Surge Pricing System: Uber - Stream Processing — System Design Interview Practice

Interview prompt

Design Design a dynamic pricing system that adjusts prices based on supply-demand in real-time. so users can Calculate real-time supply and demand reliably at scale.

• Define the source of truth for Calculate real-time supply and demand; Dynamically adjust prices per area and make retries idempotent.
• Use bounded, partitioned state to meet Handle millions of price calculations per minute and Real-time price updates (<1 second).
• Separate the critical request path from Stream processing for real-time events, Geospatial hashing for zones, Machine learning for demand prediction.
• Explain consistency, failure recovery, authorization, observability, and a degraded mode.

Requirements and scale assumptions

• Support the core workflow to Calculate real-time supply and demand.
• Expose status, results, and freshness appropriate to Design a dynamic pricing system that adjusts prices based on supply-demand in real-time..
• Support authorization, validation, updates, deletion, and recovery semantics.
• Meet Real-time price updates (<1 second) under normal load.
• Scale to Handle millions of price calculations per minute without a single hot key or unbounded synchronous work.
• Do not lose committed state; make retries and duplicate events safe.
• Degrade safely when downstream workers, caches, or external dependencies fail.
• Handle millions of price calculations per minute
• Partition by the primary tenant, user, item, or geographic key and isolate hot partitions.
• Keep serving state bounded; retain raw events or durable records for replay and auditing.

• Peak scale: Handle millions of price calculations per minute — Capacity assumption that drives partitioning and backpressure.
• Latency target: Real-time price updates (<1 second) — User-facing budget for the primary request or read path.
• Durable boundary: Committed before async — The source of truth is Calculate real-time supply and demand; Dynamically adjust prices per area.
• Async boundary: At-least-once workers — Keep Stream processing for real-time events, Geospatial hashing for zones, Machine learning for demand prediction off the synchronous path.

Key entities

• SourcePartitionsourceId, partitionId, cursor, schemaVersion, watermark, status

  Replayable surge pricing system uber stream processing source evidence and ingestion cursor.

• SchemaVersiondatasetId, version, compatibility, owner, effectiveAt, status

  Governed surge pricing system uber stream processing contract used to validate producers and consumers.

• ProcessingRunrunId, inputWatermark, checkpoint, qualityStatus, codeVersion, status

  Checkpointed surge pricing system uber stream processing processing attempt with quality and lineage metadata.

• AnalyticalDatasetdatasetId, partition, watermark, schemaVersion, qualityStatus, location

  Curated surge pricing system uber stream processing serving partition with freshness and quality state.

Data flow

1. 1. Register sources and contractsThe surge pricing system uber stream processing catalog records owners, schemas, compatibility rules, retention, lineage, and partitioning before data is accepted.
2. 2. Ingest with backpressureConnectors checkpoint surge pricing system uber stream processing source cursors, validate schema and deduplication keys, and slow producers when downstream capacity is exhausted.
3. 3. Process event time with checkpointsStream or batch engines compute surge pricing system uber stream processing transformations using watermarks, late-data policy, state checkpoints, and deterministic code versions.
4. 4. Publish quality-gated datasetsOnly surge pricing system uber stream processing outputs that pass completeness, freshness, validity, and privacy checks become visible to analytical consumers.
5. 5. Serve, replay, and reconcileConsumers read bounded partitions with freshness metadata while operators replay failed surge pricing system uber stream processing ranges and compare output checksums.

Deep dives and trade-offs

• Schema evolution and data qualityVersion surge pricing system uber stream processing contracts and make compatibility rules explicit for every producer and consumer. Quarantine malformed partitions instead of poisoning the whole dataset. Track row counts, null rates, duplicates, distribution changes, and policy violations by partition.
• Watermarks, late data, and exactly-once effectsUse source cursors and event-time watermarks for surge pricing system uber stream processing progress, not wall-clock assumptions. Make checkpoints, output keys, and sink commits retry-safe under at-least-once delivery. Document how late events revise windows, aggregates, or snapshots.
• Replay, lineage, and costKeep immutable surge pricing system uber stream processing raw evidence and code or schema versions so failed outputs can be reproduced. Separate hot serving storage from cold retention and cap replay concurrency. Measure freshness, backlog, compute cost, storage growth, and quality-gate failure rate.
• Streaming versus batchUse streaming for freshness-critical surge pricing system uber stream processing paths and batch for backfills, compaction, and expensive recomputation. Forcing every workload into streaming makes state, replay, and cost harder to operate.
• Raw retention versus curated-only storageRetain enough immutable raw evidence for replay, audit, and correction, then tier or expire it according to policy. Without raw evidence, a bad transformation can require an unreproducible emergency fix.
• Central warehouse versus domain-owned datasetsCentralize governance and discovery while letting domain owners own contracts and quality signals. A single team owning every transformation becomes a delivery bottleneck and hides data ownership.