01 / The Problem & Hard Constraints

The Problem & Hard Constraints

Handling thousands of simultaneous micro-tips and live audience transactions in real-time presents severe database and state reconciliation challenges.

• Race Conditions & Double-Spend: Concurrent incoming transaction requests to a single wallet balance cause row-level lock contention in standard RDBMS architectures, leading to request timeouts or double-spending.
• Sub-50ms Processing Budget: Tip execution must acknowledge receipt to the UI in <50ms to maintain real-time interactive stream feedback.
• Offline-to-Online State Reconciliation: Mobile clients operating in degraded network environments must maintain local ledger state without permitting unverified balance updates.

02 / Architecture & Core Design Decisions

Architecture & Core Design Decisions

• Append-Only Event Sourcing: Replaced mutable balance rows with an immutable event log (TransactionCreated, BalanceReserved, LedgerCommitted). Account balances are computed via state projection streams rather than in-place mutation.
• Optimistic Concurrency Control (OCC): Used monotonic version numbers and atomic Compare-And-Swap (CAS) semantics instead of pessimistic database row locks, allowing 100% non-blocking parallel reads and writes.
• Write-Behind In-Memory Batching: Micro-transactions are ingested into a high-throughput Redis stream, aggregated in memory per creator ID over a 100ms window, and flushed as a single batch operation to PostgreSQL.

03 / Deep Technical Challenges & Solutions

Deep Technical Challenges & Solutions

The Challenge: Database Row Locking Under Burst Traffic. During live events, thousands of users tip the same creator within the same second. Standard SELECT ... FOR UPDATE row locks caused connection pool exhaustion and database CPU saturation.

The Solution: In-Memory Ring Buffer & Aggregated Batch Commits.

1. Ingested incoming tips directly into an in-memory LMAX Disruptor-style ring buffer written in Rust/Go.
2. An async background worker aggregates 1,000 individual $1 tip events into a single $1,000 bulk debit/credit ledger entry every 100ms.
3. Decreased individual database write operations by 98.4%, while preserving individual event audit trails in an append-only log.

04 / Post-Mortem & Next Steps

Post-Mortem & Next Steps

• Redis Node Failover Data Loss Risk: Relying on in-memory streams exposed a window where uncommitted tips could be lost if the Redis master node crashed before flushing to Postgres.
• Mitigation: Configured Redis AOF (Append Only File) with fsync=everysec and implemented a local WAL (Write-Ahead Log) on the edge worker nodes to replay pending transactions upon restart.