru: Building your own NoSQL storeNibiru: Building your own NoSQL store
Nibiru: Building your own NoSQL store
 
Building your own NSQL store
Building your own NSQL storeBuilding your own NSQL store
Building your own NSQL store
 
Nibiru: Building your own NoSQL store
Nibiru: Building your own NoSQL storeNibiru: Building your own NoSQL store
Nibiru: Building your own NoSQL store
 
There's no magic... until you talk about databases
 There's no magic... until you talk about databases There's no magic... until you talk about databases
There's no magic... until you talk about databases
 
A Deep Dive into Apache Cassandra for .NET Developers
A Deep Dive into Apache Cassandra for .NET DevelopersA Deep Dive into Apache Cassandra for .NET Developers
A Deep Dive into Apache Cassandra for .NET Developers
 
Communicating State Machines
Communicating State MachinesCommunicating State Machines
Communicating State Machines
 
SignalFx Kafka Consumer Optimization
SignalFx Kafka Consumer OptimizationSignalFx Kafka Consumer Optimization
SignalFx Kafka Consumer Optimization
 
Threads and multi threading
Threads and multi threadingThreads and multi threading
Threads and multi threading
 
Data Collection and Storage
Data Collection and StorageData Collection and Storage
Data Collection and Storage
 
Flink Forward Berlin 2017: Robert Metzger - Keep it going - How to reliably a...
Flink Forward Berlin 2017: Robert Metzger - Keep it going - How to reliably a...Flink Forward Berlin 2017: Robert Metzger - Keep it going - How to reliably a...
Flink Forward Berlin 2017: Robert Metzger - Keep it going - How to reliably a...
 
5 Pitfalls to Avoid with MongoDB
5 Pitfalls to Avoid with MongoDB5 Pitfalls to Avoid with MongoDB
5 Pitfalls to Avoid with MongoDB
 
Optimal Strategies for Large-Scale Batch ETL Jobs
Optimal Strategies for Large-Scale Batch ETL JobsOptimal Strategies for Large-Scale Batch ETL Jobs
Optimal Strategies for Large-Scale Batch ETL Jobs
 
Optimal Strategies for Large Scale Batch ETL Jobs with Emma Tang
Optimal Strategies for Large Scale Batch ETL Jobs with Emma TangOptimal Strategies for Large Scale Batch ETL Jobs with Emma Tang
Optimal Strategies for Large Scale Batch ETL Jobs with Emma Tang
 
Everything We Learned About In-Memory Data Layout While Building VoltDB
Everything We Learned About In-Memory Data Layout While Building VoltDBEverything We Learned About In-Memory Data Layout While Building VoltDB
Everything We Learned About In-Memory Data Layout While Building VoltDB
 
Clojure ♥ cassandra
Clojure ♥ cassandra Clojure ♥ cassandra
Clojure ♥ cassandra
 

Recently uploaded

The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProduct Anonymous
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?Igalia
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...apidays
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 

Recently uploaded (20)

The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 

LMAX Disruptor as real-life example

Editor's Notes

  1. 16 threadsAbout 375K TPS per threadAbout 518 billion T/day.1 cent per transaction = 5 billion Euros per day.Won the Duke’s 2011 award for innovation !
  2. Sun (Oracle) JDK 1.7Intel i7 2600K (SandyBridge) + Overclocking
  3. Intel i7 2600K SandyBridgeL1 cache speed: 450GB/secCPU – Memory speed: about 18GB (x25 slower than L1)
  4. Single-threaded example
  5. Single-threaded example
  6. Producers works in the same way.Disruptor provide various barriers for various models.