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MicroCPH - Managing data consistency in a microservice architecture using Sagas

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The services in a microservice architecture must be loosely coupled and so cannot share database tables. What’s more, two-phase commit (aka 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.

Published in: Software

MicroCPH - Managing data consistency in a microservice architecture using Sagas

  1. 1. @crichardson Managing data consistency in a microservice architecture using Sagas Chris Richardson Founder of Eventuate.io Founder of the original CloudFoundry.com Author of Microservices Patterns and POJOs in Action @crichardson chris@chrisrichardson.net http://learn.microservices.io Copyright © 2019. Chris Richardson Consulting, Inc. All rights reserved
  2. 2. @crichardson Presentation goal ACID transactions within services Sagas between services
  3. 3. @crichardson About Chris http://learn.microservices.io
  4. 4. @crichardson About Chris https://microservices.io/book 40% discount with code ctwmicrocph19
  5. 5. @crichardson Agenda ACID is not an option Overview of sagas Coordinating sagas
  6. 6. The microservice architecture structures an application as a set of loosely coupled services
  7. 7. @crichardson Loose coupling = encapsulated data Order Service Customer Service Order Database Customer Database Order table Customer table orderTotal creditLimit availableCredit Change schema without coordinating with other teams
  8. 8. @crichardson ACID transactions within services are fine Order Service Customer Service Order Database Customer Database Order table Customer table orderTotal creditLimit availableCredit ACID transaction ACID transaction
  9. 9. @crichardson But how to maintain data consistency across services? Order Service Customer Service Order Database Customer Database Order table Customer table orderTotal creditLimit availableCredit ?
  10. 10. @crichardson Enforcing the customer’s credit limit createOrder(customerId, orderTotal) Pre-conditions: • customerId is valid Post-conditions • Order was created • Customer.availableCredit -= orderTotal Customer class Invariant: availableCredit >= 0 availableCredit <= creditLimit Spans services
  11. 11. @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 = ? … … CHECK FOR SUFFICIENT CREDIT…. … INSERT INTO ORDERS … … COMMIT TRANSACTION Private to the Order Service Private to the Customer Service Distributed transactions
  12. 12. @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 ….
  13. 13. @crichardson Basically Available Soft state Eventually consistent http://queue.acm.org/detail.cfm?id=1394128 ACID
  14. 14. @crichardson Agenda ACID is not an option Overview of sagas Coordinating sagas
  15. 15. @crichardson From a 1987 paper
  16. 16. @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
  17. 17. @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
  18. 18. @crichardson Seems simple but …
  19. 19. @crichardson Challenge: rollback BEGIN TRANSACTION … UPDATE … … INSERT …. … …. BUSINESS RULE VIOLATED!!!! … ROLLBACK TRANSACTION Really simple ACID transaction! Create Order Reserve Credit FAILS Already committed. How to undo?! VS.
  20. 20. @crichardson Rolling back sagas Use compensating transactions Developer must write application logic to “rollback” eventually consistent transactions Careful design required!
  21. 21. @crichardson Saga: Every Ti has a Ci T1 T2 … C1 C2 Compensating transactions T1 T2 C1 FAILS
  22. 22. @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
  23. 23. @crichardson Writing compensating transactions isn’t always easy Write them so they will always succeed If a compensating transaction fails no clear way to recover Challenge: Undoing changes when data has already been changed by a different transaction/saga More on this later Actions that can’t be undone, e.g. sending email Reorder saga and move to end
  24. 24. Structure of Create Order Saga T1: Create Order C1: Reject Order T2: Reserve Credit T3: Approve Order Needs to be compensatable because Reserve Credit can fail Last step that can fail: Does not need compensation Can’t fail
  25. 25. Saga structure: 3 phases T1 C1 … T2 C2 Tn+1 Tn+2 …. Compensatable transactions Pivot transaction = GO/NO GO Retriable transactions that can’t fail and don’t need compensation Great for actions that cannot be undone
  26. 26. Ordering the steps of a saga Dependencies between steps: Result of one step is used as input to next e.g. Create Order before Reserving Credit But often you have a choice Simplify by moving updates to after pivot transaction Update Customer Undo Update Customer … Pivot Transaction Update Customer … Pivot Transaction No compensation required Reorder
  27. 27. @crichardson Challenge: 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
  28. 28. @crichardson Lack of I Outcome of concurrent execution != a sequential execution = Data anomalies
  29. 29. @crichardson Anomaly: Lost update Ti: Create Order Tj: Approve Order Ti: Cancel Order Saga 1 Create Order Saga 2 Cancel order Time Overwrites cancelled order Also: A compensating transaction that simply restores previous value can cause a lost update
  30. 30. @crichardson Anomaly: Dirty reads Ti: Reserve Credit Ci: Unreserve credit Ti: Reserve Credit Reads uncommitted changes Order rejected unnecessarily OR Credit limit exceeded Time Saga 1 Create Order Saga 2 Create Order Ti: Reserve InventoryX Temporarily changed
  31. 31. @crichardson Use countermeasures to prevent anomalies Use semantic lock to prevent lost updates e.g. a PENDING order cannot be cancelled Reorder saga to eliminate dirty reads caused by compensating transactions Ci undoes changes made by Ti => possibility of dirty read Eliminate Ci by moving update to (after) pivot transaction Use commutative updates to eliminate lost updates caused by compensating transactions Customer.reserveCredit() and releaseCredit() Account.debit() and credit() … https://www.slideshare.net/chris.e.richardson/saturn-2018-managing-data-consistency-in-a-microservice-architecture-using-sagas
  32. 32. @crichardson Agenda ACID is not an option Overview of sagas Coordinating sagas
  33. 33. @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)
  34. 34. @crichardson Use asynchronous, broker- based messaging Order Service Customer Service …. Message broker Guaranteed atleast once delivery ensures a saga complete when its participants are temporarily unavailable
  35. 35. @crichardson Saga step = a transaction local to a service (a.k.a. participant) Service Database Message Broker update Send message/event How to make atomic without 2PC?* *Transactional Outbox pattern or Event Sourcing
  36. 36. @crichardson Choreography: distributed decision making vs. Orchestration: centralized decision making
  37. 37. @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
  38. 38. @crichardson Order Service: publishing domain events Publish event Create order
  39. 39. @crichardson Customer Service: consuming domain events… https://github.com/eventuate-tram/eventuate-tram-examples-customers-and-orders Subscribe to event
  40. 40. @crichardson Customer Service: consuming domain events Attempt to Reserve credit Publish event on success Publish event on failure
  41. 41. Benefits and drawbacks of choreography Benefits Simple, especially when using event sourcing Participants are loosely coupled Drawbacks Decentralized implementation - potentially difficult to understand Cyclic dependencies - services listen to each other’s events, e.g. Customer Service must know about all Order events that affect credit 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
  42. 42. @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
  43. 43. @crichardson A saga (orchestrator) is a persistent object that implements a state machine that invokes the participants and handles replies
  44. 44. Saga orchestrator behavior On create: Invokes a saga participant Persists state in database Wait for a reply On reply: Load state from database Determine which saga participant to invoke next Invokes saga participant Updates its state Persists updated state Wait for a reply …
  45. 45. @crichardson Order Service CreateOrderSaga orchestrator Customer Service Create Order Customer creditLimit creditReservations ... Order state total… reserveCredit CreateOrder Saga OrderService create() create() approve() Credit Reserved Customer command channel Saga reply channel https://github.com/eventuate-tram/eventuate-tram-sagas-examples-customers-and-orders
  46. 46. @crichardson CreateOrderSaga definition Sequence of steps step = (Ti, Ci) Saga’s Data
  47. 47. @crichardson Customer Service command handler Route command to handler Reserve credit Make reply message
  48. 48. @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
  49. 49. Benefits and drawbacks of orchestration Benefits Centralized coordination logic is easier to understand Current state is in database Reduced coupling, e.g. Customer knows less Reduces cyclic dependencies Drawbacks Risk of smart sagas directing dumb services
  50. 50. @crichardson Summary Microservices tackle complexity and accelerate development Database per service is essential for loose coupling Use ACID transactions within services 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
  51. 51. @crichardson @crichardson chris@chrisrichardson.net http://learn.microservices.io Questions? 40% discount with code ctwmicrocph19

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