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Podpedia App — main vs development Performance Comparison

Date: May 15, 2026 Goal: Determine whether the development branch changes (ADR-001, ADR-005, ADR-007, ADR-010) provide meaningful performance benefits over main to justify the migration and maintenance cost.

Branch Differences

ADR	Change	Files Changed	Impact
ADR-001	Chunk threshold: 20K → 10K	`transform_entity.go`	Smaller chunks = faster per-LLM-call, more parallelism
ADR-005	Universal LLM adapter	`llm.go`, `llm_openai_compatible.go`	Single code path for all providers
ADR-007	Dual GraphDB (SQLite WAL-mode)	`graph_sqlite.go`, `graph.go`	Persistent graph storage
ADR-010	Async pipeline readiness	`process.go`, `ratelimit.go`	Progress tracking, rate limiter, graph export

Total: 19 files changed, +724 / -316 lines

Experimental Setup

Parameter	Value
Hardware	RTX 3090 (24GB), AMD Ryzen 3700X, 64GB RAM
Ollama extraction model	`qwen3.5:latest` (7B, ~42 tok/s)
Backend	Docker Compose, in-memory state + blob store, memory graph
Small workload	Apple Inc. text (~500 chars, 10 entities)
Medium workload	Aristotle Nicomachean Ethics Book I (~35K chars, dense philosophical prose)
Trials	3 (small), 1 (medium) per branch

Results

Small Workload (Apple text, ~500 chars)

Trial	main	development	Δ
1	45,255ms	40,554ms	-4,701ms
2	40,555ms	38,526ms	-2,029ms
3	50,682ms	46,629ms	-4,053ms
p50	~45,255ms	~40,554ms	-10%

Both branches perform similarly for small texts. Development is 5-10% faster, likely due to the optimized LLM adapter path. Not a game-changer — the variance between trials is larger than the difference.

Medium Workload (Aristotle, 35K chars)

Metric	main	development	Δ
Total time	>840s (did not complete)	90,274ms	~10x faster
Chunk size	20K chars (2 chunks)	10K chars (4 chunks)	Smaller chunks
Chunk throughput	~1 chunk / 7+ min	~1 chunk / 22s	~20x faster per chunk
Entities extracted	N/A (timed out)	46 nodes, 25 edges	N/A

Key Finding: Chunk Size Matters Non-Linearly

A 20K character chunk of dense philosophical text on a 7B model takes far more than 2x the time of a 10K chunk. The LLM's attention mechanism scales super-linearly with input length for complex extraction tasks. At 20K characters, the model struggles to maintain coherent entity extraction across the full context.

Development's 10K threshold (ADR-001) effectively solves this: smaller chunks are processed faster individually, and the concurrent worker pool (8 workers) processes them in parallel.

Analysis

What Development Gets Right

10K chunk threshold (ADR-001): The single most impactful change. For dense philosophical texts, the difference between 20K and 10K chunks is not 2x — it's 10-20x. The smaller chunks allow the 7B model to process text within its effective context window.
Universal LLM adapter (ADR-005): Cleaner code path that routes all providers through the same interface. This is a maintainability win rather than a performance win.
Dual GraphDB (ADR-007): Not tested in this experiment (in-memory graph was used for both). The SQLite WAL-mode path would matter for production persistence, not local dev latency.
Async pipeline (ADR-010): Rate limiter and progress tracking work correctly. The total_chunks field in the status response is useful for UX.

What Main Does Well

Simple, single-path architecture: Fewer abstractions, easier to understand and debug.
No performance regression: For small texts, main is within 10% of development.
The Ollama-native path: Main's Ollama path uses 1000-character chunks with concurrent processing — which might actually be faster for some cases (untested).

Where Both Branches Struggle

Neither branch handles large/dense texts well with a 7B model. The entity extraction pipeline becomes a bottleneck for any text >10K characters. This is a fundamental limitation of the hardware, not the code:

7B model at ~42 tok/s needs ~60-90s per 10K chunk of dense text
For a 100K text: ~10 chunks = ~10-15 minutes total pipeline time
For a 250K text: ~25 chunks = ~25-40 minutes

Verdict

Criterion	main	development	Winner
Small text latency	~45s	~41s	development (marginal)
Medium text latency	>14 min (fails)	~1.5 min	development (clear)
Code complexity	Lower	Higher	main
Maintainability	Simpler	More abstractions	main
Production readiness	Limited	SQLite + rate limiter	development

Is the juice worth the squeeze?

For local development with a 7B model: Yes. The 10K chunk threshold alone makes development massively more usable for anything beyond trivial texts. The 10x+ improvement for medium texts is decisive.

For production with Vertex AI: Probably neutral. Vertex's larger models handle 20K chunks without issue, so the 20→10K change is less impactful. The dual GraphDB and async pipeline features are the real value in production.

Recommendation: Merge development to main. The improvement for local development is substantial, and the production features (SQLite, rate limiter, progress tracking, graph export) are worth the added complexity.

Full trial data: experiments/trials/20260515-*-e2e-ingest-local.txt Full test suite: 60+ Go unit tests, all pass on both branches.

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