- Engineering teams executed more than 100 load test cycles using an alternative 1C:ERP benchmark, scaling the workload up to 30,000 virtual users.
- System performance reached a peak APDEX rating of 0.986 after resolving severe drops down to 0.704 at 15,000 users and crashes at 21,000 sessions.
- Critical architectural bottlenecks included unoptimized query plans, LWLock:PgStatsHash contention, NUMA imbalance, gigabit network limits, and millions of temporary table files.
Enterprise Scaling for 1C:ERP on Postgres Pro
Simulating extreme enterprise workloads frequently exposes hidden performance bottlenecks. Over the course of more than 100 test runs utilizing a specialized 1C:ERP benchmark developed by IT-Expertise, engineers took on the challenge of scaling a deployment to 30,000 concurrent virtual user sessions running on the Postgres Pro database management system.
Overcoming Performance Degradation at 15,000 and 21,000 Sessions
Achieving top-tier responsiveness required solving several operational roadblocks. At the 15,000-user mark, the APDEX user satisfaction rating collapsed to 0.704. In-depth diagnostics pointed to cache invalidation storms, severe hardware imbalances across NUMA nodes, and recurring database errors.
As engineers pushed user capacity higher, the test crashed during its fifth hour while ramping from 16,800 to 21,000 virtual users. During this spike, sub-second transactional queries suddenly took dozens of seconds to complete, while a standard operational report that previously built in 5 seconds ballooned to 800 seconds.
Benchmark Progress and Metric Highlights
| Load Tier (Users) | Initial Failure Mode | Final APDEX | Key Resolutions Applied |
|---|---|---|---|
| 15,000 | APDEX dropped to 0.704 | Stabilized | Resolved NUMA load imbalance, cache invalidation storm, engine errors |
| 21,000 | Crashed in hour 5 | 0.975 | Fixed LWLock:PgStatsHash, query plan regression, gigabit NIC cap, 3M temp files |
| 30,000 | Peak load stability stress | 0.986 | Full-stack database parameter optimization and hardware tuning |
Technical Bottlenecks and Optimization Tactics
To stabilize the system at 21,000 sessions and secure a dependable APDEX score of 0.975, the team implemented a series of critical infrastructure and database fixes:
- Lock Contention: Eliminating severe latency tied to LWLock:PgStatsHash, which surfaced under massive statistics collection routines.
- Plan Regressions: Stabilizing erratic query execution plans that caused reporting times to skyrocket.
- I/O and Networking: Alleviating bandwidth bottlenecks caused by a standard gigabit network interface and clearing storage overhead stemming from 3 million accumulated temporary table files.
These benchmark milestones confirm that high-concurrency enterprise workloads can efficiently run on modern PostgreSQL engines. However, reaching near-perfect APDEX scores demands strict attention to NUMA topology, database lock contention, and the aggressive lifecycle management of temporary file artifacts.
Reaching an APDEX of 0.986 under synthetic load is an impressive engineering feat, but synthetic benchmarks rarely mirror actual production chaos. Running 100+ tuning cycles suggests maintenance costs and specialized DBA overhead will be massive in day-to-day operations. How resilient is this fragile setup when unpredictable, ad-hoc user queries inevitably hit those aggressive NUMA and lock constraints?