All distributed data is synchronized in real time. Multiple bot instances stay consistent and behave like a single system.
Bot A
Bot B
Sync Engine
Database
Cache
Inter-bot event propagation, visible in real time. See which bot fired which event and which store it propagated to — instantly.
Real-time feed of all events
Source / event type / target tracing
Propagation latency monitoring
Event filtering and search
Event stream
14:32:01.001
Bot A
message.create
→ DB + Cache
2ms
14:32:01.004
Engine
state.broadcast
→ Bot A, Bot B
1ms
14:32:01.228
Bot B
member.join
→ DB
3ms
14:32:01.231
Engine
state.broadcast
→ Bot A, Bot B
1ms
14:32:01.445
Bot A
role.assign
→ Cache
1ms
14:32:01.890
Bot B
command.execute
→ DB + Cache
4ms
From event generation to data-store persistence, a four-stage pipeline guarantees consistency. Each step completes in milliseconds.
Event fires
A user command, message, or other event is generated.
Publish
The event is sent through Pub/Sub to the sync engine — millisecond-scale delay.
Propagate
Changes propagate to all related instances, preserving consistency.
Store update
Persisted to Redis / PostgreSQL — state survives pod restarts.
Pub/Sub messaging
Event-driven, reactive architecture
Distributed locking
Strict mutual exclusion via Redis Redlock
Event sourcing
All changes recorded chronologically and replayable
WebSocket integration
Instant push notifications to clients
Backpressure control
Event buffering under overload
< 5ms
Average sync delay
10K+
Concurrent connections
Strong
Data consistency
Auto
Failure recovery
Pub/Sub messaging
Event-driven, reactive architecture. Every instance receives events instantly and stays consistent.
Distributed locking
Redis Redlock eliminates race conditions with strict mutual exclusion.
Event sourcing
All changes recorded chronologically — reconstruct state at any point in time.
WebSocket integration & auto-recovery
Instant push to clients with automatic reconnection, backpressure control, and event buffering to prevent data loss.