This document discusses strategies for synchronizing data between a legacy system and a new microservices architecture. It proposes using change data capture (CDC) to log all database changes in the legacy system and streaming them to the microservices using Apache Kafka. This allows the two systems to run concurrently while keeping the data eventually consistent as the services are migrated gradually using a strangler pattern.

1. Two way data sync between legacy
and your brand new micro-service
architecture

2. Brice LEPORINI
43 YO
CTO PALO IT France
Senior Developer
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4. Replace a teenager monolith (15 YO)
Micro-service oriented architecture
Progressive migration
The mission
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5. @blep

6. Microservices ? Why?
Distinct life cycle for each service
Ability to scale out specific service
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7. Gradual migration
No big bang
Strangler pattern: features / bounded
contexts gradually replaced
Canary releases
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8. The legacy and the micro-services
have to run side by side.
They can modify concurrently same
data
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9. Schéma sync ms <-> legacy
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10. Conflictless !
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11. Periods in the migration of each context
● The new context is not fully effective for everyone → the legacy system is the master of
data
● The new context is battle tested → the corresponding features are no longer available in
the legacy but it may need to be notified of changes for other features
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12. Remember how work databases
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14. You can’t change what
happened.
But you can add an event
that cancels a previous one.
Events are immutable
What happens if a
cancelation event is
consumed before the event
it cancels!
Events are ordered
Strong coupling with the
snapshot will imply complex
migration during
application’s life.
Beware of tight coupling
your event model
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15. Entity state in MS environment
=
Last entity found in the snapshot
+
Events without validation
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16. Yes it is a kind of
CQRS + ES
usage
CQRS = Command Query Responsability Segregation
ES = Event Sourcing
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17. Legacy —> Microservices snapshot synchronization
No interrogation allowed
Minimal latency
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18. Legacy —> Microservices snapshot synchronization
Business events , however :
● Huge legacy code base, missing events may occur
● High costs
Database triggers :
● Run with each DML, high impact on performance and scalability
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19. CDC : Change Data Capture
Logs every change that occurs in the database
Nothing to change in the legacy application
Technical options: Debezium, built in in MSSQL
Technical events
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20. Consideration for CDC Usage in SQL Server
One update on one row ⇒ two CDC rows with previous state and new state
1000 on one row ⇒ 2000 CDC rows…
One bulk update …
Note:
● Beware on the tables you activate CDC: high traffic tables need your attention …
○ Performance
○ File system filling
● CDC tables should be stored in other disks than business tables
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21. Retail example: order article count
select count(*) from tbl_item
where fkid_order=?
Item count may be inferred from the sum of
article prices compared to total order price
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22. What if one or more items come with
0 as price?
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23. This design is based on the
eventual consistency
principle which means that you may
be inconsistent for a short moment
(a few milliseconds?) but you will
finally be consistent
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24. Keystone : no message can be lost
Apache Kafka:
● Data streaming platform
● Messages are persisted and replicated
● Delivery order is kept by partition
● Distributed, fault tolerant and horizontally scalable
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26. Data path: changes coming from legacy
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27. Data path: changes coming from microservices
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28. Rollout procedure
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