Data distribution in the cloud with Node.js

Data distribution in the cloud with Node.js
Data Distribution in the cloud with Node.js Copyright Push Technology 2012
• British startup. Founded in 2006. • ‘Last mile’ data distribution specialist. • Data-centric approach to messaging/caching. • Preferred by 6 of the top 10 online eGaming exchanges. • Growing fast. 400% year on year. • Focus: Better bang for your bytes! Introducing Push Technology Copyright Push Technology 2012 Twitter: @push_technology
• Distributed Systems / HPC guy. • Chief Scientist :- at Push Technology • Alumnus of :- Motorola, IONA, Betfair, JPMC, StreamBase. • School: Trinity College Dublin. - BA (Mod). Comp. Sci.+ - M.Sc. Networks & Distributed Systems • Responds to: Guinness, Whisky About me? Copyright Push Technology 2012 Darach@PushTechnology.com
• Favorite language: Erlang • Favorite bits? • OTP – Behaviors • Bit Syntax • Least favorite language: Java • Paid to write this stuff • Love the JVM • Liking Node a lot. • Small fast data guy. I work in microseconds, measure in nanoseconds. On my critical path micro-benchmarking is a way of life. About me? Copyright Push Technology 2012 Darach@PushTechnology.com
1st clean room certified JVM in 10 years. Built in Dublin! It rocks. Tenant #1 Tenant #2 Tenant #N (Diffusion) (Diffusion) (Diffusion) Push Technology Diffusion Waratek Cloud VM for Java Benefits • High density deployments • Elastic. Scalable on demand • Meterability: Bandwidth and compute utilization • Multi-tenant. Each tenant fully isolated Copyright Push Technology 2012
A US Cap Market second? • 174 microseconds round trip time rules out High Frequency Trading applications. Not on the critical path! Source: Me, former life @StreamBase • http://slidesha.re/guZOVe
Data Distribution. Wat? Copyright Push Technology 2012
Traditional Messaging A B ba bb Producers ? Consumers Pros Cons • Loosely coupled. • No data model. Slinging blobs • All you can eat messaging patterns • Fast producer, slow consumer? Ouch. • Familiar • No data ‘smarts’. A blob is a blob. Copyright Push Technology 2012
Invented yonks ago… Before the InterWebs For ‘reliable’ networks For machine to machine Remember DEC Message Queues? - That basically. Vomit! Copyright Push Technology 2012
When fallacies were simple -The network is reliable -Latency is zero -Bandwidth is infinite -There is one administrator -The network is secure -Transport cost is zero -The network is homogeneous Copyright Push Technology 2012
Then in 1992, this happened: The phrase ‘surfing the internet’ was coined by Jean Poly. First base. Copyright Push Technology 2012
It grew, and it grew Copyright Push Technology 2012
Then in 2007, this happened: The god phone: Surfing died. Touching happened. Second base unlocked. Copyright Push Technology 2012
Then in 2007, this happened: So we took all the things and put them in the internet: Cloud happened. So we could touch all the things. Messaging Apps Hardware Virtualize all the things Services Skills, Specialties Copyright Push Technology 2012
Then in 2009, this happened: Ryan Dahl, basically. Tyrannically asynchronous. Devilishly event oriented. Amazoidingly non-blocking. Copyright Push Technology 2012
It grew, and it grew Like all the good things do. Copyright Push Technology 2012
Stop. Fallacies? Reality: -The network is not reliable nor is it cost free. -Latency is not zero nor is it a democracy. -Bandwidth is not infinite nor predictable especially the last mile! -There is not only one administrator trust, relationships are key -The network is not secure nor is the data that flows through it -Transport cost is not zero but what you don’t do is free -The network is not homogeneous nor is it smart Copyright Push Technology 2012
Look. What, How & Why? -What and How are what geeks do. -Why gets you paid -Business Value and Trust dictate What and How - Policies, Events and Content implements Business Value -Science basically. But think like a carpenter: -Measure twice. Cut once. Copyright Push Technology 2012
The Problem: The bird, basically. Immediately Inconsistent. But, Eventually Consistent … Maybe. Copyright Push Technology 2012
Listen. - Every nuance comes with a set of tradeoffs. - Choosing the right ones can be hard, but it pays off. - Context, Environment are critical - Break all the rules, one benchmark at a time. - Benchmark Driven Development FTW Copyright Push Technology 2012
Act. - You measured twice, right? - So get cutting! - Simples Copyright Push Technology 2012
Act. Telepathy? Telemetry! A B ba bb Buffer Producers Bloat Consumers Virtualize client queues? Nuance: ‘See’ backlog, client affinity. Tradeoff GD harder :/ Copyright Push Technology 2012
Act. Stateless or Stateful Topics A B ba x bb x Producers Is it a Consumers cache? Data one hop closer to consumers. Good state? Touch it! Exploit it! Use it! Copyright Push Technology 2012
Act. Finagle the data A B Snapshot Delta ba x bb x Producers State! Consumers Last value cached. Tradeoff? Memory. Snapshot on subscribe. Deltas thereafter Copyright Push Technology 2012
Act. ‘Smart data’ A B C A C D t0 t1 Don’t repeat yourself. Send the changes, not the whole list after initial ‘snapshot’. Copyright Push Technology 2012
Act. Behaviors A B ba x bb x X The Producers topic is Consumers the cloud! Extensible. Nuance? Roll your own protocols. Tradeoff? 3rd party code in the engine :/ Copyright Push Technology 2012
Data Distribution Messaging remixed around: Relevance - Queue depth for conflatable data should be 0 or 1. No more Responsiveness - Use HTTP/REST for things. Stream the little things Timeliness - It’s relative. M2M != M2H. Context - Packed binary, deltas mostly, snapshot on subscribe. Environment- Don’t send 1M 1K events to a mobile phone with 0.5mbps. Copyright Push Technology 2012
An Example Operations:> Tenants :> Gaming Live Internet Apps Finance QA + Dev + UAT Copyright Push Technology 2012
Either way? It’s about the data. Period. The rest (analysis, storage, transformation) is sugar. Copyright Push Technology 2012
Sugar? Streams w w S C Q w w Stream Operations • Mapping. Change/enrich the data structurally. • Aggregation. A ‘window of’ data. Eg. A seconds worth. • Splitting & Filtering • Combining multiple streams. Eg. Temporal pattern matching • Access/Store. Eg: CRUD, variable, file, … Copyright Push Technology 2012
Sugar? Streams w w S C Q w w Stream Operations • Mapping. Just a function call in Node.js • Aggregation. A ‘window of’ data. Eg. A seconds worth. • Splitting & Filtering. An expression or a set thereof. • Combining multiple streams. It depends. Can be ‘complex’ • Access/Store. Trivial. Copyright Push Technology 2012
Embedded Event Processing with Node.js. eep.js Copyright Push Technology 2012
Introducing eep.js w w S C Q w w What is eep.js? • Add aggregate functions and window operations to Node.js • 4 window types: tumbling, sliding, periodic, monotonic • Node makes evented IO easy. So just add windows. • Fast. 8-40 million events per second (upper bound). Copyright Push Technology 2012
eep.js: Tumbling Windows x() x() x() x() emit() x() x() x() x() emit() 1 2 3 4 x() x() x() x() emit() 2 3 4 5 init() 2 3 4 5 init() init() t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 ... What is a tumbling window? • Every N events, give me an average of the last N events • Does not overlap windows • ‘Closing’ a window, ‘Emits’ a result (the average) • Closing a window, Opens a new window Copyright Push Technology 2012
eep.js: Aggregate Functions What is an aggregate function? • A function that computes values over events. • The cost of calculations are ammortized per event • Just follow the above recipe • Example: Aggregate 2M events (equity prices), send to GPU on emit, receive 2M options put/call prices as a result. Copyright Push Technology 2012
meh: Fumbling Windows Copyright Push Technology 2012
Lesser Fumbling Windows Copyright Push Technology 2012
Event Windows, tumbling. Copyright Push Technology 2012
eep.js: Sliding Windows init() 1 2 3 4 5 .. .. .. .. x() 1 2 3 4 .. .. .. .. x() 1 2 3 .. .. .. .. x() 1 2 .. .. .. .. t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 ... What is a sliding window? • Like tumbling, except can overlap. But O(N2), Keep N small. • Every event opens a new window. • After N events, every subsequent event emits a result. • Like all windows, cost of calculation ammortized over events Copyright Push Technology 2012
Event Windows, sliding. Copyright Push Technology 2012
eep.js: Periodic Windows x() x() x() x() emit() x() x() x() x() emit() 1 2 3 4 x() x() x() x() emit() 2 3 4 5 init() 2 3 4 5 init() init() t0 t1 t2 t3 ... What is a periodic window? • Driven by ‘wall clock time’ in milliseconds • Not monotonic, natch. Beware of NTP Copyright Push Technology 2012
Event Windows, periodic. Copyright Push Technology 2012
eep.js: Monotonic Windows my my my x() x() x() x() emit() x() x() x() x() emit() 1 2 3 4 x() x() x() x() emit() 2 3 4 5 init() 2 3 4 5 init() init() t0 t1 t2 t3 ... What is a monotonic window? • Driven mad by ‘wall clock time’? Need a logical clock? • No worries. Provide your own clock! Eg. Vector clock Copyright Push Technology 2012
Event Windows, monotonic. Copyright Push Technology 2012
eep.js Embedded Event Processing: • Simple to use. Aggregates Functions and Windowed event processing. • Get it from GitHub/npm soon. Use it. Fork it. • Fast. CEP engines typically handle ~250K/sec. • For small N (most common) is 34x - 200x faster than commercial CEP engines. • But, at a small price. Simple. No multi-dimensional, infinite or predicate windows • Reduces a flood of events into a few in near real time • Can handle 8-40 million events per second (max, on my laptop). YMMV. • Combinators may be added. [Ugh, if I need combinators] Copyright Push Technology 2012
Le Performance? 100 10 Millions Java Tumbling Events Java Sliding 1 per Second 2 4 8 16 32 64 128 256 512 1024 2048 4096 8192 16384 Node Tumbling Node Sliding 0.1 0.01 0.001 Window Size [Fixed] Copyright Push Technology 2012
Performance? In perspective • A 1 producer, 1 consumer lock-free wait-free full duplex queue implementation on a 2.3GHz intel Sandybridge can: • Distribute ~300M events between hyperthreads per second • Distribute ~50M events between two hardware threads on two cores on the same physical die • Distributed ~30M events between two hardware threads on two cores on separate physical dies • You can, with a fully lock-free wait-free system (and you bypass the operating system kernel), maybe, ~1M 1K events/second • There’s no point being capable of > 30M events/second on a thread if you’re going over a wire. • So, 8-40 million events/second in node is a pleasant sufficiency • It’s not the algorithm. It’s the mechanical sympathy, stoopid! • Lock free wait-free concurrency is easier than lock based concurrency. Try it. Copyright Push Technology 2012
• Thank you for listening • Thank you for having me • Thank you Push for the beer budget • Le twitter: @darachennis • Expect eep.js in GitHub soon • I’ll hashtag it #nodedublin • Thank you @Waratek geeks. About me? Copyright Push Technology 2012 Darach@PushTechnology.com

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