The services in a microservice architecture must be loosely coupled and so cannot share database tables. What’s more, two phase commit (a.k.a. a distributed transaction) is not a viable option for modern applications. Consequently, a microservices application must use the Saga pattern, which maintains data consistency using a series of local transactions. In this presentation, you will learn how sagas work and how they differ from traditional transactions. We describe how to use sagas to develop business logic in a microservices application. You will learn effective techniques for orchestrating sagas and how to use messaging for reliability. We will describe the design of a saga framework for Java and show a sample application.

1. @crichardson
Managing data consistency in a
microservice architecture using
Sagas
Chris Richardson
Founder of eventuate.io
Author of Microservices Patterns
Founder of the original CloudFoundry.com
Author of POJOs in Action
@crichardson
chris@chrisrichardson.net
http://eventuate.io http://learn.microservices.io

2. @crichardson
Presentation goal
Distributed data management challenges
in a microservice architecture
Sagas as the transaction model

3. @crichardson
About Chris

4. @crichardson
About Chris
Consultant and trainer
focusing on modern
application architectures
including microservices
(http://www.chrisrichardson.net/)

5. @crichardson
About Chris
Founder of a startup that is creating
an open-source/SaaS platform
that simplifies the development of
transactional microservices
(http://eventuate.io)

6. @crichardson
For more information
http://learn.microservices.io
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7. @crichardson
Agenda
ACID is not an option
Overview of sagas
Coordinating sagas
Countermeasures for data anomalies

8. The microservice architecture
structures
an application as a
set of loosely coupled
services

9. @crichardson
Microservices enable
continuous delivery/deployment
Process:
Continuous delivery/deployment
Organization:
Small, agile, autonomous,
cross functional teams
Architecture:
Microservice architecture
Enables
Enables
Enables
Successful
Software
Development
Services
=
testability
and
deployability
Teams own services

10. @crichardson
Microservice architecture
Browser
Mobile
Device
Store
Front UI
API
Gateway
Customer
Service
Order
Service
…
Service
Customer
Database
Order
Database
…
Database
HTML
REST
REST
Database per service

11. @crichardson
Private database
!=
private database server

12. @crichardson
Loose coupling =
encapsulated data
Order Service Customer Service
Order Database Customer Database
Order table
Customer
table
orderTotal creditLimit

13. @crichardson
How to maintain data
consistency?!?!?
Invariant:
sum(open order.total) <= customer.creditLimit

14. @crichardson
Cannot use ACID transactions
that span services
BEGIN TRANSACTION
…
SELECT ORDER_TOTAL
FROM ORDERS WHERE CUSTOMER_ID = ?
…
SELECT CREDIT_LIMIT
FROM CUSTOMERS WHERE CUSTOMER_ID = ?
…
INSERT INTO ORDERS …
…
COMMIT TRANSACTION
Private to the
Order Service
Private to the
Customer Service
Distributed transactions

15. @crichardson
2PC is not an option
Guarantees consistency
BUT
2PC coordinator is a single point of failure
Chatty: at least O(4n) messages, with retries O(n^2)
Reduced throughput due to locks
Not supported by many NoSQL databases (or message brokers)
CAP theorem 2PC impacts availability
….

16. @crichardson
Basically
Available
Soft state
Eventually consistent
http://queue.acm.org/detail.cfm?id=1394128
ACID

17. @crichardson
Agenda
ACID is not an option
Overview of sagas
Coordinating sagas
Countermeasures for data anomalies

18. @crichardson
From a 1987 paper

19. @crichardson
Saga
Use Sagas instead of 2PC
Distributed transaction
Service A Service B
Service A
Local
transaction
Service B
Local
transaction
Service C
Local
transaction
X Service C

20. @crichardson
Saga step = a transaction
local to a service
Service
Database Message Broker
update publish message/event
Transactional DB: BEGIN … COMMIT
DDD: Aggregate
NoSQL: single ‘record’
How to
make atomic
without 2PC?

21. @crichardson
Order Service
Create Order Saga
Local transaction
Order
state=PENDING
createOrder()
Customer Service
Local transaction
Customer
reserveCredit()
Order Service
Local transaction
Order
state=APPROVED
approve
order()
createOrder() Initiates saga

22. @crichardson
But what about rollback?
BEGIN TRANSACTION
…
UPDATE …
…
INSERT ….
…
…. BUSINESS RULE VIOLATED!!!!
…
ROLLBACK TRANSACTION
Really simple!

23. @crichardson
Rolling back sagas
Use compensating transactions
Developer must write application logic to “rollback” eventually
consistent transactions
Careful design required!

24. @crichardson
Saga: Every Ti has a Ci
T1 T2 …
C1 C2
Compensating transactions
T1 T2 C1
FAILS

25. @crichardson
Order Service
Create Order Saga - rollback
Local transaction
Order
createOrder()
Customer Service
Local transaction
Customer
reserveCredit()
Order Service
Local transaction
Order
reject
order()
createOrder()
FAIL
Insufficient credit

26. @crichardson
Agenda
ACID is not an option
Overview of sagas
Coordinating sagas
Countermeasures for data anomalies

27. @crichardson
How to sequence the saga
transactions?
After the completion of transaction Ti “something” must
decide what step to execute next
Success: which T(i+1) - branching
Failure: C(i - 1)

28. @crichardson
Choreography: distributed decision making
vs.
Orchestration: centralized decision making

29. @crichardson
Option #1: Choreography-based
coordination using events
Order
Service
Customer
Service
Order created
Credit Reserved
Credit Limit Exceeded
Create Order
OR
Customer
creditLimit
creditReservations
Order
state
total
create()
reserveCredit()
approve()/
reject()
Order events channel
Customer events channel

30. @crichardson
Order Service: publishing
domain events
Publish event

31. @crichardson
Customer Service: consuming
domain events
https://github.com/eventuate-tram/eventuate-tram-examples-customers-and-orders
Subscribe to event

32. Benefits and drawbacks of
choreography
Benefits
Simple, especially when
using event sourcing
Participants are loosely
coupled
Drawbacks
Cyclic dependencies -
services listen to each
other’s events
Overloads domain objects,
e.g. Order and Customer
know too much
Events = indirect way to
make something happen
https://github.com/eventuate-examples/eventuate-examples-java-customers-and-orders

33. @crichardson
Order Service
Option #2: Orchestration-based saga
coordination
Local transaction
Order
state=PENDING
createOrder()
Customer Service
Local transaction
Customer
reserveCredit()
Order Service
Local transaction
Order
state=APPROVED
approve
order()
createOrder()
CreateOrderSaga
InvokesInvokesInvokes

34. @crichardson
A saga (orchestrator)
is a persistent object
that
tracks the state of the saga
and
invokes the participants

35. Saga behavior
On create:
Invokes a saga
participant
Wait for a reply
On reply:
Determine which saga
participant to invoke next
Invokes saga participant
Updates its state
Wait for a reply
…

36. @crichardson
Order Service
CreateOrderSaga orchestrator
Customer Service
Create Order
Customer
creditLimit
creditReservations
...
Order
state
total…
reserveCredit()
CreateOrder
Saga
OrderService
create()
create()
approve()
creditReserved()

37. @crichardson
CreateOrderSaga definition
Sequence of
steps
step = (Ti, Ci)
Build command
to send
Saga’s Data

38. @crichardson
Customer Service command
handler Route command
to handler
Reserve
credit
Make reply message

39. @crichardson
Eventuate Tram Sagas
Open-source Saga orchestration framework
Currently for Java
https://github.com/eventuate-tram/eventuate-tram-sagas
https://github.com/eventuate-tram/eventuate-tram-sagas-
examples-customers-and-orders

40. @crichardson
Use asynchronous messaging
Ensures sagas complete when
participants are temporarily unavailable

41. @crichardson
Create Order Saga -
messaging
Order Service
Create
Order
Saga
Message
Broker
Customer
Service
Customer
Reserve
Credit
Reserve
Credit
Reply
Customer Request
Channel
Saga Reply
Channel
Order Request
Channel
Approve
Order

42. Benefits and drawbacks of
orchestration
Benefits
Centralized coordination
logic is easier to understand
Reduced coupling, e.g.
Customer knows less
Reduces cyclic
dependencies
Drawbacks
Risk of smart sagas
directing dumb services

43. @crichardson
Agenda
ACID is not an option
Overview of sagas
Coordinating sagas
Countermeasures for data anomalies

44. @crichardson
Lack of isolation complicates business
logic
Order Service
Local transaction
Order
state=PENDING
createOrder()
Customer Service
Local transaction
Customer
reserveCredit()
Order Service
Local transaction
cancelOrder()
?
Time

45. @crichardson
How to cancel a PENDING
Order?
Don’t throw an OrderNotCancellableException
Questionable user experience
“Interrupt” the Create Order saga?
Cancel Order Saga: set order.state = CANCELLED
Causes Create Order Saga to rollback
But is that enough to cancel the order?
Cancel Order saga waits for the Create Order saga to complete?
Suspiciously like a distributed lock
But perhaps that is ok

46. @crichardson
Countermeasure Transaction
Model
http://bit.ly/semantic-acid-ctm - paywall 😥

47. @crichardson
Sagas are ACD
Atomicity
Saga implementation ensures that all transactions are
executed OR all are compensated
Consistency
Referential integrity within a service handled by local databases
Referential integrity across services handled by application
Durability
Durability handled by local databases

48. @crichardson
Lack of I anomalies

49. @crichardson
Anomaly: Lost update
Ti: Create
Order
Tj: Approve
Order
Ti: Cancel
Order
Saga 1
Saga 2
Time
Overwrites
cancelled order

50. @crichardson
Anomaly: Dirty reads
Ti: Reserve
Credit
Ci: Unreserve
credit
Ti: Reserve
Credit
Reads
uncommitted
changes
Saga 1
Saga 2
Order rejected unnecessarily OR Credit limit exceeded
Time

51. @crichardson
Anomaly: non-repeatable/
fuzzy read
Ti: Begin
Revise Order
Tj: Finalize
Revise Order
Ti: Update
Order
Time
Order has changed
since Ti
Saga 1
Saga 2

52. @crichardson
Countermeasures for
reducing impact
of isolation anomalies…
*
i.e. good enough, eventually consistent application

53. Saga structure
T1 C1
…
T2 C2
Tn+1
Tn+2
….
Compensatable transactions
Pivot transaction = GO/NO GO
Retriable transactions that can’t fail

54. Countermeasure: Semantic
lock
Compensatable transaction sets flag,
retriable transaction releases it
Indicates a possible dirty read
Flag = lock:
prevents other transactions from
accessing it
Require deadlock detection, e.g.
timeout
Flag = warning - treat the data
differently, e.g.
Order.state = PENDING
a pending deposit
… …
…
Approve Order
Create Pending
Order
Reject Order
Order.state =
PENDING
Order.state =
REJECTED
Order.state =
APPROVED

55. @crichardson
Countermeasure:
Commutative updates
Commutative: g(f(x)) = f(g(x))
For example:
Account.debit() compensates for Account.credit()
Account.credit() compensates for Account.debit()
Avoids lost updates

56. @crichardson
Countermeasure: Pessimistic
view
Increase avail.
credit
Reduce avail.
credit
Cancel Order Delivery
Cancel Order
Reorder saga
to
reduce risk
Cancel Order Delivery
Cancel Order
Increase avail. credit
… …… …
Won’t be compensated,
so no dirty read

57. @crichardson
Countermeasure: Re-read
value
Ti: Read
Order
Tj: Re-read, then
update Order
Ti: Update
Order
Time
Saga 1
Saga 2
Verify unchanged, possibly restart
Prevents lost update
~Offline optimistic lock pattern

58. @crichardson
Countermeasure: version file
Ti: Create
Order
Tj: Authorize
Credit Card
Ti: Cancel
Order
Time
Saga 1
Saga 2
Tj: Reserve
Credit
Tj: Reverse
Credit Card
Payment
1. reverse()
2. authorize()
“log” of changes:
Enables operations to commute

59. @crichardson
Countermeasure: By value
Business risk determines strategy
Low risk => semantic ACID
High risk => use 2PC/distributed transaction

60. @crichardson
Summary
Microservices tackle complexity and accelerate development
Database per service is essential for loose coupling
Use orchestration-based or choreography-based sagas to
maintain data consistency across services
Use countermeasures to reduce impact of anomalies caused
by lack of isolation

61. @crichardson
@crichardson chris@chrisrichardson.net
http://learn.microservices.io
Questions?
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