building scalable systems: an asynchronous approach

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Building Scalable Systemsan Asynchronous Approach

pleasure and pain

Monday, June 20, 2011

Who am I? @postwait on twitter

Author of “Scalable Internet Architectures”Pearson, ISBN: 067232699X

Contributor to “Web Operations”O’Reilly, ISBN:

Founder of OmniTI, Message Systems, Fontdeck, & CirconusI like to tackle problems that are “always on” and “always growing.”

I am an EngineerA practitioner of academic computing.IEEE member and Senior ACM member.On the Editorial Board of ACM’s Queue magazine.

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Some rants about systems.

cutting through the crap


BIG data

• BIG data: doesn’t exist

• but it sure is a good way to market things.

• If you measure things in petabytesas we have for the last several yearsyou have data, not BIG data.


Data stores

• “new” NoSQL systems can scale better than an RDBMS

• yes

• They scale better than sharded RDBMS

• no

• I need NoSQL systems for “BIG” data.

• no


Why NoSQL

• The choice to use a NoSQL system is driven by:

• a more suitable data model

• we desire to shift our CAP theorem constraints


The cloud

• The cloud is not magic.

• The cloud enables engineers to rapidly deploy an architecture to run an application.

• There is nothing wrong with this.


The cloud: gotcha 1

• Provisioning services “the old way” took time due to:

• installing systems

• install packages

• determining and enforcing data security

• determining and enforcing SLA monitoring

• determining and enforcing DR: RPO and RTO

• documenting escalation and remediation efforts


The cloud: gotcha 2

• Quality of service in multi-tenancy environments is a very, very hard problem.

• In fact, no one has solved it.


The cloud: gotcha 3

• Platform as a service provides:

• MySQL? PostgreSQL? Redis? Node.js?

• .. patched.

• .. patched.

• .. patched.

• .. patched.


The cloud: gotcha 4

• Scaling isn’t magic pixie dust.

• “I can run more instances of my app.”

• horribly false sense of security.

• You must have a scalable architecture


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Design & Implementation Techniques

some say architecture != implementation


Architecture vs. Implementation



Architecture is without specification of the vendor,make, and model of components.




Implementation is the adaptation of an architectureto embrace available technologies.




Implementation is the adaptation of an architectureto embrace available technologies.

They are intrinsically tied.Insisting on separation is a metaphysical argument(with no winners)


Respect Engineering Math

Engineering math:

19 + 89 = 110

“Precise” Math:

19 + 89 = 10.8


Respect Engineering Math

Engineering math:

19 + 89 = 110

“Precise” Math:

19 + 89 = 10.8

Ok. Ok. I must have, I must have put a decimal point in the wrong place or something. Shit. I always do that. I always mess up some mundane detail.

- Michael Bolton in Office Space


Ensure the gods aren’t angry.



Bob: We need to grow our cluster of web servers.




Alice: How many requests per second do they do, how many do you have and what is their current resource utilization?





Bob: About 200 req/second, 8 servers and they have no headroom.






Alice: How many req/second do you need?







Bob: 800 req/second would be good.








Alice: Um, Bob, 200 x 8 = 1600... you have 50% headroom on your goal.









Bob: No... 200 / 8 = 25 req/second per server.









Bob: No... 200 / 8 = 25 req/second per server.

Alice: Bob... the gods are angry.


Why you’ve pissed off the gods.



Most web apps are CPU bound (as I/O happens on a different layer)




Typical box today: 8 cores are 2.8GHz or 22.4 BILLION instructions per second.





22x109 instr/s / 25 req/s = 880 MILLION instructions per request.






This same effort (per-request) provided me with approximately15 minutes enjoying “Might & Magic 2” on my Apple IIe- you’ve certainly pissed me off.






This same effort (per-request) provided me with approximately15 minutes enjoying “Might & Magic 2” on my Apple IIe- you’ve certainly pissed me off.

No wonder the gods are angry.


Develop a model


Develop a model

Queue theoretic models are for “other people.”


Develop a model


Sorta, not really.


Develop a model


Sorta, not really.

Problems:


Develop a model


Sorta, not really.

Problems:

very hard to develop a complete and accurate model


Develop a model


Sorta, not really.

Problems:


Benefits:


Develop a model


Sorta, not really.

Problems:


Benefits:

provides insight on architecture capacitance dependencies


Develop a model


Sorta, not really.

Problems:


Benefits:


relatively easy to understand


Develop a model


Sorta, not really.

Problems:


Benefits:


relatively easy to understand

illustrates opportunities to further isolate work


Rationalize your model



Draw your model out



Draw your model out

Take measurements and walk through the model to rationalize iti.e. prove it to be empirically correct



Draw your model out


You should be able to map actions to consequences:



Draw your model out



A user signs up ➙ 4 synchronous DB inserts (1 synch IOPS + 4 asynch writes) 1 AMQP durable, persistent message 1 asynch DB read ➙ 1/10 IOPS writing new Lucene indexes



Draw your model out




In a dev environment, simulate traffic and rationalize your model



Draw your model out




In a dev environment, simulate traffic and rationalize your model

I call this a “data flow causality map”


Complexity will eat your lunch



there will always be empirical variance from your model




explaining the phantoms leads to enlightenment





service decoupling in complex systems gives:






simplified modeling and capacity planning







slight inefficiencies








promotes lower contention









requires design of systems with less coherency requirements










each isolated service is simpler and safer










each isolated service is simpler and safer

SCALES.


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Asynchronous Systems

it’s likely you have no idea what you’re doing


Asychronous

• of or requiring a form of computer control timing protocol in which a specific operation begins upon receipt of an indication (signal) that the preceding operation has been completed.

• ...or “I’ll act when you tell me you are done”

• ...or a protocol wherein the initiation of a task and the report of its completion are separate operations.


Protocols

• Standards:

• AMQP(impl: ActiveMQ, RabbitMQ, OpenAMQ, etc.)

• Others:

• ZeroMQ

• Gearman


Guarantees

• Queueing protocols can be misleading.

• Are you sure you did what you think you did?

• Let’s use a publish as an example.


Publication

• Imagine a Queue:

• You assume that by calling “publish” thatyour message is placed on the queue andwill eventually be consumed(assuming a consumer).

• Most systems are ‘more’ asynchronous than that.


Publication what you think happens

Kernel Network Stack Network Stack Queue

message framewrite

User Space

publish

SAFE

read

message framewritepublish read

BOOMwritemessage framereaderror

call

return

writemessage framereaderror

call

return


Publication what really happens

Kernel Network Stack Network Stack Queue

message framewrite

User Space

publish

SAFE

read

message framewritepublish

read

BOOMwrite

message framereaderror

call

return

call

return

return


Why?

• Why do queueing protocols use

“silence for success?”

• Simple: performance

• no need for a roundtrip before the next message

• success is common, failure rare


Why?

• AMQP is not alone in this...

• 0MQ as well.


Now what?

• In each component you must decide if you need:

• synchronous system w/ synchronous protocol

• asynchronous system w/ synchronous protocol

• asynchronous system w/ asynchronous protocol


Service decisions

• Knowing you can lose messages is... okay?

• it can be

• there are plenty of uses for unreliable communications

• however... generally,it is much easier to build services that have end-to-end guarantees.


Non-asynchronous: synchronous

Kernel Network Stack Network Stack Database

message framewrite

User Space

publish

SAFE

read

message framewritepublish read

BOOMwritemessage framereaderror

call

return

writemessage framereaderror

call

return


Asynchronous to the purpose

• Why is a Queue “asynchronous”

• and a Database “synchronous”

• I lied... “asynchronous” is “to the purpose.”

• If the ultimate, final goal is: storage in a DB

• and you return the result only after a commit

• then you are synchronous


Simple example: image thumbnailing

• A user uploads an email to a web site

• you need to produce 7 different transformations

• (size, color, etc.)

• Asynchronous system:

• synchronous upload protocol:

• user upload -> thank you we have it

• asynchronous processing

• file -> 7 mutations


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A (more) complete example.

foursquare-like, untappd.com-like service


Better example: rewards calculation

• A user performs an action on your site

• and you need to reward them based on:

• social network, history, value

• you want to show them their reward “immediately.”

• Step 1: engineer for failure.


Rewards calculation: step 1

• the inability to calculate the rewardshall not prevent the action.(think: beer checkin on untappd)

• I want the reward calculation immediately.

• I need the checkin to be recorded.



• Decouple the rewards calculation:

1. receive user request

2. store(C)

3. queue the checkin(C) on QC

4. wait up to 500ms (reading rewards R from QR)

5. return R witnessed.



• Decouple the rewards calculation:

1. dequeue checkin: C from QC

2. calculate rewards(C) -> R

3. store(R)

4. queue(R) on QR


Rewards calculation: win

• You win big.

• If the rewards calculation system is

• too slow, or

• goes offline

• checkins still proceed and

• responses are served within 500ms

• You have decoupled the service availability requirements of the checkin system from the rewards system: happier users.


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Final random thoughts

think outside of the box


Things to look at: free your mind

• Node.js

• Javascript? Seriously?

• Yes.

• Forces you to think asynchronously

• Forces you to share nothing

• Forces you to build stateless systems

• These systems scale


unsafe: when to use

• “silence is success” messaging is almost always useful when new, more temporally relevant data is bound to arrive.

• game location data

• performance data

• status data

• the casual observer


be mindful

• Always monitor:

• message rates

• queue depths

• queue counts

• connection concurrency


Thank you.

• Thank you

• Merci beaucoup.


building scalable systems: an asynchronous approach

Technology

cluster of web servers

gods arent angry

big data big data

data security

current resource utilization

scalable systems

web operationsoreilly

suitable data model