This document discusses Revolut's event streaming architecture. It describes Revolut's use of an event store to provide strong consistency, at least once delivery, and high scalability when processing over 3.7 billion events per month. Key aspects discussed include using PostgreSQL for event storage, partitioning events by month, streaming events to consumers in parallel using reactive streams, and ensuring guaranteed processing through checkpointing and dead letter queues.

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Event Streaming
The hard Revolut way
César Luis Alvargonzález

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César Luis Alvargonzález
Senior Software Engineer at Revolut
Platform team Jan 2019 - Ongoing
Twitter: @sisa989
Email: cesar.alvargonzalez@revolut.com

3. Opinions are my own

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Agenda
- Alpha Framework
- Motivations
- Eventstore architecture
- Write path
- Streaming
- Partitioning
- Flow of an Event
- Conclusion

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What’s Revolut?

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Revolut’s Architecture
Around 160 services
Synchronous & asynchronous communication
Java 11, Kotlin and Scala
Postgres 12 and Redis

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Alpha Framework [DDD Framework]
Action Model
Action Event Model Event
1 n

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Requirements
Strong consistency
At least once delivery
Low latency
High Scalability
Easy to operate

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Why not Kafka?
Ad-hoc queries & filters
Querying by time
Guaranteed consistency between state changes and persisted
events.
One to many relation between Action-Event and Model-Event
Allow archiving events

11. EventStore & EventStream

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EventStore in numbers
Streaming
Events per month
Retention
Years of available data
Ingestion
Events per month 3.7B1B
Growth
Year over year10x 4y

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Fraud Detection
🕵🕵
FX-Risk
Calculation
🕵
Marketing and
Promotion.
📢
Derived Systems
🕵🕵
Use Cases

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Technologies

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EventStore

16. Persistence

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Write-Path - Publisher
Write ahead log
Atomic Operation
Reconciler

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Write-Path - Server Side
PostgreSQL Native Partitioning
Partitioning per month
PG Partman extension

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Write-Path - Server Side

20. Streaming - Client Side

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Streaming Architecture

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Consumers control
public final class AddressChangeConsumer extends
SingleEventConsumer {
AddressChangeConsumer() {
super(USER_MODEL_TYPE,
USER_ADDRESS_CHANGED_EVENT);
}
@Override
public void handle(ModelEvent event) {
// Business Logic
}
}
public final class TransactionRevertedEventConsumer
extends MultiEventConsumer {
public TransactionRevertedEventConsumer() {
super(TRANSACTION_MODEL_TYPE, Set.of(
TRANSACTION_REVERTED_EVENT,
TRANSACTION_REFUNDED_EVENT,
TRANSACTION_PARTIALLY_REFUNDED_EVENT
);
}
@Override
public void handle(final ModelEvent event) {
// Business Logic
}
}

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Event Processor
Execute Several Consumers
Execute Several Partitions
Relies on distributed locks
final List<EventStoreConsumerWithQuery> consumers =
List.of(
new AddressChangeConsumer(),
new TransactionRevertedEventConsumer()
);
final EventStreamProcessor processor =
eventStreamProcessor("com.revolut.my-app", config)
.consumers(consumers)
.build();
processor.run();

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Partitioning
Sequential
Virtual Partitions
Increase throughput by adding partitions

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Consumers Execution
Parallel Sequential
Single threaded
Parallelize consumers

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Stream
Built-in repartitioning

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Checkpointing
Commit per event
Persist on Redis
Record per partition

28. Streaming - Server Side

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Anatomy of a Subscription
Offline stream: Historical events
Live Stream: Real Time events

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Offline Stream
offset.isBefore(
Instant.now().minusSeconds(600));
!offset.isBefore(
Instant.now().minusSeconds(600));

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Live Stream

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Live Stream

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Reactive Streams
Lazy
Built-in backpressure
Non-blocking
Consumers control the pace

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Guarantee of Processing
Dead Letter Queue
Processing Guarantee Policy:
- Unlimited
- Expiring
- Disabled
Replay Strategy:
- Startup
- Periodic
- Disabled

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Flow of an Event
1 2 3
4 6 5a
5b
78

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Q & A
www.revolut.com