query-cascade 0.3.3

Minimal demand-driven query framework for incremental computation.

Cascade Query

Cascade Query is a Python library for incremental dependency tracking. It caches function results and re-executes them only when their specific inputs or upstream dependencies change.

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Core Principles

1. Automatic Caching: Results are stored. If dependencies are unchanged, the function body does not execute.
2. Dependency Tracking: Cascade records every @engine.input or @engine.query accessed during execution.
3. Targeted Updates: When an input changes, Cascade identifies and invalidates only the affected downstream functions.
4. Early Bail-out: If a function's output remains identical after its dependencies change, re-computation stops for that branch.

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Quickstart

import time
from cascade import Engine

engine = Engine()

@engine.input
def user_id():
    return "user_1"

@engine.query
def fetch_data():
    time.sleep(2) 
    return {"id": user_id(), "data": "value"}

@engine.query
def get_result():
    data = fetch_data()
    return f"Result for {data['id']}"

# First run: Executes for 2 seconds.
print(get_result())

# Second run: Returns immediately from cache.
print(get_result())

# Update input:
user_id.set("user_2")

# Third run: Executes for 2 seconds to refresh.
print(get_result())

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Engine API

Core Methods

• Engine(max_entries=10000, stats=False, cache_dir=None, cache_map_size=2**30): Initializes the engine. max_entries sets the limit for the Least Recently Used (LRU) cache. Passing cache_dir enables persistent disk caching (see below).
• @engine.input: Decorator for mutable data roots.
  • input.set(value): Updates the value and increments the global revision.
  • input.set(*args, value=value): Updates a keyed input.
• @engine.query: Decorator for cached computations.
• engine.snapshot(): Returns a Snapshot object pinning the current global revision. Use query(snapshot=s) to read data as it existed at that revision.
• engine.save(path) / engine.load(path): Persists all inputs and cached results to a SQLite database.
• engine.clear_disk_cache(): Deletes every entry in the persistent disk cache. Raises if the engine was created without cache_dir.

Parallel & Background Execution

• engine.compute_many(calls, workers=None): Executes a list of queries in parallel using a thread pool.
• engine.submit(query, *args, executor=None): Schedules a query for background execution. Returns a concurrent.futures.Future.
• QueryCancelled: Exception raised if a background query's dependencies change before it completes.

Graph Utilities

• engine.inspect_graph(): Returns a dictionary of all nodes and edges in the dependency graph.
• engine.subgraph(roots, direction="deps"): Filters the graph to the dependency chain of the specified root nodes.
• engine.prune(roots, vacuum_disk=False): Removes cached query results from the in-memory LRU cache that are not reachable from the specified roots. Set vacuum_disk=True to also do a deep vacuum of the persistent LMDB disk cache, deleting all orphaned blobs and metadata.

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Persistent Disk Caching

Passing cache_dir to the Engine turns on zero-config persistence. Cascade provisions an embedded LMDB store in that directory, serializes query results with a deterministic msgpack encoding, and fingerprints every input value by hashing its serialized bytes with blake2b. Nothing else changes: queries and inputs are written exactly as before.

from cascade import Engine

engine = Engine(max_entries=10_000, cache_dir=".cascade_cache")

@engine.input
def package_source_text(pkg: str) -> str:
    with open(pkg, "r") as f:
        return f.read()

@engine.query
def parsed_package_ast(pkg: str):
    return parse(package_source_text(pkg))

The first run executes normally and writes each result to disk. A later run in a new process starts with an empty in-memory cache, finds the entry on disk, and verifies it top-down: leaf inputs are re-executed and re-hashed (for the input above, that means re-reading the file), and the current hashes are compared against the fingerprints saved with the entry. If everything matches, the stored value is deserialized and returned without running any query body. If a file changed, its hash mismatches, and exactly the queries downstream of that file recompute. Early bail-out works across sessions too, since dependency fingerprints are content hashes: a whitespace-only edit that leaves an intermediate result unchanged will not recompute anything past it.

Accumulator effects are stored with each entry and replayed on disk hits, so a warning emitted in run 1 still appears in run 2 even when the query is served from disk.

lmdb and msgpack are required once cache_dir is set; there is no fallback, and the engine raises PersistentCacheError with install instructions if either is missing:

pip install query-cascade[disk]

A few things to know:

• Values and arguments must be serializable: primitives, bytes, list/tuple/set/frozenset/dict, @dataclass instances, and typing.NamedTuple instances. A query that returns anything else raises at compute time when persistence is on. A query called with an unserializable argument still computes and memoizes in memory, it just skips the disk.
• Cache addresses are derived from the function id (module:qualname) and the hashed arguments, so renaming or moving a function starts it from a cold cache. Editing a function body does not invalidate its entries; bump the cache with engine.clear_disk_cache() or delete the directory when query logic changes.
• The store supports concurrent access from multiple processes through LMDB's own locking. Within one process, engines sharing a cache_dir share one LMDB environment; the first opener's cache_map_size wins.
• The default cache_map_size is 1 GiB. LMDB allocates this lazily, so the file only grows as entries are written. If the cache fills up, PersistentCacheError explains the options.
• The on-disk data is a cache: clearing it is always safe and only costs recomputation. Cascade wipes it automatically when its own storage format version changes.

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Advanced Features

Side-Effect Accumulators

Queries must be pure functions. Use Accumulator to record side-effects (like logs or warnings) that must be replayed when a result is served from the cache.

warnings = engine.accumulator("warnings")

@engine.query
def validate_data():
    data = fetch_data()
    if not data:
        warnings.push("No data found")
    return data

# On cache hit, 'warnings' are re-populated into the effects dictionary.
effects = {}
validate_data(effects=effects)
print(effects["warnings"])

Performance Metrics

Set stats=True in the Engine constructor to track execution timing.

• engine.stats_summary(): Returns wall-clock time spent in function bodies and cache eviction counts.
• engine.reset_stats(): Clears accumulated timing data.

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Visualization

Cascade provides renderers for the dependency graph.

from cascade import export_dot, export_mermaid

graph = engine.inspect_graph()
# Generate Graphviz DOT format
dot_text = export_dot(graph)
# Generate Mermaid flowchart format
mermaid_text = export_mermaid(graph)

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Limitations

1. Cycle Detection: Cascade detects and rejects recursive function calls (cycles) with a CycleError.
2. Thread Safety: While Cascade supports parallel query execution, the Engine object itself should be modified (.set(), @engine.query) from a single thread or with external synchronization.
3. Persistence Security: engine.load() and the persistent disk cache resolve @dataclass and NamedTuple types via importlib. Only load databases or open cache directories from trusted sources.
4. Python Version: Optimization for parallel CPU-bound work requires CPython 3.14+ free-threaded builds with PYTHON_GIL=0.

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Installation

pip install query-cascade

Examples (in examples/)

Script	What it shows
compiler_pipeline.py	source → parse → symbols → typecheck, warnings accumulator, cache-hit narration
dynamic_macro_expansion.py	Query that changes downstream dependencies at runtime
snapshot_isolation.py	Snapshot reads while live inputs change
concurrent_background_work.py	Dedup under concurrency + cancellation after input changes
compute_many_with_accumulators.py	compute_many(..., effects=...) accumulator collection
persistence_and_inspection.py	Save/load and graph summaries
gil_parallel_speedup.py	Threaded CPU benchmark: GIL vs free-threaded

Run one:

python3.14t examples/compiler_pipeline.py

Run all (Unix-style shell):

for example in examples/*.py; do
  echo "Running $example"
  python3.14t "$example"
done

Examples print narration as they run so you can follow each behavior.

Compare GIL vs free-threaded (same machine)

Install both 3.14 and 3.14t if you want apples-to-apples. On Ubuntu (deadsnakes):

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update
sudo apt install -y python3.14 python3.14-venv python3.14t python3.14t-venv
python3.14 -m pip install -e .
python3.14t -m pip install -e .

Quick check on free-threaded build:

python3.14t -c "import sys, sysconfig; print('Py_GIL_DISABLED=', sysconfig.get_config_var('Py_GIL_DISABLED')); print('GIL enabled?', sys._is_gil_enabled())"

Same interpreter, toggle GIL at runtime:

PYTHON_GIL=1 python3.14t examples/gil_parallel_speedup.py --workers 8 --tasks 96 --rounds 300000 --repeats 5
PYTHON_GIL=0 python3.14t examples/gil_parallel_speedup.py --workers 8 --tasks 96 --rounds 300000 --repeats 5

Or compare python3.14 vs PYTHON_GIL=0 python3.14t on the same script.

Compare median parallel seconds (lower is better) and threaded speedup in this runtime (higher is better). Keep args identical, reduce background load, and use --repeats (e.g. 5) to smooth noise. On multi-core machines, free-threaded + GIL off usually wins clearly for this CPU-bound demo.

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Design stance

The core is intentionally minimal: pull-based evaluation, dependency capture, red/green style bailout, dedup, snapshots, cancellation, accumulator replay, tracing, and persistence. That set is enough for many real pipelines without baking in advanced internals (e.g. fixed-point cycle solving or custom AST red/green structures). CPU-bound parallelism is expected to matter when you use free-threaded CPython with the GIL disabled.

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Development

Tests (match main CI)

export PYTHON_GIL=0   # Windows: set PYTHON_GIL=0
python3.14t -m pip install -e ".[dev]"
python3.14t -c "import sys, sysconfig; print('Py_GIL_DISABLED=', sysconfig.get_config_var('Py_GIL_DISABLED')); print('GIL enabled?', sys._is_gil_enabled())"
python3.14t -m pytest -q \
  --ignore=tests/test_performance.py \
  --cov=src/cascade \
  --cov-branch \
  --cov-report=term-missing \
  --cov-fail-under=95

Branch coverage check (CI uses an equivalent step on coverage.json):

python3.14t - <<'PY'
import json
with open("coverage.json", encoding="utf-8") as fh:
    b = json.load(fh)["totals"]["percent_branches_covered"]
print(f"branch coverage: {b:.2f}%")
assert b >= 90.0
PY

Stateful fuzz:

PYTHON_GIL=0 python3.14t -m pytest -q tests/test_stateful_engine_invariants.py

Mutation testing:

PATH="$HOME/.local/bin:$PATH" PYTHON_GIL=0 mutmut run
PATH="$HOME/.local/bin:$PATH" PYTHON_GIL=0 mutmut results

Use the mutmut CLI (mutmut run), not python -m mutmut run. Bounded local loop:

PYTHON_GIL=0 MUTMUT_MAX_CHILDREN=2 ./scripts/mutation_fast.sh

Focused mutants:

PYTHON_GIL=0 MUTMUT_MAX_CHILDREN=2 ./scripts/mutation_fast.sh "<mutant-name>" "<mutant-name>"

See docs/mutation_triage.md for survivor triage.

Formal model (TLA+)

Specs live under docs/formal/:

• docs/formal/cascade_core.tla
• docs/formal/cascade_core.cfg

Run TLC (example):

java -cp tla2tools.jar tlc2.TLC docs/formal/cascade_core.tla -config docs/formal/cascade_core.cfg

Checked properties include snapshot consistency, active-dependency validity (red/green alignment), and cancellation epoch monotonicity.

Performance suite

Heavy behavior clusters around cache hits vs full recompute, concurrent dedup, compute_many throughput on free-threaded workloads, large-graph mutation vs rebuild, mark-green cost vs depth, and prune scaling.

python -m benchmarks.performance_suite --report-dir artifacts/performance --assert-thresholds

Outputs:

• artifacts/performance/performance-report.json
• artifacts/performance/performance-report.md

CI runs the same suite and uploads performance-report.

The compute-many-parallel-speedup scenario (and tests/test_performance.py::test_compute_many_parallel_speedup_scenario) is sensitive to CPU scheduling. On a busy laptop or small VM, thresholds may flap without a real regression. Mitigations:

• Re-run the test, or set CASCADE_QUERY_PARALLEL_PERF_RETRIES (e.g. 3).
• To skip while iterating: CASCADE_QUERY_SKIP_PARALLEL_PERF=1 (CI does not set this).

Nightly: .github/workflows/nightly-performance.yml runs a longer sweep (e.g. 8 runs) and publishes nightly-performance-report.

Scale and stress tests

tests/test_scale_behavior.py covers large-graph invalidation, dynamic dependency churn, prune stress, persistence at scale, eviction under churn, and mixed concurrency (submit + compute_many + writes). The heaviest cases are marked @pytest.mark.slow; default pytest skips them via pyproject.toml.

Internal invariants are concentrated in tests/test_internal_invariants.py (via engine._internals) to limit coupling while keeping safety checks.

Default CI-like run (no perf file, no slow):

PYTHON_GIL=0 python3.14t -m pytest -q --ignore=tests/test_performance.py

Slow only:

pytest -q -m slow

Everything including slow:

pytest -q -m "slow or not slow"

CI overview

• Workflow: .github/workflows/ci.yml (pushes and PRs).
• Ruff before tests.
• Separate package build (python -m build) to catch packaging issues early.