trade and event processing at a massive scale - qcon ny 2012

Event Processing at Massive Scale Approaches to Concurrency

Uri Cohen @uri1803 | github.com/uric

Head of Product @ GigaSpaces

This is What It Used to Be Like

That’s What

It’s Like Now

Some Numbers

15 Billion Trades / Day on

NYSE alone

http://www.nytimes.com/2011/08/27/business/as-trade-volumes-soar-exchanges-cash-in.html

Some Numbers

That’s

641K Trades / Second

http://www.nytimes.com/2011/08/27/business/as-trade-volumes-soar-exchanges-cash-in.html

Some Numbers

12 Billion Shares change

hands every day

http://www.bloomberg.com/news/2012-01-23/stock-trading-is-lowest-in-u-s-since-2008.html

Some Numbers

$4 Million The cost of 1

millisecond of latency to a broker

http://www.tabbgroup.com/PublicationDetail.aspx?PublicationID=346

The Problem

Massive stream of events

Time is money, literally

Can’t lose a single message

Fairness is a must

Order Book -

Simplistic Example

Buy Sell 50, $12 60, $10

60, $11 100, $11

30, $10 30, $12

Buy Sell 50, $12 60, $10

60, $11 100, $11

30, $10 30, $12

Price: $10

Order Book -

Simplistic Example

Buy Sell 60, $11 10, $10

30, $10 100, $11

30, $12

Price: $10

Order Book -

Simplistic Example

Buy Sell 60, $11 10, $10

30, $10 100, $11

30, $12

Price: $10

Order Book -

Simplistic Example

Buy Sell 50, $11 100, $11

30, $10 30, $12

Price: $10

Order Book -

Simplistic Example

Buy Sell 50, $11 100, $11

30, $10 30, $12

Price: $11

Order Book -

Simplistic Example

Buy Sell 30, $10 50, $11

30, $12

Price: $11

Order Book -

Simplistic Example

What it Really Means

Low latencyIn memory, GC tuning

Scalability Multi-coreMulti-node

OrderingBy price, order time

Exclusivity

Resiliency

Trading is Just One Use Case

All things FCFS, with a limited stock

Flight booking

Betting

Online Auctions

Cloud Spot Instances

eCommerce

Let’s Talk Solutions

Queue (SEDA/Actor Style)

Not Validated Validated Processed

Validator Processor

Queue (SEDA /Actor Style)

The Good: Ordered (Is it fair?)Multi-threaded

The Bad: Not very scalable

LockingContext

switchingTransient

The Cost of Locking

Method Time in msec

Single Thread 300

Single Thread w/ Lock 10.000

2 Threads w/ Lock 224.000

Single Thread w/ CAS 5.700

2 Threads w/ CAS 30.000

Single Thread w/ Volatile Write 4.700

http://disruptor.googlecode.com/files/Disruptor-1.0.pdf

Queue (Lack of) Fairness

Consumer Thread 1

Consumer Thread 2

60

50 Buy Sell 100

Queue (Lack of) Fairness

Consumer Thread 1

Consumer Thread 2

50

60

Buy Sell 100

Queue (Lack of) Fairness

Consumer Thread 1

Consumer Thread 2

Buy Sell 100

60 50

Can you tell which order will be executed 1st?

Single-Threaded Queue

Validator Processor

Single- Threaded

Queue

The Good: Fast, very fast

No contentionNo context

switchesAlways fair

The Bad: Multi-core?

Not fit for Intense compute & I/O

Need to be async.Transient

Single- Threaded

Queue

They do it…

Disruptor (LMAX)

Segmented Queue

Symbol=A-H Symbol=I-S Symbol=T-Z

Validator Processor Processor

Processor thread pool per segment

Segmented Queue - Optimization Single Processor thread pool, pick random segment

Symbol=A-H Symbol=I-S Symbol=T-Z

Processor

Segmented Queue

The Good: Scalable

But segments can get hot

Minimizes contention

The Bad: Not trivial to

implementStill unfair

Is total ordering needed?

Transient

What about

Fairness?

Exclusivity is Key Process one message for each segment at the same time

No exclusivity across segments

Implicit ExclusivitySingle processor thread per segment

Symbol=A-H Symbol=I-S Symbol=T-Z

Processor Processor Processor

Explicit ExclusivityShared thread pool, mark segments under processing (CAS)

Segment 1 Segment 2 Segment 3

Processor

Segment 1

Segment 2

Segment 3

Explicit ExclusivityShared thread pool, mark segments under processing (CAS)

Segment 1 Segment 2 Segment 3

Processor

Segment 1 X

Segment 2

Segment 3

Explicit ExclusivityShared thread pool, mark segments under processing (CAS)

Segment 1 Segment 2 Segment 3

Processor

Segment 1 X

Segment 2

Segment 3 X

Explicit ExclusivityShared thread pool, mark segments under processing (CAS)

Segment 1 Segment 2 Segment 3

Processor

Segment 1

Segment 2

Segment 3 X

Explicit Exclusivity

Num. of segments is keyToo few: little concurrencyToo many: wasting memory

Dynamic Segmentation Segments are created and removed as needed

Processor

Dynamic Segmentation Segments are created and removed as needed

“GOOG”

Processor

“GOOG”

Dynamic Segmentation Segments are created and removed as needed

“GOOG”

Processor

“GOOG”

Dynamic Segmentation Segments are created and removed as needed

“GOOG”

Processor

GOOG

AAPL

“AAPL”

Dynamic Segmentation Segments are created and removed as needed

“GOOG”

Processor

GOOG X

AAPL

AMZN

“AAPL” “AMZN”

Dynamic Segmentation

Segments created as needed

Randomize on segments until available one found

Fast, scalable, fair

We call it “FIFO groups” or “Virtual Queues”

It Can (and Does) Get Much

More Complex

Memory state can get corrupt on errors

It’s not always as simple as “pop off the queue”

limits, priorities, circuit breakers, etc.

Resiliency is always a pain

A Bit about Usability

What you don’t want to do

Implement data structuresHandle concurrencyHandle HA Handle transactions

A Bit about Usability

What you want to control

Event flow Grouping attribute (e.g

symbol)Event handlers

Data Grid as a

Foundation

Transactional

Highly available

Supports complex matching

How We Thought

of It

How We Thought

of It

How We Thought

of It

How We Thought

of It

How We Thought

of It

Thank You!

References: http://martinfowler.com/articles/lmax.htmlhttp://www.nytimes.com/2011/08/27/business/as-trade-volumes-soar-exchanges-cash-in.html http://disruptor.googlecode.com/files/Disruptor-1.0.pdfhttp://www.gigaspaces.com/wiki/display/XAP9/FIFO+Grouping