It is a story about a brave team who took the decision of taking over the parallel-run pattern among others and afforded to introduce and handle a multi-master system as a temporary step in a refactoring process.

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Whoʼs speaker?

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Whoʼs esurance?
Distribution platform for insurance products and services

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Why are we doing refactoring?
Migration from e-brokerage business to the distribution platform business

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What is the biggest challenge?
Extract the organization data without freezing other teams

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What is the biggest challenge?
● Aggregates related to organizations (core customer data) has to be redesigned to support
future use cases
● Show must go on: we have no possibility to freeze the development process for other
teams
Extract the organization data without freezing other teams

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What is the biggest challenge?
● What makes it harder:
○ core customer data is used by multiple clients (services), finding and addressing all of them requires substantial
efforts
● What makes it easier:
○ refactored data structures are critically required now only for 1 upcoming usecase (a 3rd party integration)
○ the rest of the system can migrate to the new data structures gradually as aligned with the product changes in
our roadmap
Extract the organization data without freezing other teams

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So, we have the following requirements for the refactoring
● Do not freeze the development
● Allow new use case implementation
● Make little to no changes to the monolith
● Single entrypoint

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What refactoring patterns did we choose from?
● Strangler Fig
● Branch by Abstraction
● Parallel Run

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Strangler Fig
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Branch by Abstraction
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Whatʼs the problem with the parallel run?
It requires two masters in our case

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What refactoring pattern did we choose?
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R1 R2 R3 R4
Strangler Fig
Branch By Abstraction
Parallel Run

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Whatʼs the problem with multi-master ?
● ~1.5 person/year implementation cost in our case
● High potential for inconsistencies
● Non-zero risk of data corruption
● Expensive maintenance
Itʼs highly complicated to implement

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Why donʼt we afraid of multi-master and data-conflict?
- Data transaction rate is relatively low
- Most of crucial changes are handled by workflow and its orchestrator
- Temporary solution
- Monitoring & Alerting
- Release planning
- Domain Driven Design adoption
- Eventual Consistency Layer
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How Domain Driven Design helps us?
● Domain modeling and ubiquitous
language development
● Clear, understandable model that
reflects reality
● Single entrypoint and business
rules encapsulated in Aggregates
● Align and sync data via Domain
Events publishing

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How to sync data stored in different structures?
● Domain Events in the new systems
● Change Data Capture (CDC) for the
old one
● Align and sync data via Eventual
Consistency Layer

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How to outbox?

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How to CDC?

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How to CDC?
Physical Replication: Transaction Log Tailing

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How to CDC?
Physical Replication: Kafka Connect + Debezium

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How to CDC?
Logical Replication Stream

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How to CDC?
pg_amqp extension

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CDC - good not only for ETL pipelines
● Easy to setup
● Single source of truth
● Reflects atomic changes
● No changes in Legacy Code Base
● Auditability
● Batch or Real-Time publish (Cron, Pullers, etc.)
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Eventual Consistency Layer
● Keeps data consistent across contexts. Eventually
● Encapsulate knowledge
● Handles changes order, can lock data if needed
● Contains consistency-fix/consistency-check endpoints
● Contains consistency-check job
● Audit
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How does the eventual consistency layer look like?
More than just a worker

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How to switch old API Consumers?

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How to switch old API Consumers?

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How to switch old API Consumers?

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Practical insights
● Monitoring & Auditability
● Do not trust your data
● Prepare a QA strategy
● Ensure the solution you choose is supported on your hosting, vm, cloud etc.
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