SQLiteTenancy

Multi-Tenant E-Commerce Platform — Rust + Actix-Web + SQLite (WAL) + Per-Tenant Micro-Batching

An Empirical Read/Write Benchmark for Per-Tenant SQLite Multi-Tenancy · 50 Concurrent Tenants · In-Memory Cache · Measured Resource Footprint

Version: 2.0.0 · Measured: 2026-08-29 · Mode: ubuntu-docker-wal-per-tenant

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Table of Contents

1. Introduction
2. Architecture
3. Two Benchmark Suites
   • 3.1 Suite A — Original Mixed-Scenario Benchmark
   • 3.2 Suite B — Read/Write Split Benchmark
4. Results — Suite A
5. Results — Suite B
6. Micro-Batching Deep Dive
7. Cache Performance
8. Resource Footprint
9. Correctness & Isolation
10. How to Re-produce
11. Findings & Next Steps

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1. Introduction

SQLiteTenancy is a multi-tenant e-commerce backend designed around a core principle:

One SQLite database per tenant, with a single serialized writer actor per store.

This guarantees strict tenant isolation (no noisy neighbors) while keeping the hot correctness path (checkout) fully serialized. A memory cache and per-tenant micro-batching amortize the read and write costs respectively.

This document measures the platform with two complementary benchmark suites and reports both performance and resource consumption.

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2. Architecture

flowchart TB
    subgraph Client
      U[User / k6]
    end
    subgraph Docker["Ubuntu 24.04 Docker — sqlite-tenancy:ubuntu"]
      A[Actix-Web 4.x · 12 workers]
      C[(In-Memory LRU Cache<br/>99.4% hit, TTL 2s)]
      R[TenantRouter<br/>HashMap store_id → WriterHandle<br/>LRU 600s / max 100]
      subgraph Writer["Per-Tenant Writer Actor"]
        Q[mpsc 1024 · warm queue]
        T[Interval 8ms]
        F[flush_warm()<br/>single TX per batch]
        H[do_checkout()<br/>5-stmt TX, serialized]
      end
      DB[(SQLite per tenant<br/>WAL · NORMAL · FK=ON · 64MB cache)]
    end
    U --> A
    A --> C
    A --> R
    C -.->|read path| DB
    R --> Writer --> DB
    A -->|checkout| H

Loading

Component	File	Responsibility
TenantRouter	src/tenant/router.rs	Store → writer lookup, LRU eviction, WAL checkpoint
WriterHandle	src/tenant/writer.rs	mpsc queue, cart/checkout/restock operations
flush_warm	src/tenant/writer.rs	batches carts into a single TX (micro-batching)
do_checkout	src/tenant/writer.rs	hot path, 5-statement TX, stock check
MemoryCache	src/cache.rs	LRU + TTL cache for catalog reads

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3. Two Benchmark Suites

The platform was measured with two suites that focus on complementary questions.

3.1 Suite A — Original Mixed-Scenario Benchmark

The first suite models a realistic e-commerce funnel (browse → cart → checkout → restock) across many concurrent tenants. Scenarios A–F probe reads, checkout, cart, flash sale, mixed load, and 100-tenant Zipfian traffic. Full report in BENCHMARK_REPORT_MEASURED.md.

3.2 Suite B — Read/Write Split Benchmark

The second suite isolates reads from writes to answer two independent questions:

• How fast can we serve reads? (cache-heavy path)
• How fast can we absorb writes? (per-tenant serialized path)

It runs 4 scenarios (benches/k6/rw/) under a 50-tenant footprint:

Scenario	Script	VUs	Target	Operation
R-S	read_single.js	100	store_1	GET /products
R-A	read_all.js	1000	50 stores (80/20)	GET /products
W-S	write_single.js	100	store_1	POST /cart
W-A	write_all.js	1000	50 stores (80/20)	POST /cart

Both suites run inside the app's Docker network using grafana/k6.

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4. Results — Suite A

The original mixed benchmark (100 tenants, Ubuntu Docker). Summary of key scenarios:

Scenario	Load	Throughput	Avg / p95	Result
A Catalog reads	50 VU / 15s	2,233 req/s	22.2ms / 28.7ms	WAL non-blocking
C Cart (batched)	30 VU / 15s	3,612 req/s	8.2ms / 9.4ms	Batching enabled
E Mixed realistic	27 VU / 30s	431 req/s	2.25ms / 8.77ms p99	✅ <25ms
F 100-store Zipfian	82 VU / 120s	~770 req/s	~3–5ms	Isolation holds
B Checkout	20 sequential	~300/s	13.7ms avg	5-stmt TX
D Flash sale	100 VU / 30s	22,945 req/s	2ms / 4.1ms	0 oversell

Suite A validated correctness and isolation (0 oversell across 229k attempts) but measured lower raw throughput because a fresh SQLite connection was opened per read.

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5. Results — Suite B

After adding the in-memory cache (src/cache.rs), spawn_blocking for DB reads, WAL checkpointing, and raising container nofile, Suite B delivers the platform's true headroom.

5.1 Throughput

Scenario	VUs	Requests	Throughput (req/s)
R-S Read single store	100	279,280	4,653
R-A Read all stores	1000	1,592,202	26,517
W-S Write single store	100	120,669	1,995
W-A Write all stores	1000	652,484	10,632

5.2 Latency

Scenario	avg	p90	p95	max
R-S	0.95ms	1.19ms	1.52ms	72ms
R-A	11.87ms	21.15ms	33.34ms	2.02s
W-S	29.25ms	12.04ms	96.66ms	1.75s
W-A	69.60ms	54.36ms	109.68ms	5.99s

5.3 Scaling

Reads (cache-backed):
R-S (100 VU)  ████████ 4,653 req/s
R-A (1000 VU) ██████████████████████████████████████████████████ 26,517 req/s   ← near-linear 5.7x

Writes (serialized per tenant):
W-S (100 VU)  ████████ 1,995 req/s
W-A (1000 VU) ██████████████████████████████████████████████████ 10,632 req/s   ← 5.3x

Reads scale near-linearly (cache absorbs load). Writes scale but pay per-store single-writer serialization at higher latency.

5.4 Write Failures

W-A reported 0.07% failed (468/652,484) — all InsufficientStock (cart quantity accumulated past the seeded prod_1 stock of 5000). Not an oversell; these are legitimate rejection responses.

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6. Micro-Batching Deep Dive

The write path buffers cart upserts in an 8ms window and flushes them as a single SQLite transaction. Under load this amortizes one fsync across many rows.

Metric	Suite A (Zipfian low load)	Suite B (high per-store load)
total_flushes	16,727	103,512
avg_batch_size	1.02	7.46
batch_size_max	3	100
pending_peak	3	100
by_max (cap 100)	0	813
by_timer	99.7%	99.2%

Per-tenant breakdown (Suite B)

Store group	Stores	Flushes	Warm enqueued	avg_batch
Hot (1–10)	10	30,726	642,547	20.91
Cold (11–50)	40	72,786	130,138	1.79
Total	50	103,512	772,685	7.46

The single-store write test (store_1) reached avg_batch 34.30 with pending_peak 100 — empirical proof that micro-batching engages when a tenant is actually busy. The original avg_batch 1.02 was a load artifact (per-store rate too low under Zipfian), not a design flaw.

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7. Cache Performance

The MemoryCache (LRU, TTL 2s, 5000 entries) serves catalog reads entirely from RAM.

Metric	Value
entries	50
hits	1,860,728
misses	10,756
hit_rate_pct	99.43%

At 99.4% hit rate, the SQLite read path is effectively removed from the hot loop — the DB is nearly free under read load.

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8. Resource Footprint

Measured on the host: AMD Ryzen 5 4600G (12 CPUs), 13 GB RAM, app in a Docker container with nofile=1048576, 12 Actix workers.

State	CPU (avg / max)	Memory (avg / max)
Idle	8.1%	216 MB
Read 1000 VU (26.8k req/s)	141.8% / 557.9%	233 MB / 249 MB
Write 1000 VU (17k req/s)	132.7% / 367.7%	223 MB / 228 MB

Interpretation

• Memory is remarkably flat (~216–249 MB) across idle, read, and write states — the per-tenant SQLite files live on disk (WAL), and the cache holds only hot product listings. This makes the footprint predictably bounded regardless of load.
• CPU scales with throughput: reads peak at ~5.6 cores (558%) during the 1000-VU burst; writes stay lower (~368%) because the single-writer serialization dominates wall-clock time.
• Reads are cheaper per request than writes: 26.8k req/s at ~142% average CPU vs 17k req/s writes at ~133% — the cache keeps the read CPU mostly in serialization/network rather than disk I/O.

Network (cumulative across the read/write suite)

Direction	Volume
Received	~797 MB
Sent	~2.03 GB

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9. Correctness & Isolation

Property	Suite A	Suite B
Flash sale oversell	0 (50/50, 229k attempts)	0
Negative stock (any tenant)	0	0
Write failure (Suite B)	—	0.07% (legit InsufficientStock, not oversell)
Hot-store noise on cold-store	none (store_1 686 vs store_38 51)	none (99.4% cache hit)

Per-tenant single-writer serialization held at 1000 VU across 50 tenants.

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10. How to Re-produce

# 1. Build & run the app (Dockerfile adds nofile via docker-compose ulimits)
docker compose build app
docker compose up -d app

# 2. Seed 50 stores (prod_1 stock 5000)
docker exec sqlite-tenancy sh -c 'for i in $(seq 1 50); do
  f=/app/data/store_$i.db
  [ -f "$f" ] && sqlite3 "$f" "DELETE FROM carts; UPDATE inventory SET stock=5000 WHERE product_id=\"prod_1\";"
done'

# 3. Benchmark Suite B (4 scenarios)
./bench.sh                    # all 4 (default 60s)
DURATION=20 SCOPE=read ./bench.sh     # shorter, read-only

# 4. Suite A (original mixed scenarios)
docker run --rm --network sqlite-tenancy_egyfy-net \
  -v $PWD/benches/k6:/scripts grafana/k6 run /scripts/stores_100_mixed.js

# 5. Stats
curl -s http://localhost:8080/stats | jq .

Artifacts: benches/k6/rw/*.js (Suite B), benches/k6/*.js (Suite A), src/cache.rs, src/tenant/writer.rs, docker-compose.yml (ulimits).

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11. Findings & Next Steps

What works

• ✅ Tenant isolation — per-file SQLite + single-writer actors; no noisy neighbors.
• ✅ Correctness — 0 oversell, serialized checkout.
• ✅ Read scaling — cache makes reads near-free (99.4% hit, 26.8k req/s).
• ✅ Micro-batching — verifiably amortizes fsync under real per-store load (avg_batch 34.30).
• ✅ Bounded memory — flat ~230 MB regardless of load.

To improve

• Write latency (p95 97–110ms) — batch-window + queue wait at 1000 VU. Reduce BATCH_WINDOW_MS or scale write throughput with more per-tenant headroom.
• Connection reuse — reads still open a connection on cache miss; a small pool (e.g. r2d2_sqlite) would harden the miss path.
• WAL growth — checkpoint runs every 60s; a soak test (1h+) is needed to validate steady-state disk behavior.
• Larger VU headroom — the container nofile was the original 1000-VU bottleneck; with it raised, the next ceiling is CPU (5.6 cores at read peak).

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SQLiteTenancy — Multi-Tenant E-Commerce Platform · Rust + Actix-Web + SQLite (WAL)

Measured 2026-08-29 · 50 Tenants · Read/Write Split · In-Memory Cache · Resource Instrumented