In-Memory Concurrency Queue — High-Volume Notification Batching
Thread-safe in-memory Java batch queue (10s interval) reducing database connection pool overloading by 55%.
Overloading eliminated under notification spikes.
Thread-safe scheduled batch bulk inserts.
Java in-memory concurrency without Kafka.
High-frequency notification write operations overwhelmed database connection pools; dedicated message brokers like Kafka were unavailable due to infrastructure constraints.
Engineered a thread-safe, in-memory Java batch queue flushing every 10 seconds, cutting database connection pool strain by 55% without external infrastructure dependencies.
Project description
Identified database connection pool exhaustion caused by high-frequency individual notification insert operations.
Engineered a thread-safe, in-memory Java queue using concurrent data structures to buffer notification requests without external message broker infrastructure.
Configured a scheduled batch worker that flushes and bulk-inserts queued notifications every 10 seconds.
Achieved a 55% reduction in database connection pool strain, preventing connection timeouts during peak traffic bursts.
Why I built this
High notification insert volume was overwhelming available database connection pool limits.
External message brokers like Kafka were unavailable due to infrastructure and resource constraints.
Process and execution
How the work moved
- Diagnosed database connection pool exhaustion caused by high-volume individual notification insert transactions.
- Architected a thread-safe in-memory buffer using concurrent Java data structures to decouple notification generation from database writes.
- Implemented a scheduled batch worker that flushes queued notifications via batch SQL inserts at fixed 10-second intervals.
- Monitored connection pool metrics, confirming a 55% reduction in pool strain under peak notification bursts.
What I owned
- Led the work as Associate Consultant - Java Backend Developer.
- Turned messy context into a clearer operating model, interaction direction, and rollout shape.
- Worked across stakeholders to move the project from framing into execution.
Notable decisions
In-memory concurrent batch queue
Avoided heavy external broker dependencies (like Kafka) by implementing a lightweight, thread-safe in-memory buffer in Java.
10-second timed batch flushing
Aggregated single inserts into bulk SQL statements every 10 seconds, minimizing connection pool acquisition overhead.
What I learned from this
Thread-safe concurrent in-memory queues in Java can solve high write bottlenecks when external message broker infrastructure is not an option.
Batch SQL writes on a 10s interval drastically reduce connection pool acquisition contention.