FlowerDocs 2025.3: Perfomance Benchmark

Written by Engineering Team | May 12, 2026 9:56:20 AM

Performance Report

FlowerDocs 2025.3 : Performance Benchmarks

Uxopian Software Engineering Team · 7 min read · Gatling Framework

Uxopian Software

Engineering Team · Uxopian Software

At a glance : what the numbers say

FlowerDocs 2025.3 was tested under sustained load across six real-world document management scenarios. Here are the three numbers that matter most.

5,786 req/s

Document reads · 17ms avg latency

3,173 req/s

Metadata creation · 31ms avg latency

1,143 ms

Full-text search avg · 1M+ documents

These figures were measured with 100 concurrent users over a sustained 10-minute window per scenario, run across all six scenarios back-to-back on the same platform instance, making memory leak detection possible alongside throughput measurement.

Architecture and test configuration

The test infrastructure runs on a 4-core pod configuration with Redis caching and OpenSearch indexing. Object storage relies on an S3-compatible layer. The Gatling load testing framework generates the traffic, and every result is compared against the previous release to catch regressions before they ship.

What we tested : six production scenarios

Each scenario reproduces an everyday workflow of a document management platform.

S1 Create documents with randomized metadata

S2 Create documents with metadata + content (randomized size and MIME type)

S3 Get documents and their metadata

S4 Update document metadata

S5 Delete documents

S6 Search documents by metadata across 1M+ documents

Load conditions : 100 concurrent users, sustained continuously over a 10-minute test, repeated across 6 consecutive scenarios on the same platform, enabling memory leak detection over a full hour. All response times in milliseconds.

Results : raw benchmark data

Without back-end event-driven Operation Handlers

Scenario	Total requests	RPS	Min (ms)	Max (ms)	Mean (ms)	Std Dev	p50	p75	p95	p99
S1	1,844,641	3,069	11	3,757	32	16	31	36	50	63
S2 ⚠	407,392	672	26	31,740	147	630	65	96	229	2,559
S3 †	3,524,496	5,864	4	843	17	7	8	10	14	18
S4 †	1,842,476	3,066	11	3,114	32	18	33	40	57	127
S5 †	187,072	311	12	2,027	320	227	481	646	943	1,183
S6	45,147	75	250	4,031	1,329	537	1,239	1,702	2,316	2,652

⚠ S2 : isolated max outlier (31,740ms) reflects a single object storage timeout event under peak concurrent upload pressure.

Analysis : what the data tells us

Without event-driven handlers

The results reveal a robust infrastructure, supported by the 4-core-pod configuration and Redis, which handles pure metadata operations with remarkable efficiency.

Document reading dominates with 5,786 req/s and 17ms average latency, confirming the cache is working perfectly. Metadata creation and updates maintain high stability at around 30ms for approximately 3,000 req/s.

Document creation with content (S2) reveals an identified infrastructure constraint : under simultaneous large-file ingestion, throughput reduces to 122 req/s and max response time reaches 148.9 seconds, pointing to S3 storage layer pressure. FlowerDocs continued to handle requests correctly throughout.

Search at 1,143ms average on 1M+ documents remains acceptable, with OpenSearch and S3 identified as the primary bottleneck for complex operations.

What this means for you

check Everyday operations are fast and reliable. Reading, creating metadata, and updating documents all respond under 50ms at the 95th percentile, across thousands of concurrent requests.
check The caching layer works exactly as intended. Redis delivers sub-20ms average read latency at nearly 6,000 req/s, a clear validation of the architecture choices made in this release cycle.
info Document creation with content : an identified infrastructure constraint. Under simultaneous large-file ingestion, response times are linked to S3 storage layer pressure, not to FlowerDocs itself.
autorenew Continuous testing prevents regressions. Every version is benchmarked against the previous one, so past optimizations are never silently broken by new code.

Written by

Uxopian Software

Engineering Team

@Uxopian Software

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Talk to our team about deployment configuration, sizing, and what these benchmarks mean for your specific usage volumes.

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