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This guide covers everything you need to create your own pipeline steps — from the minimal contract to advanced patterns like dependency injection, async execution, and testing.
---
## The step contract
Any Python object with `requires`, `provides`, and `__call__` is a valid step. No base class needed.
```python
from types import MappingProxyType
from pipeline import StepContext
class MyStep:
requires = frozenset({"input_field"})
provides = frozenset({"output_field"})
def __call__(self, ctx: StepContext) -> StepContext:
result = process(ctx.metadata["input_field"])
return ctx.replace(
metadata=MappingProxyType({**ctx.metadata, "output_field": result})
)
```
Rules:
- `requires` and `provides` can be `set` or `frozenset` — the pipeline normalizes to `frozenset`
- `__call__` receives a `StepContext` and must return a `StepContext`
- Never mutate the incoming context — always use `.replace()`
---
## Sync vs async steps
=== "Sync"
```python
class ComputeStep:
requires = frozenset({"data"})
provides = frozenset({"result"})
def __call__(self, ctx: StepContext) -> StepContext:
result = expensive_computation(ctx.metadata["data"])
return ctx.replace(
metadata=MappingProxyType({**ctx.metadata, "result": result})
)
```
=== "Async"
```python
class FetchStep:
requires = frozenset({"url"})
provides = frozenset({"response"})
async def __call__(self, ctx: StepContext) -> StepContext:
async with aiohttp.ClientSession() as session:
resp = await session.get(ctx.metadata["url"])
data = await resp.json()
return ctx.replace(
metadata=MappingProxyType({**ctx.metadata, "response": data})
)
```
Use async steps for I/O-bound work (HTTP requests, API calls, file I/O). The pipeline detects and handles both transparently.
---
## Dependency injection
Steps that need external collaborators receive them via `__init__`. The `__call__` method stays stateless — it only uses `self.*` for injected dependencies and `ctx` for data.
```python
class ScoringStep:
requires = frozenset({"predictions"})
provides = frozenset({"scores"})
def __init__(self, scorer, threshold: float = 0.5):
self.scorer = scorer
self.threshold = threshold
def __call__(self, ctx: StepContext) -> StepContext:
raw_scores = self.scorer.evaluate(ctx.metadata["predictions"])
filtered = {k: v for k, v in raw_scores.items() if v >= self.threshold}
return ctx.replace(
metadata=MappingProxyType({**ctx.metadata, "scores": filtered})
)
```
This makes testing easy — inject mocks:
```python
pipe = Pipeline().then(ScoringStep(scorer=mock_scorer, threshold=0.8))
```
---
## Declaring concurrency
Two optional class attributes control how a step participates in concurrent execution:
### `async_boundary`
Marks the foreground/background split point. Everything from this step onward runs in a background thread:
```python
class AnalyzeStep:
requires = frozenset({"data"})
provides = frozenset({"analysis"})
async_boundary = True # background from here
def __call__(self, ctx: StepContext) -> StepContext: ...
```
See [Execution Model — Async Boundary](execution.md#async-boundary-fire-and-forget-background) for details.
### `max_workers`
Controls the per-step-class thread pool size for background execution:
```python
class ParallelAnalyzeStep:
requires = frozenset({"data"})
provides = frozenset({"analysis"})
async_boundary = True
max_workers = 4 # up to 4 concurrent analyses
def __call__(self, ctx: StepContext) -> StepContext: ...
```
Default is `max_workers = 1` (serialized).
!!! warning
Steps that write shared state (e.g. updating an external database or accumulating results into a shared object) must use `max_workers = 1` to avoid race conditions.
---
## Subclassing StepContext
When `metadata` becomes unwieldy, subclass `StepContext` to add named fields:
```python
from dataclasses import dataclass
@dataclass(frozen=True)
class MLContext(StepContext):
predictions: list | None = None
scores: dict | None = None
report: str | None = None
```
Steps write to named fields using `.replace()`:
```python
class PredictStep:
requires = frozenset()
provides = frozenset({"predictions"})
def __init__(self, model):
self.model = model
def __call__(self, ctx: MLContext) -> MLContext:
preds = self.model.predict(ctx.sample)
return ctx.replace(predictions=preds)
```
!!! tip "When to subclass"
- **Named fields**: Data shared across multiple steps that benefits from type checking
- **Metadata**: Step-specific or integration-specific transient data (e.g. `metadata["cache_key"]`)
The `requires`/`provides` validation works on attribute names, so it's subclass-agnostic. A step declaring `requires = {"predictions"}` works with any context subclass that has a `predictions` attribute.
---
## Testing steps
### Unit test — step in isolation
```python
from types import MappingProxyType
from pipeline import StepContext
def test_tokenize_splits_words():
step = Tokenize()
ctx = StepContext(sample="hello world")
result = step(ctx)
assert result.metadata["tokens"] == ["hello", "world"]
assert result.metadata["word_count"] == 2
def test_uppercase_transforms_tokens():
step = Uppercase()
ctx = StepContext(
metadata=MappingProxyType({"tokens": ["hello", "world"]})
)
result = step(ctx)
assert result.metadata["upper_tokens"] == ["HELLO", "WORLD"]
```
### Protocol compliance
```python
from pipeline import StepProtocol
def test_step_satisfies_protocol():
step = Tokenize()
assert isinstance(step, StepProtocol)
assert hasattr(step, "requires")
assert hasattr(step, "provides")
assert callable(step)
```
### Pipeline integration test
```python
from pipeline import Pipeline, StepContext
def test_full_pipeline():
pipe = Pipeline().then(Tokenize()).then(Uppercase())
results = pipe.run([StepContext(sample="hello world")])
assert len(results) == 1
assert results[0].error is None
assert results[0].output.metadata["upper_tokens"] == ["HELLO", "WORLD"]
```
---
## Common patterns
### Map-reduce step
A step that internally fans out to multiple sub-inputs:
```python
class MultiSearchStep:
requires = frozenset()
provides = frozenset({"search_results"})
def __call__(self, ctx: StepContext) -> StepContext:
queries = generate_queries(ctx.sample) # 1 → N
sub_ctxs = [StepContext(sample=q) for q in queries]
sub_pipe = Pipeline().then(FetchStep())
results = sub_pipe.run(sub_ctxs, workers=len(queries)) # parallel
merged = merge_results(results) # N → 1
return ctx.replace(
metadata=MappingProxyType({**ctx.metadata, "search_results": merged})
)
```
From the outer pipeline's perspective, this is a black box that takes one context and returns one.
### Logging / observability step
A pass-through step that logs without modifying data:
```python
class LogStep:
requires = frozenset()
provides = frozenset()
def __init__(self, logger):
self.logger = logger
def __call__(self, ctx: StepContext) -> StepContext:
self.logger.info(f"Processing sample: {ctx.sample}")
self.logger.debug(f"Metadata keys: {list(ctx.metadata.keys())}")
return ctx # pass through unchanged
```
### Retry wrapper
A step that wraps another step with retry logic:
```python
import time
class RetryStep:
def __init__(self, inner, max_retries: int = 3, delay: float = 1.0):
self.inner = inner
self.max_retries = max_retries
self.delay = delay
self.requires = inner.requires
self.provides = inner.provides
def __call__(self, ctx: StepContext) -> StepContext:
for attempt in range(self.max_retries):
try:
return self.inner(ctx)
except Exception:
if attempt == self.max_retries - 1:
raise
time.sleep(self.delay * (attempt + 1))
```
Usage:
```python
pipe = Pipeline().then(RetryStep(FlakyAPIStep(), max_retries=3))
```
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