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9 min readFebruary 03, 2026

PostgreSQL Scaling: Connection Pooling, Partitioning & High-Throughput Vacuum Tuning

A deep architectural guide to pushing PostgreSQL beyond 50,000 queries per second without locking up.

A
Avernus Engineering Team
Systems Architecture
Key Takeaways
  • Process-per-connection architecture makes PgBouncer transaction pooling mandatory above 200 concurrent clients.
  • Declarative table partitioning by date or tenant prevents index bloat on tables exceeding 100M rows.
  • Aggressive autovacuum tuning prevents transaction ID wraparound and table bloat during high-write workloads.

The Anatomy of PostgreSQL Concurrency Limits

Because PostgreSQL forks a separate backend OS process for each client connection, opening thousands of direct database connections consumes massive RAM and causes devastating CPU context-switching overhead. Implementing transaction-level pooling via PgBouncer is the single most impactful optimization for web scale.

Declarative Partitioning in Practice

When audit logs or event streams exceed 50 million rows, standard B-Tree indices exceed RAM cache sizes. Partitioning tables by month or tenant allows PostgreSQL query planner to prune untouched partitions completely.

Creating Declarative Range Partitions in PostgreSQL 16+sql
CREATE TABLE telemetry_events (
    id BIGSERIAL,
    event_time TIMESTAMPTZ NOT NULL,
    device_id UUID NOT NULL,
    payload JSONB
) PARTITION BY RANGE (event_time);

-- Monthly partition
CREATE TABLE telemetry_2026_02 PARTITION OF telemetry_events
    FOR VALUES FROM ('2026-02-01') TO ('2026-03-01');

CREATE INDEX idx_telemetry_2026_02_device 
    ON telemetry_2026_02 (device_id, event_time);
Topics Covered:
#PostgreSQL#PgBouncer#Database#Performance#SQL

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