duroxide 0.1.14

Python SDK for the Duroxide durable execution runtime

duroxide-python

Python SDK for the Duroxide durable execution runtime.

Write durable workflows as Python generators. The Rust runtime handles replay, persistence, and fault tolerance.

Features

• Generator-based orchestrations — yield task descriptors, Rust handles DurableFutures
• Activities — regular Python functions for side effects (I/O, network calls)
• Timers — durable delays that survive process restarts
• Events — wait for external signals
• Sub-orchestrations — compose workflows hierarchically
• Fan-out/Fan-in — ctx.all() for parallel execution, ctx.race() for first-to-complete
• Continue-as-new — long-running orchestrations with bounded history
• Deterministic replay — safe resume after crashes
• SQLite & PostgreSQL — pluggable storage providers
• Custom Status — ctx.set_custom_status() / ctx.reset_custom_status() for orchestration progress reporting, client.wait_for_status_change() for efficient polling
• KV Store — durable per-instance state via ctx.set_value() / ctx.get_value() / ctx.clear_value() / ctx.clear_all_values(), plus client.get_value() / client.wait_for_value()
• Event Queues — ctx.dequeue_event(queue_name) for FIFO mailbox-style message passing, client.enqueue_event() to send messages
• Retry on Session — ctx.schedule_activity_with_retry_on_session() for retry with session affinity
• Tag Routing — worker tags for activity affinity (MAX_WORKER_TAGS=5, MAX_TAG_NAME_BYTES=256, MAX_KV_KEYS=10, MAX_KV_VALUE_BYTES=16384)
• Admin APIs — instance management, metrics, pruning
• Activity client access — ctx.get_client() lets activities start new orchestrations
• Runtime metrics — metrics_snapshot() for orchestration/activity counters

Installation

pip install duroxide

Quick Start

from duroxide import SqliteProvider, Client, Runtime

# Create provider and runtime
provider = SqliteProvider.in_memory()
runtime = Runtime(provider)

# Register an activity
@runtime.register_activity("greet")
def greet(ctx, input):
    return f"Hello, {input['name']}!"

# Register an orchestration (generator function)
@runtime.register_orchestration("GreetWorkflow")
def greet_workflow(ctx, input):
    result = yield ctx.schedule_activity("greet", input)
    return result

# Start runtime and run orchestration
import threading
runtime.start()

client = Client(provider)
client.start_orchestration("greet-1", "GreetWorkflow", {"name": "World"})
status = client.wait_for_orchestration("greet-1", 10000)
print(status.output)  # "Hello, World!"

runtime.shutdown()

Orchestrations

Orchestrations are Python generator functions. They must be deterministic — no I/O, no randomness, no time.time(). Use only ctx.* methods for side effects.

@runtime.register_orchestration("MyWorkflow")
def my_workflow(ctx, input):
    # Schedule activities
    result = yield ctx.schedule_activity("DoWork", input)

    # Fan-out / Fan-in
    results = yield ctx.all([
        ctx.schedule_activity("TaskA", {"id": 1}),
        ctx.schedule_activity("TaskB", {"id": 2}),
    ])

    # Timer
    yield ctx.schedule_timer(5000)  # 5 seconds

    # Wait for external event
    approval = yield ctx.wait_for_event("approval")

    # Sub-orchestration
    sub_result = yield ctx.schedule_sub_orchestration("SubWorkflow", input)

    # Race (first to complete wins)
    winner = yield ctx.race(
        ctx.schedule_activity("Fast", None),
        ctx.schedule_timer(10000),
    )

    # Custom status (fire-and-forget, no yield)
    ctx.set_custom_status("processing complete")

    # Dequeue from event queue (FIFO, blocks until message available)
    msg = yield ctx.dequeue_event("inbox")

    return {"result": result, "winner": winner}

Activities

Activities are regular Python functions that perform side effects. They run outside the replay engine and are safe for I/O operations.

@runtime.register_activity("SendEmail")
def send_email(ctx, input):
    ctx.trace_info(f"Sending email to {input['to']}")
    # ... actual email sending ...
    return {"sent": True}

PostgreSQL Provider

from duroxide import PostgresProvider, Client, Runtime

provider = PostgresProvider.connect("postgresql://user:pass@localhost:5432/mydb")
# or with custom schema:
provider = PostgresProvider.connect_with_schema("postgresql://...", "duroxide_python")

runtime = Runtime(provider)
client = Client(provider)

Admin APIs

client = Client(provider)

# Metrics
metrics = client.get_system_metrics()
depths = client.get_queue_depths()

# Instance management
instances = client.list_all_instances()
info = client.get_instance_info("instance-1")
tree = client.get_instance_tree("instance-1")

# Execution history with full event data
executions = client.list_executions("instance-1")
events = client.read_execution_history("instance-1", executions[0])
for event in events:
    print(event.kind, event.data)
    # event.kind: "OrchestrationStarted" | "ActivityCompleted" | ...
    # event.data: JSON string with event-specific content (result, input, error, etc.)

# Cleanup
client.delete_instance("instance-1", force=True)
client.prune_executions("instance-1", PyPruneOptions(keep_last=5))

Custom Status

Report orchestration progress visible to external clients:

@runtime.register_orchestration("ProgressWorkflow")
def progress_workflow(ctx, input):
    ctx.set_custom_status("step 1: validating")
    yield ctx.schedule_activity("Validate", input)

    ctx.set_custom_status("step 2: processing")
    result = yield ctx.schedule_activity("Process", input)

    ctx.reset_custom_status()  # clear status
    return result

# Poll for status changes from outside
status = client.wait_for_status_change("instance-1", 0, 50, 10000)
if status:
    print(status.custom_status)          # "step 1: validating"
    print(status.custom_status_version)  # monotonically increasing counter

KV Store

Durable per-instance key-value state for orchestration coordination and request/response patterns:

@runtime.register_orchestration("KvWorkflow")
def kv_workflow(ctx, input):
    ctx.set_value("status", "running")
    result = yield ctx.schedule_activity("Compute", input)
    ctx.set_value("result", str(result))
    return result

# External reads
status = client.wait_for_value("instance-1", "status", 10000)
result = client.get_value("instance-1", "result")

KV entries are scoped to a single orchestration instance and remain readable after completion until the instance is deleted or pruned.

Event Queues

Persistent FIFO message passing between clients and orchestrations:

@runtime.register_orchestration("ChatBot")
def chat_bot(ctx, input):
    msg_json = yield ctx.dequeue_event("inbox")
    msg = json.loads(msg_json)
    response = yield ctx.schedule_activity("Generate", msg["text"])
    ctx.set_custom_status(json.dumps({"state": "replied", "response": response, "seq": msg["seq"]}))
    if "bye" in msg["text"].lower():
        return f"Done after {msg['seq']} msgs"
    return (yield ctx.continue_as_new(""))

# Send messages from outside
client.enqueue_event(instance_id, "inbox", json.dumps({"seq": 1, "text": "Hello!"}))
status = client.wait_for_status_change(instance_id, 0, 50, 10000)
reply = json.loads(status.custom_status)

Development

# Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install build tools and test dependencies
pip install maturin pytest

# Build the native extension and install in development mode
maturin develop

# Run all 59 tests
pytest

# Run tests with verbose output
pytest -v

# Run a single test file
pytest tests/test_e2e.py -v

# Run a single test
pytest tests/test_e2e.py::test_hello_world

# Stop on first failure
pytest -v -x

# Build release wheel
maturin build --release

After Rust source changes (src/*.rs), re-run maturin develop to rebuild. Python-only changes (python/duroxide/, tests/) take effect immediately.

Changelog

See CHANGELOG.md for release notes.

Documentation

• User Guide — orchestration patterns, activities, providers, tracing, determinism rules
• Architecture — PyO3 interop, GIL deadlock fix, generator driver, tracing internals

License

MIT