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A little about myself
● Sean Kellyo Also known as Stabby
● I went from .NET to Ruby to Goo But my favorite language is SQL
● Core maintainer of Tapjoys:o Chore - https://github.com/Tapjoy/choreo Dynamiq - https://github.com/Tapjoy/dynamiq
● I love IPAs
Speaking of Tapjoy...
We do…● 1.8 Billion Requests per minute
o And almost as many messages per day● ~170 Million Jobs per day● All on ~750 EC2 instances and private
servers● A stocked double-kegerator
o Right now: Pinner IPA / Cisco Summer of Lager
Messaging is...
● A way to share important events, without needing to know who's listening
● A way to handle processing events and information at a larger scale
● Not all that unlike “background jobs”o Jobs: “I’ll do this later”o Messaging: “Other people will do this later”
Messaging and You
Now you have several services, and they all need to share info
Monolith1.5
Jobs
One-off which becomes a core part of
your business
Jobs
Failed attempt at
Micro Service
Reporting System
Sure, but how can you actually use Messaging?
Those weren’t even very good drawings
They didn’t have lines or anything
What types of Messaging are there?● 1:1, traditional “Queueing”
o Basic push / pull model of doing worko Common with asynchronous job processingo RabbitMQ, ActiveMQ, SQS, Disqueue, Dynamiq, NSQ
● Fanouto Broadcast style publishing, all listeners get a copyo Ex: A game pushing out notifications of an updateo Most technologies with 1:1 queues support this in some way
What types of Messaging are there?● Routing
o Intelligent fanout, routes to listeners based on message metadata
o Newsgroups: Subscribe to food.charcuterie.*, get bressolao RabbitMQ does this pretty well
● Streamingo Persistent connection, constant source of raw byteso Twitter's Firehose is one exampleo Kafka is a current popular choiceo Really popular with the Scala / Spark crowd
OK, so my Apps and Services need to talk
Can’t I just stick it all in a shared database and be done with it?
Why not just stick it all in a DB?
● You can some share of your data this wayo Depends on the use case, type of informationo This is outside the scope of this talk
● Databases are not designed for delivering messages
o Any “queue” tables will be ridiculously contendedo No atomic “pull” options
At Tapjoy, we use...● RabbitMQ
o Moves analytics events to reporting endpoints by way of complex filesystem / s3 approacho Single node with sharded queueso Rabbit HA cannot handle our scale
● SNS / SQSo SNS in some newer projects, mostly for fanouto SQS for all traditional background jobs
● Kinesiso Pilot integration for a new analytics pipelineo Being supplanted with Kafka
● Kafkao New analytics pipelineo Used to distribute metrics to both the new endpoint as well as the existing one for
verification● Dynamiq
o Inhouse Open Source SNS/SQS-alike built on top of Riak 2.0o Currently used to circumvent complicated and slow legacy messaging service
I’m not really here to tell you what to pick, either!
I’d rather talk to you about how to pick, and how you integrate your choices
Distributed Systems are all about tradeoffs
Ask: What are my actual needs?
● Planning for 2 years down the road is smarto But solutions right now get shit doneo Include a cost projection with scale estimates
● Build a prototype (or two)o Try to iterate quicklyo Understand how you’d use whatever you chooseo Don’t be afraid to move ono Look at multiple client libraries
Look for: Docs, Active repos, Idiomatic
Ask: What is my latency tolerance?
● Publishing Messageso How much time can your app tolerate for publishing?o What does publish latency look like during an issue?o Consider the worst-case scenario when planning
● Consuming Messageso Can you run multiple consumers without impacting
the service?● End to End
o How fast is the whole experience, round trip?
Ask: What level of durability?
● Cliento Batched VS Unbatched / Streamingo Acknowledged writes
● Servero Messages held in memory VS disko Messages highly-available?o Recover from network partitions safely?o At-Most-Once VS At-Least-Once
Exactly-Once is something of a myth
Ask: What about throughput?
● How many producing clients do you have● How many messages per second will they submit
o Does message size impact performance?● What size should the cluster be?
o Super cluster VS specialized clusters● How many consumers it takes to keep pace
o With room to grow
Ask: What does failure mean?
● What does a message publishing error mean?
● What does a delay in the processing pipeline mean?
● What does a “lost” or failed message mean?● What does a total failure of the messaging
system mean?
Ask: What behavior do I want?
Is it…● CA?
o Not distributed, will be difficult to scale past 1 boxo Traditional RDBMS systems are typically CA
● CP?o Good if you need strongly consistent datao Partitions can cause data unavailability
● AP?o Good if you need complete availabilityo Eventual consistency can often be “good enough”
Do you have...
● Relatively small (< 256kb) message sizes?● Not so strict (~50ms) latency requirements?● Throughput on the order of 100m or less per
month?● A tolerance or capability to handle the
occasional duplicate message?● No concern around being locked into a
vendor-specific technology?
Go use SNS and SQS immediately
Leave here now and just do itIt’s easy, it’s cheap (at that scale), and you
don’t need to maintain it
You don’t have to choose just 1
● It’s a falsehood that you need 1 perfect technology
o Each has strengths, weaknesses, and ideal use cases
● Don’t be afraid to use something elseo If you’re lucky, your app lives long enough to see
many different infrastructure needs
Avoid direct implementations
● Wrap the notion of Publishing in an interfaceo Most technologies look surprisingly similar to publisho You can wrap this in a simple interface, and switch
implementations as needed● Consuming is usually unique per technology
o Just write a new oneo Trying to interface this part is probably more trouble
than it’s wortho Play to the unique strengths of the technology
Interfacing your Messaging choices● Sending messages is often as simple as a name and a chunk of
datao Define a simple interface for pushing arbitrary data towards a
named endpointo A name and a string of JSON is usually enough to get goingo At Tapjoy, we use our Chore library to handle abstracting
message publishing from messaging technologies● Destinations are independent from messages
o You could need to switch sending messages to a new technology
o You could have 2 or more different systems depending on the information in a given message
How do I change messages safely?● Wrap messages in a simple envelope
o Keep metadata about the message distinct from metadata about the event it describes
● Define schemas for message bodieso Schemas give you a catalogue of message definitions, and the
ability to version themo At Tapjoy, we use our TOLL to build endpoint-agnostic clients
based on schemas, and register them to use Chore publishers.● Consumers need older schemas
o Lets them reason about how to handle older messageso Keep a backlog of N older versions, drop support for > N
Keep in mind
● Distributed Systems - all about tradeoffso Never trade “P”
● Understand your needso Latency, Throughput, Availability, Durability
● Understand how it fits into your architecture● Interfaces are your friend
o They can give you a lot of flexibility
Keep in mind
● Use schemas and versioning to support changes to messages themselves
● Just pick somethingo Build a prototype, or two (or three)o Your second try will probably go bettero SNS/SQS is a decent choice, if latency isn’t a
concern● Tapjoy is a great place to work on these kinds of
problems at huge scale
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