1.
Full slide deck here:
http://bit.ly/ceposta-hardest-part

2.
Twitter: @christianposta
Blog: http://blog.christianposta.com
Email: christian@redhat.com
Christian Posta
Principal Architect – Red Hat
• Author “Microservices for Java Developers”
• Committer/contributor Apache Camel, Apache ActiveMQ,
Fabric8.io, Apache Kafka, Debezium.io, et. al.
• Worked with large Microservices, web-scale, unicorn
company

3.
Free download @ http://developers.redhat.com

4.
People try to copy Netflix, but they can only
copy what they see. They copy the
results, not the process.
Adrian Cockcroft, former Chief Cloud Architect, Netflix

5.
“Microservices” is about optimizing… for speed.

6.
• Maybe it doesn’t matter so much… What
we really care about is speed, reduced
time to value, and business outcomes.
• Maybe a data-driven approach is a better
way to answer this question...
Are you doing microservices?

7.
• Number of features accepted
• % of features completed
• User satisfaction
• Feature Cycle time
• defects discovered after deployment
• customer lifetime value (future profit as a result of relationship with the
customer) https://en.wikipedia.org/wiki/Customer_lifetime_value
• revenue per feature
• mean time to recovery
• % improvement in SLA
• number of changes
• number of user complaints, recommendations, suggestions
• % favorable rating in surveys
• % of users using which features
• % reduction in error rates
• avg number of tx / user
• MANY MORE!
Are you doing microservices?

8.
How does your company go fast?

9.
Manage dependencies.

10.
Data is a major dependency.

11.
Wait. What is data?

12.
What is one “thing”?

13.
Book checkout / purchase Title Search
Recommendations
Weekly reporting

14.
Focus on domain models, not data models
• Break things into smaller,
understandable models
• Surround a model and its
“context” with a boundary
• Implement the model in code
or get a new model
• Explicitly map between
different contexts
• Model transactional
boundaries as aggregates

15.
Aggregates
• Use the domain to lead you to invariant rules across your domain
model
• Model the invariants and their associated entities/value objects as
“aggregates”
• Aggregates focus on transactional boundaries (ie, transactional in
the “A” from ACID sense)
• Individual aggregates are transactionally consistent
• Aggregates use relaxed consistency models between aggregates
(ie, something like the Actor model?)
• Bounded Contexts use relaxed consistency models between
boundaries

16.
Stick with these conveniences as long as you can.
Seriously.

17.
But ...
• Load/size is too great to fit on one box
• Modules/use cases have different read/write
characteristics
• Queries/joins are getting too complex
• Security issues
• Lots of conflicting changes to the model/schema
• Need denormalized, optimized indexing engines
• We can live with eventual consistency (whatever that
really means)

18.
From here on out, what we’re saying is
“thank you old reliable, awesome database…
we’ve got it from here”…

19.
Kinda looks like a combinatorial mess….

20.
“A microservice has its own database”

21.
How do we deal with data in this world?

22.
We need to understand something about the data
inside our services and the data outside our services.
https://msdn.microsoft.com/en-us/library/ms954587.aspx

23.
Data inside a service

24.
Data inside a service

25.
Data outside a service

26.
Data outside a service

27.
Data outside a service

28.
We’re now building a full-fledged distributed system.
Some things to remember…

29.
Plan for failures.
Build concepts of time, delay,
network, and failures into the
design as a first-class citizen.

30.
How do you “read” data and how do you “update” data.

31.
tx.begin()
c = retrieveCustomer()
c.addNewAddress(address)
tx.add(c)
tx.commit()
publishAddressChange(address, c.id)

32.
tx.begin()
c = retrieveCustomer()
c.addNewAddress(address)
tx.add(c)
publishAddressChange(address, c.id)
tx.commit()

33.
Separate reads and writes
(CQRS)

34.
https://secure.phabricator.com/book/phabcontrib/article/n_plus_one/

35.
https://secure.phabricator.com/book/phabcontrib/article/n_plus_one/
getBulkHats()
getBulkHatsForCatsExcept()
wellReallyIJustWantCertainHats()
justExecuteThisSqlForMe()

36.
https://secure.phabricator.com/book/phabcontrib/article/n_plus_one/

37.
For our reads and writes, we need some “consistency”.

38.
What is consistency?
The history of past operations we observe as
a reader of the data

39.
We need reads and writes. But we expect failures. This
is starting to sound like a distributed-systems theorem
I’ve heard…

40.
CAP tells us to pick 2: Consistency, Availability,
Partition Tolerance
CAP is a bad way to think about this.

41.
Linearizable (strict) consistency
CAP - C

42.
Sequential consistency

43.
Monotonic reads consistency

44.
Eventual consistency

45.
Consistency models…
https://en.wikipedia.org/wiki/Consistency_model
• Strict consistency (Linearizability)
• Sequential consistency
• Causal consistency
• Processor consistency
• PRAM consistency (FIFO)
• Bounded staleness consistency
• Monotonic read consistency
• Monotonic write consistency
• Read your writes consistency
• Eventual consistency

46.
Can we really use relaxed consistency models?

47.
Tradeoffs to make with read consistency and
performance

48.
Replicated Data Consistency Explained through Baseball
(Doug Terry)
https://www.microsoft.com/en-us/research/publication/
replicated-data-consistency-explained-through-baseball/
• What consistency model do you need, depending on what
role you’re playing?
• What consistency model are you willing to pay for?
• Official score keeper? (Linearizability or RMW)
• Umpire? (Linearizability)
• Sports writer? (Bounded staleness, Eventual consistency)
• Radio updates? (Monotonic read, Bounded staleness)
• Statistician (Bounded staleness)
• Friends in the pub (Eventual consistency)

49.
Replicated Data Consistency Explained through Baseball
(Doug Terry)
https://www.microsoft.com/en-us/research/publication/
replicated-data-consistency-explained-through-baseball/

50.
Maybe we can use a relaxed consistency model for some
of those previously mentioned use cases…

51.
Example relaxing consistency…

52.
Internet companies created their own tools
for helping with this. (some opensource!!)
• Yelp – MySQL Streamer
https://github.com/Yelp/mysql_streamer
• LinkedIn – Databus
https://github.com/linkedin/databus
• Zendesk – Maxwell
https://github.com/zendesk/maxwell

53.
Meet debezium.io

54.
Meet debezium.io

55.
WePay uses Debezium
https://wecode.wepay.com/posts/streaming-databases-in-realtime-with-mysql-debezium-kafka

56.
Meet debezium.io

57.
Twitter: @christianposta
Blog: http://blog.christianposta.com
Email: christian@redhat.com
Thanks for listening! Time for demo?