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PARIS - 11 OCTOBRE 2018
Cédric Vidal
CTO
Rethinking digital onboarding: how
QuickSign's new microservices platform
leverages Kafka to massively scale
@cedricvidal
Kafka is the backbone of
microservices architecture
Why such a choice ?
~500 million tweets per day
~1,3 million trace entries per day
We are not twitter ...
Who are we ?
● QuickSign is the European leader in digital
onboarding for financial services
● White label
● Now handling millions of digital subscriptions
per year
10 years experience
15 countries
+70% Transactions growth / year
The state of digital
subscription
● critical channel
● banks, insurances
● major challenge
● performance of the subscription
● fast access to information
● fast decision
● automate the decision process
● push the data to the consumer
Realtime is key to the
decision process
realtime streamed
architectures
Artificial intelligence
● game changer
● the need for realtime dialog
● need to process a lot of data
● the machine can have doubts
● ask the human for confirmation
Traceability
● Regulated business
● Qualified signature ETSI 319 411
● Be able to prove conformance
● Non-repudiation
Data privacy and protection
● GDPR
● Personal data protection
● Multi tenant isolation
Full end to end
encryption !
No compromise !
Kafka Encryption
github.com/quicksign/kafka-encryption
code.quicksign.io/kafka-encryption
Apache 2
License
● application payload encryption
● GDPR request -> throw the key !
CQRS-ES with Kafka
user
Command Query Responsibility Segregation (Event Sourcing)
Aggregation Root
Store
direct modification
commands & queries
Controller
action
evaluates
Command
Event
EventEvent
Event Store
stores
produces
Command
Handler
reads
Event
EventEvent
Aggregation
Root
Event
Handler
Why Kafka ?
General observation
• Works for extremely massive big data use
cases
• Should handle easily our CQRS ES based
workloads …
Technical details of interest for CQRS-ES
• At least once guaranteed delivery
• Sequential processing per topic partition
• Sharding by design
• RPC like low latency
• Durability
• Replayability
Kafka Streams - The key
Technical details of interest for CQRS-ES
• Automatic offset tracking (inherited from Consumer
API)
• Embedded ephemeral disk based KV Store to persist
intermediate stream states and query stores
(Facebook’s RocksDB)
• Rich streaming DSL with operators (aggregates, joins,
…)
• Interactive Queries
Natural fit for Kubernetes’ stateless model
• a simple static void main which joins the Kafka Stream
cluster
• Very natural with the stateless Kubernetes deployment
model
All different
• Specific to each customer/product/country
• Business requirements
• Integration requirements
• Different maturity levels, business wise and technical wise
Progressive onboarding
• Start small
• Optimize progressively
• Gain maturity, business wise and technical wise
BPMN
• A process for each
customer/product
• State and transitions
The Kappa/CQRS-ES Architecture
REST APIWrite Interface Read Interface
Commands
History stream
History SPI
append only immutable log
Queries
Extremely fast read access
Streams
Pre-computed JSON
representations
Case µS
stream
processors
Avro
A reactively generated
REST API, crazy ?
● One API per customer
● API “generated” from BPMN
● Sync or Async
● Push or polling
Your turn!
Cédric Vidal
CTO
@cedricvidal
21
PARIS - 11 OCTOBRE 2018

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Rethinking Quicksign's Digital Onboarding - Confluent Streaming Days Paris

  • 1. 1 PARIS - 11 OCTOBRE 2018 Cédric Vidal CTO Rethinking digital onboarding: how QuickSign's new microservices platform leverages Kafka to massively scale @cedricvidal
  • 2. Kafka is the backbone of microservices architecture
  • 3. Why such a choice ?
  • 4. ~500 million tweets per day ~1,3 million trace entries per day We are not twitter ...
  • 5. Who are we ? ● QuickSign is the European leader in digital onboarding for financial services ● White label ● Now handling millions of digital subscriptions per year 10 years experience 15 countries +70% Transactions growth / year
  • 6. The state of digital subscription ● critical channel ● banks, insurances ● major challenge ● performance of the subscription
  • 7. ● fast access to information ● fast decision ● automate the decision process ● push the data to the consumer Realtime is key to the decision process realtime streamed architectures
  • 8. Artificial intelligence ● game changer ● the need for realtime dialog ● need to process a lot of data ● the machine can have doubts ● ask the human for confirmation
  • 9. Traceability ● Regulated business ● Qualified signature ETSI 319 411 ● Be able to prove conformance ● Non-repudiation
  • 10. Data privacy and protection ● GDPR ● Personal data protection ● Multi tenant isolation Full end to end encryption ! No compromise !
  • 12. CQRS-ES with Kafka user Command Query Responsibility Segregation (Event Sourcing) Aggregation Root Store direct modification commands & queries Controller action evaluates Command Event EventEvent Event Store stores produces Command Handler reads Event EventEvent Aggregation Root Event Handler
  • 13. Why Kafka ? General observation • Works for extremely massive big data use cases • Should handle easily our CQRS ES based workloads … Technical details of interest for CQRS-ES • At least once guaranteed delivery • Sequential processing per topic partition • Sharding by design • RPC like low latency • Durability • Replayability
  • 14. Kafka Streams - The key Technical details of interest for CQRS-ES • Automatic offset tracking (inherited from Consumer API) • Embedded ephemeral disk based KV Store to persist intermediate stream states and query stores (Facebook’s RocksDB) • Rich streaming DSL with operators (aggregates, joins, …) • Interactive Queries Natural fit for Kubernetes’ stateless model • a simple static void main which joins the Kafka Stream cluster • Very natural with the stateless Kubernetes deployment model
  • 15. All different • Specific to each customer/product/country • Business requirements • Integration requirements • Different maturity levels, business wise and technical wise
  • 16. Progressive onboarding • Start small • Optimize progressively • Gain maturity, business wise and technical wise
  • 17. BPMN • A process for each customer/product • State and transitions
  • 18. The Kappa/CQRS-ES Architecture REST APIWrite Interface Read Interface Commands History stream History SPI append only immutable log Queries Extremely fast read access Streams Pre-computed JSON representations Case µS stream processors Avro
  • 19. A reactively generated REST API, crazy ? ● One API per customer ● API “generated” from BPMN ● Sync or Async ● Push or polling
  • 21. 21 PARIS - 11 OCTOBRE 2018