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Layers at the same DAG level are independent — their dependencies are already computed. The runner now groups layers by level and executes each group concurrently. Single-layer levels still run inline. Thread safety: snapshot_txn writes protected by threading.Lock. Stats accumulated from return values, not shared mutation. For a pipeline with 5 fold layers at level 2 (~30 min each), this reduces build time from ~2.5 hours to ~30 minutes.
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Note Red Team Review — OpenAI GPT-5.4 | Adversarial review (docs + diff only) Threat assessment — Medium-high risk: this changes execution semantics in the build runner, introduces concurrency around shared state, and the tests only prove speed, not correctness. One-way doors
Findings
Missing
Verdict — Block: this is a scheduler change in the core build engine, and the PR does not establish semantic safety, only that some toy transforms finish faster. Review parameters
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Note Architectural Review — Claude Opus | Blind review (docs + diff only) Summary This PR parallelizes execution of independent layers within the build runner. Layers at the same DAG level (no dependency edges between them) now run concurrently via Alignment Strong fit. The DAG is the core structural concept (DESIGN.md §3, §1.2), and exploiting level-parallelism is the natural optimization the design anticipated ("transforms are embarrassingly parallel" — §4.4.2). Artifacts remain immutable and content-addressed; provenance chains are unmodified. Cache keys still capture all inputs. The change respects the build-system mental model exactly: independent compilation units parallelize, dependent ones don't. Observations
Verdict Good incremental step — correct exploitation of the DAG structure with reasonable thread safety, but needs a cap on Review parameters
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Note Red Team Review — OpenAI GPT-5.4 | Adversarial review (docs + diff only) Threat assessment — Moderate risk: this changes execution semantics in the build runner and the tests are too shallow for the concurrency hazards introduced. One-way doors
Findings
Missing
Verdict — Block: the speedup idea is reasonable, but this PR changes core execution semantics with insufficient correctness guarantees, insufficient test coverage, and obvious race surfaces outside the narrow lock scope. Review parameters
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Note Architectural Review — Claude Opus | Blind review (docs + diff only) Summary — This PR parallelizes independent layers at the same DAG level. It refactors the sequential build loop into Alignment — Fits well. The DAG structure already encodes independence — layers at the same level have no dependency edges between them. Parallelizing them is a natural optimization for the build-system model. Immutability of artifacts means concurrent production is safe in principle. Content-addressed storage and provenance chains are not altered by execution order. This advances the "expensive LLM transforms" concern from DESIGN.md §2.1 (dbt comparison) — parallelism directly reduces wall-clock time. Observations
Verdict — Good incremental step that exploits the DAG structure naturally, but the Review parameters
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- ThreadPoolExecutor workers capped at min(group_size, concurrency) to avoid oversubscription with nested transform parallelism - Search surfaces materialize AFTER all same-level layers complete, not per-layer — prevents exposing incomplete state when a surface depends on multiple layers at the same level
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Note Red Team Review — OpenAI GPT-5.4 | Adversarial review (docs + diff only) Threat assessment — Medium-high risk: this changes core build execution semantics with concurrency, but the tests only cover happy-path timing and miss correctness, determinism, and failure behavior. One-way doors
Findings
Missing
Verdict — Block: core execution semantics changed without enough correctness proof, and the current tests are too shallow to justify concurrent writes in the build engine. Review parameters
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Note Architectural Review — Claude Opus | Blind review (docs + diff only) Summary This PR introduces parallel execution of independent layers within the build runner. Layers at the same DAG level (no mutual dependencies) now run concurrently via Alignment Strong fit. The DAG is the foundational abstraction (DESIGN.md §3.1), and exploiting its structure for parallel execution is a natural build-system optimization — Bazel does the same thing. Layers remain immutable artifacts with provenance; the DAG topology determines what can parallelize. Content-addressed caching and materialization keys are untouched. This directly addresses the "LLM transforms are expensive" observation (§2.1) by reducing wall-clock time without changing semantics. Observations
Verdict Good incremental step that delivers meaningful wall-clock speedup while respecting the DAG structure, but the thread-safety gaps around Review parameters
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1 participant
Summary
Layers at the same DAG level are independent. The runner now groups layers by level and executes each group concurrently via ThreadPoolExecutor.
For a pipeline with 5 fold layers at level 2 (~30 min each), this reduces total build time from ~2.5 hours to ~30 minutes.
Changes
src/synix/build/runner.py: Extract_run_single_layer(), group by level, concurrent execution with_txn_lockfor snapshot thread safetytests/unit/test_parallel_layers.py: 4 tests — parallel timing, single-layer inline, dependency ordering, 5-layer concurrentTest plan