We all know, that software development can not be learned by following checklists and that writing distributed systems is not easy. But wouldn't it be nice, if you had a checklist of the most important things to keep in mind when you start building your own microservices project? This is what my talk is about. I presented it at codetalks 2016 in Hamburg, Germany. (YouTube video of the talk will follow soon). No technical details about how to use Kafka or which cloud manager is the best. Just some general principles that will help you to focus on the right things from the beginning. You don't have to agree on all principles, but I am pretty sure that if you just focus on 80% you will be better off. Feedback and comments are very welcome so this list can grow (and maybe even shrink) based on new project experiences.

1. The 36th Chamber
of Shaolin
Improve Your Microservices Kung Fu
in 36 Easy Steps
freiheit.com technologies - Hamburg, September 29, 2016
code.talks 2016 - Keynote Track

2. This talk derives from more
than 17+ years of experience
building large-scale Internet
systems.
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3. Internally we are calling these
principles BLACK & WHITE.
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4. Black & White principles are
principles that are true most
of the time.
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5. This talk is about
Microservices.
But as you will see, most of
these principles are true for all
kinds of systems.
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6. Don’t build your own cloud
or cluster management.
People with far more experience have already done this.
Use what is given.
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7. Don’t build your own data
center and availability zone
failover.
Again: People with far more experience have already
done this. It is difficult. Use what is given.
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8. Don’t roll your own service
discovery.
People with far more experience … - you know the spiel.
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9. One container. One service.
This is how you should use Docker.
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10. I am a big fan of Kubernetes
running on GCE or AWS.
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11. A microservice provides a set
of remote procedure calls that
are closely related to each
other (high cohesion).
● It should be as small as needed.
● It should do only one thing and do it well.
● It should do 'one thing' entirely.
● It's better to split later than to build it too small at first.
● A split should be simple.
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12. Service API
Receives requests
Returns responses
No state,
no session,
“non-sticky”
A MICROSERVICE
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13. A microservice starts up fast.
Start-up time < 10 sec.
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14. A microservice has a low
memory footprint.
It doesn’t cache large amounts of data.
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15. A microservice has well
defined resource constraints.
Memory/heap limits, CPU limits, swap disabled
(or constrained), etc.
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16. A microservice has a fast
response time.
Like 15ms/50th, 50ms/95th and 100ms/99th percentile.
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17. A microservice has a low
error rates.
There will be some 404, 500, 503, etc. But not many. And
you should know and understand why they are happening.
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18. A microservice differentiates
between technical errors and
usage errors.
Technical is like out of memory, usage is like receiving
wrong requests from clients the service is unable to reply
and therefore denies to answer.
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19. A microservice must be
monitored at all times.
Monitor response time, request count and errors.
Define thresholds and alerts when thresholds are reached.
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21. You must be able to trace a
request from the client down
through all services calls.
Each request needs to have a unique ID.
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22. A microservice never shares
its persistent state (database)
with any other microservice.
Exception: Importer service writes to database.
Online service reads from / writes to database.
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23. It is totally okay to use a
relational database.
Don’t shoot yourself in the foot with Cassandra and NoSQL.
Rather use really flat data models, no joins, no complicated
queries in a relational database.
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24. A microservice should never
include both batch and online
processing at the same time.
Separate batch from online processing to make sure that
your online services aren’t getting slow when the batch
processing starts.
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25. Microservices will and can
call other microservices.
Call as few other microservices as possible.
Prevent circular calls.
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26. A microservice answers
requests asynchronously.
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27. One thread
handling
requests fast,
handing off to
worker threads
Receives requests
Returns responses
Many worker
threads running in
the background
ASYNC REQUEST HANDLING
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28. A microservice sends requests
to other microservices
asynchronously.
Take a look at reactive programming Rx* or other stuff ...
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29. ASYNC CALLING OTHER SERVICES
Async
Async Async
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30. If a downstream service fails
your service should still work.
Use graceful degradation to return useful responses
as long as possible.
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31. If you use REST,
use it asynchronously.
This doesn’t mean “message queues”.
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32. It is better to use specialized
RPC protocols instead of
REST that can handle
interface changes better.
Like gRPC, Thrift, Finagle.
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33. Each request needs a
reasonable time-out and
must be cancelable.
Like with gRPC/Context.
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34. All asynchronous downstream
work resulting from a request
must be cancelable, too.
Like with gRPC/Context.
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35. A request must be repeatable
providing the same result.
Requests must be idempotent.
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36. Log the right amount of data
and create actionable,
meaningful log entries.
Or you will drown in your logs with unusable information.
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37. If you need two or more
requests to succeed, you need
to implement retry/rollback on
error.
This is a distributed system. It can fail anytime.
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38. No branches. All new code is
in the HEAD branch.
Update your microservices
with very small commits.
Few large commits are more unpredictable than lots of
small commits that are pushed continuously to your cluster.
Your monitoring and alerting will protect you.
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39. You must be able to identify
which version of your code is
currently running in production.
You can use the commit rev as a Docker release tag.
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40. Design from the beginning
how to run integration tests.
How to run a test over dozens or hundreds of services,
all having separate databases?
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41. Share only code where a
change doesn’t force
everybody to update all their
services.
Exception: Crypto, security-related code, etc.
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42. No commit without tests.
The “good feelings” start at code coverage > 85 %.
70 % unit tests, 20 % integration tests, 10 % end-to-end tests.
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43. Actively manage your
microservices’ lifecycle.
Handle SIGTERM, readiness & lifeness probes, etc.
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44. Deploy new versions to a
canary context first.
In Kubernetes you can use namespaces to separate
contexts. Measure and compare key KPIs with your
production context.
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45. Backend-for-frontend helps
you to reduce the number of
requests and bandwidth
consumption of your clients.
Fewer requests. The least amount of data transferred.
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46. Simulate real-world traffic
for load testing.
One option is to record live traffic for replay.
See https://github.com/buger/gor.
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47. Run a chaos monkey in
production.
It randomly kills services and you will learn if everything is
really under control.
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48. DevOps doesn’t work if it
gets big.
You need dedicated Site Reliability Engineers (SREs) to
team up with your software engineers.
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49. I lied. There were
more than 36 steps ...
Thank you.
freiheit.com technologies gmbh 2016
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