Building Scalable Systems: an asynchronous approach

Building Scalable Systems
an 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, & Circonus
I like to tackle problems that are “always on” and “always growing.”

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

2

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 petabytes
as we have for the last several years
you 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


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 architecture
to embrace available technologies.



Implementation is the adaptation of an architecture
to 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.



headroom.
Alice: How many req/second do you need?



headroom.
Bob: 800 req/second would be good.



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



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



headroom.
your goal.
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)



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



layer)
Typical box today:
22x109 instr/s / 25 req/s = 880 MILLION instructions per
request.



layer)
Typical box today:
request.
This same effort (per-request) provided me with approximately
15 minutes enjoying “Might & Magic 2” on my Apple IIe
- you’ve certainly pissed me off.



layer)
Typical box today:
request.
This same effort (per-request) provided me with approximately
15 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 it
i.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
A user signs up ➙
In a dev environment, simulate traffic and rationalize your model



Draw your model out
A user signs up ➙
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



requirements
each isolated service is simpler and safer



requirements
each isolated service is simpler and safer
SCALES.


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” that
your message is placed on the queue and
will eventually be consumed
(assuming a consumer).

• Most systems are ‘more’ asynchronous than that.


Publication what you think happens

User Space Kernel Network Stack Network Stack Queue

publish write
call message frame read

S
A
F
read write E
error message frame
return

call publish write
message frame read

B
O
O
M
read message frame write
error

return


Publication what really happens

User Space Kernel Network Stack Network Stack Queue

call publish
write
return message frame
read

S
A
F
call publish
write E
return message frame
read

B
O
O
M
write

read message frame
error

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

User Space Kernel Network Stack Network Stack Database

publish write
call message frame read

S
A
F
read write E
error message frame
return

call publish write
message frame read

B
O
O
M
read message frame write
error

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


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 reward
shall 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.


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

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