Project Name
Database Row-Locking Replaced with In-Memory Flink Processing for a High-Volume eCommerce Retailer
A fast-growing eCommerce retailer processing massive order volumes during high-traffic flash sales and holiday events ran a monolithic, synchronous architecture where every transaction queried a relational database to check and update inventory. Under flash sale loads, this caused severe database row-locking, transaction queuing, system crashes, and delayed operational visibility – resulting in stockouts, overselling, and direct revenue loss. The platform had no real-time routing intelligence, no in-memory state management, and no live visibility into regional warehouse health. Applying its AI-First approach, Ksolves replaced the synchronous bottleneck with a decoupled, event-driven streaming infrastructure on Apache Flink and Kafka – achieving zero database locks during checkout, sub-10ms routing decisions, and 100% inventory accuracy maintained entirely in memory across 4 regional warehouses.
- Database Bottlenecks and Row-Locking: Every transaction required a synchronous PostgreSQL query to verify stock and update rows. Under flash sale loads, this caused massive lock contention and transaction queuing, stalling checkout and crashing the system entirely.
- Delayed Inventory Visibility: Operations teams had no real-time view of regional stockouts. Dashboards were entirely reactive - the supply chain team only discovered a product was sold out after the revenue was already gone.
- Rigid Fulfillment Routing: Routing logic was inflexible and costly. The business was losing margin on shipping and risking product spoilage because the system could not instantly restrict fallback routing for categories like Groceries.
- Stateful Memory Management at Scale: The platform needed to track 100+ active SKUs across 4 regional warehouses in real time, without hitting the database as the source of truth. That required a fundamentally different processing model.
- Dual-Path Database Synchronisation: Two outputs were needed simultaneously, an append-only order ledger and a live inventory snapshot that overwrites continuously. Both had to write concurrently without slowing the main processing stream.
Ksolves implemented a decoupled, event-driven streaming infrastructure using Apache Kafka as the high-throughput event bus and Apache Flink as the stateful stream processing engine. The governing principle was zero database dependency in the hot path: all inventory checks, routing decisions, and state mutations happen entirely in memory using Flink's MapState, with PostgreSQL receiving results asynchronously through a dual-sink output strategy.
- Apache Flink Stateful Stream Processing: A Flink pipeline using KeyedProcessFunction keyed by product_id ensures race-condition-free, ordered inventory deductions using local MapState. All 4 regional warehouses stay accurate in real time, zero database reads in the processing path.
- Apache Kafka Event Bus: The inventory_events topic ingests thousands of simultaneous ORDER and RESTOCK payloads. Kafka safely decouples the ingestion layer from downstream processing - protecting the database from direct traffic spikes during flash sales.
- Dual-Sink Output via Flink OutputTag: A single Apache Flink pipeline emits two parallel output streams. The main stream writes immutable fulfillment outcomes (FULFILLED_LOCAL, FULFILLED_FALLBACK, OUT_OF_STOCK) to the order ledger. A side stream concurrently fires JDBC UPSERTs to keep the live inventory snapshot current.
- Multi-Tier Routing With Category Constraints: The routing algorithm tries local warehouse fulfillment first, falls back to the nearest available warehouse if out of stock, and enforces hard category constraints - Grocery products (product_id >= 401) are blocked from fallback shipping entirely to prevent spoilage.
- Apache Superset Command Centre Dashboard: A 10-second auto-refreshing dashboard joins live streaming facts with PostgreSQL dimensional data. Business stakeholders see revenue at risk, network fulfillment efficiency, warehouse stock levels, and flash sale velocity, all in real time.
Technology Stack
| Category | Technology |
|---|---|
| Stream Processing | Apache Flink |
| Message Queue | Apache Kafka |
| Database | PostgreSQL |
| Output Strategy | Flink OutputTag (Dual-Sink) |
| BI / Analytics | Apache Superset |
- Zero Database Locks During Checkout: All inventory checks and stock deductions now execute entirely in Flink MapState, the synchronous PostgreSQL bottleneck that caused row-locking and system crashes during flash sales has been eliminated from the checkout path entirely.
- Sub-10ms Routing Decisions Across 4 Warehouses: Routing decisions including local fulfillment, fallback warehouse selection, and Grocery spoilage constraint enforcement complete in under 10 milliseconds, entirely in-memory with no database round-trips.
- 100% Inventory Accuracy in Memory: Flink MapState maintains a perfectly accurate real-time count of 100+ product SKUs across all 4 regional warehouses, stockouts and overselling eliminated from the checkout flow.
- Live Operational Command Centre: Apache Superset refreshing every 10 seconds gives operations teams live visibility into warehouse stock levels, fulfillment efficiency, flash sale velocity, and revenue at risk, replacing reactive after-the-fact reporting.
- Dual-Sink Handles Concurrent Persistence: Two concurrent JDBC sinks write the order fulfillment ledger and live inventory snapshot simultaneously - no processing slowdown, no write conflicts, no manual synchronisation required.
A fast-growing eCommerce retailer crashing under flash sale load due to synchronous PostgreSQL row-locking was transformed into a zero-lock, real-time fulfillment operation through Ksolves Big Data consulting services. Apache Flink with MapState eliminated all database reads from the checkout hot path, Kafka absorbed ingestion spikes, and a dual-sink OutputTag strategy maintains both the order ledger and live inventory snapshot concurrently without conflict. Zero database locks, sub-10ms routing, 100% inventory accuracy in memory, and a live Superset command centre, the platform now scales through flash sales without operational disruption.
Every Row Lock During a Flash Sale Is a Lost Order. Let’s Eliminate Them.
