barca 0.1.5

Invisible asset orchestrator

barca

The invisible asset orchestrator.
Rust plans it. Python runs it. You just write functions.

---

Barca is an asset orchestrator that adds zero perceptible overhead to your Python pipelines. A compiled Rust binary handles parsing, DAG construction, and execution planning. Python does what it's best at: running your code.

# pipeline.py
from barca import asset

@asset()
def raw_data() -> list[dict]:
    return [{"x": 1}, {"x": 2}, {"x": 3}]

@asset(inputs={"data": raw_data})
def summary(data: list[dict]) -> dict:
    return {"count": len(data), "total": sum(d["x"] for d in data)}

$ barca get pipeline.py
{"elapsed_seconds":0.042,"steps_executed":2,"phases":2,"final_output":{"count":3,"total":6}}

No config files. No YAML. No daemon. Just functions and a fast binary.

Install

Barca is designed for use with uv:

uv add barca

This gives you:

• The barca CLI binary (compiled Rust)
• Python API: barca.get(), barca.plan()
• Decorator stubs for @asset, @sensor, @effect (IDE autocomplete + type checking)

For optional parquet (DataFrame) support:

uv add 'barca[parquet]'

All in one wheel, built with maturin. Requires Python >= 3.12.

From source

git clone https://github.com/ExSidius/barca.git
cd barca
uv sync
cargo build --release
maturin develop --release    # installs into current .venv

Quick start

# assets.py
from barca import asset

@asset()
def hello() -> dict:
    return {"message": "Hello from barca!"}

barca get assets.py

That's it. Barca parses your Python source with ruff's AST parser (no import, pure static analysis), builds a dependency graph, generates a phased execution plan, spawns Python workers, and persists results to a local SQLite database -- all in under 40ms for a trivial asset.

How it works

                    ┌─────────────────────────────────────┐
                    │          barca get pipeline.py       │
                    └──────────────┬──────────────────────┘
                                   │
                    ┌──────────────▼──────────────────────┐
                    │         Rust binary (barca)          │
                    │                                      │
                    │  1. Parse Python source (ruff AST)   │
                    │  2. Build DAG (petgraph)              │
                    │  3. Generate execution plan           │
                    │  4. Initialize DB (.barca/metadata.db)│
                    │  5. Spawn Python workers per phase    │
                    │  6. Collect outputs, persist to DB    │
                    └──────────────┬──────────────────────┘
                                   │
                    ┌──────────────▼──────────────────────┐
                    │      Python worker (per phase)       │
                    │                                      │
                    │  - Loads modules via importlib        │
                    │  - Executes steps in tier order       │
                    │  - LRU cache for in-process results   │
                    │  - Emits JSON lines to stdout         │
                    └─────────────────────────────────────┘

Key design decisions:

• Static analysis only -- Rust never imports your Python code. It parses source text and extracts decorator metadata from the AST.
• Phased execution -- The planner decomposes the DAG into sequential phases. Within each phase, independent streams run in parallel workers.
• No framework lock-in -- Decorators are identity functions. Your code runs standalone without barca installed.
• Single binary -- One pip install gives you everything. No JVM, no Docker, no scheduler service.

Decorators

from barca import asset, sensor, effect, sink, unsafe
from barca import Always, Manual, Schedule
from barca import partitions, partitions_from, collect, asset_ref

@asset

Cached computation node. The workhorse.

@asset()
def prices() -> dict:
    return {"AAPL": 150, "MSFT": 380}

@asset(inputs={"data": prices})
def report(data: dict) -> str:
    return f"Tracked {len(data)} tickers"

@sensor

Observes external state. Returns (update_detected, output).

@sensor()
def inbox_files() -> tuple[bool, list[str]]:
    files = list(Path("inbox").glob("*.csv"))
    return bool(files), [str(f) for f in files]

@effect

Side-effect leaf node. Never cached, can't be used as input.

@effect(inputs={"report": report})
def publish(report: str) -> None:
    print(f"Publishing: {report}")

@sink

Stacks on @asset to write outputs to files.

@asset()
@sink("output/data.json", serializer="json")
def my_data() -> dict:
    return {"rows": 42}

Freshness markers

Marker	Behavior
Always	Auto-materializes whenever stale (default for @effect)
Manual	Only runs on explicit refresh
Schedule("0 5 * * *")	Cron expression

Partitions

Fan a single asset definition into N independent materializations:

@asset(partitions={"ticker": partitions(["AAPL", "MSFT", "GOOG"])})
def prices(ticker: str) -> dict:
    return {"ticker": ticker, "price": get_price(ticker)}

Function	Purpose
partitions(values)	Static list of partition keys
partitions_from(source)	Derive partitions from upstream asset
collect(asset_fn)	Aggregate all partitions of an upstream
asset_ref(ref_string)	Canonical asset reference

CLI

barca get [target] <file.py> [file.py ...] Get asset(s) — cache-aware
barca plan <file.py> [file.py ...]         Emit execution plan as JSON
barca --help                               Show help

Shorthand: barca pipeline.py works as barca get pipeline.py (all assets).

Python API

import barca

# Get all assets in a file (returns the last asset's value)
value = barca.get("pipeline.py")
print(value)  # {"count": 3, "total": 6}

# Get a specific asset's value (cache-aware)
value = barca.get("summary", "pipeline.py")
print(value)  # {"count": 3, "total": 6}

# Inspect the execution plan
plan = barca.plan("pipeline.py")
print(plan["total_steps"])  # 2

All output formats work transparently: dicts, lists, sets, DataFrames, and arbitrary Python objects are serialized as JSON, pickle, or parquet and deserialized automatically.

barca plan -- inspect without running

$ barca plan pipeline.py
{
  "total_steps": 2,
  "phases": [
    { "reason": "Independent", "streams": [{"stream_id": 0, "steps": ["raw_data"]}] },
    { "reason": "Dependent",   "streams": [{"stream_id": 1, "steps": ["summary"]}] }
  ]
}

barca get -- execute and get results

Parses source, builds DAG, spawns workers, collects outputs, persists to .barca/metadata.db. With a target, only the target's subgraph runs. Without a target, all assets run.

Output is a JSON summary:

{
  "elapsed_seconds": 0.042,
  "steps_executed": 2,
  "phases": 2,
  "final_output": {"count": 3, "total": 6}
}

Use --no-cache to skip cache lookups and execute everything fresh.

Diagnostics go to stderr:

[barca] 2 nodes, 1 edges, 2 phases, 2 streams | plan: 1.2ms | exec: 38ms | total: 40ms

Benchmarks

All benchmarks measured with hyperfine (3 warmup runs, 10 measured runs) on the same machine. Barca is compared against Dagster and Prefect running equivalent pipelines.

Trivial (1 asset, zero work)

Measures pure framework overhead -- how long it takes to do nothing.

Framework	Mean	Relative
barca	38.0 ms	1.00x
dagster	538.1 ms	14.2x
prefect	3977.7 ms	104.7x

Barca's total overhead (parse + plan + spawn + persist) is 38ms. Dagster needs ~0.5s. Prefect needs ~4s.

Benchmark suite

The benchmarks/ directory contains 12 scenarios covering a range of DAG topologies and workloads:

Benchmark	Assets	Topology	What it tests
trivial	1	single node	Pure framework overhead
chain_100	100	linear chain	Sequential dependency resolution
fan_out_500	500	flat (independent)	Wide parallelism, process spawning
fan_out_500_50ms	500	flat + 50ms sleep	Parallelism under I/O latency
deep_diamond	18	diamond (5-wide, 6-deep)	Fan-out/fan-in patterns
wide_layers	varies	parallel layers	Tier-based parallel execution
large_payloads	varies	varied	JSON serialization overhead
map_reduce	varies	map-reduce	Scatter-gather pattern
mixed_io_cpu	varies	varied	Mixed I/O and CPU workloads
multi_file_discovery	varies	multi-file	Cross-file asset discovery
iris_pipeline	varies	diamond	ML pipeline (iris dataset)
spaceflights	10	diamond (3-wide, 6-deep)	Full ML pipeline (Kedro-style)

Run any benchmark:

cd benchmarks/trivial
./bench.sh 10    # 10 measured runs

Each benchmark includes equivalent Dagster and Prefect implementations for apples-to-apples comparison.

Architecture

Cargo.toml                  Rust workspace root
crates/
  barca-core/               Engine: parser, DAG, planner, dispatch, DB, cache
  barca-cli/                Thin CLI shell (clap → barca-core)
python/barca/
  __init__.py               Decorator stubs + API exports
  api.py                    Python API (get/plan via subprocess)
  _worker.py                Execution worker (invoked by Rust binary)
  _artifacts.py             Artifact serialization (json/pickle/parquet)
  py.typed                  PEP 561 marker
pyproject.toml              Maturin build config

Tech stack

Layer	Technology
Parser	ruff Python AST (static, no import)
DAG	petgraph
Database	Turso/libSQL (local SQLite)
Serialization	serde + serde_json
Hashing	SHA-256 (content-addressed artifacts)
Build	maturin (Rust binary + Python stubs in one wheel)
Python runtime	Python >= 3.12

Node kinds

Kind	Decorator	Cached	Can be input
asset	@asset()	Yes	Yes
sensor	@sensor()	No	Yes
effect	@effect()	No	No (leaf)

Development

git clone https://github.com/recursia-io/barca.git
cd barca

# Build
cargo build --release
maturin develop --release

# Test
cargo test

# Run
barca get examples/basic_app/example_project/assets.py
barca plan examples/basic_app/example_project/assets.py

Project status

Barca is in active development. The core pipeline (parse -> DAG -> plan -> execute -> persist) is working and benchmarked. See the guide for a walkthrough.

License

MIT