Design a database schema for tracking stock trades in real-time.

Section 1 — The Context (The 'Why')
Real-time stock trade systems must capture every tick with millisecond latency. The primary challenge is write throughput, exactly-once semantics for P&L, and querying raw and aggregated data. At-least-once without dedup causes regulatory risk.

Section 2 — The Diagram

[Exchange] --> [Kafka] --> [TimescaleDB] --> [Agg View]
     |                |              |                  |
     v                v              v                  v
[Schema Reg]   [Symbol Part]   [Hypertables]    [Redis/Position]

Section 3 — Component Logic
Exchange feeds into Kafka partitioned by symbol. TimescaleDB ingests; hypertables partition by time. Append-only trade log; idempotency via exchange_trade_id. Redis for position and P&L. Exactly-once via consumer commit after DB write. In production, monitor consumer lag, checkpoint success rate, and sink write latency as primary SLOs. Partitioning strategies should align with query patterns; bucketing within partitions mitigates join skew. TTL policies on raw and intermediate data control storage cost while preserving replay capability for debugging and backfill. Data skew mitigation via salting or secondary hashing prevents single partitions from becoming bottlenecks. Exactly-once semantics require transactional commits at the sink; at-least-once delivery demands idempotent write logic to avoid duplicates. Fan-out patterns allow one source topic to feed multiple downstream consumers without re-ingestion. Backpressure handling ensures that slow processors do not cause unbounded buffer growth; Kafka consumer lag is a key metric. Schema evolution should follow additive-only rules where possible to avoid breaking consumer compatibility. The CAP trade-off should be documented per component: analytics typically favors AP, while financial reconciliation requires CP. Blast radius from component failure is bounded by replication and checkpointing; design for graceful degradation during partial outages. Cost optimization: use Spot instances for batch workloads and tier cold data to lower storage classes. Dead-letter queues preserve failed records for replay rather than dropping them.

Section 4 — The Trade-offs (The 'Senior' part)

Section 5 — Pro-Tip