vikhry 0.1.1

Async distributed load-testing framework contracts and runtime

vikhry

vikhry is an async distributed load-testing framework built for high concurrency, horizontal scaling, and workloads that need globally unique resources.

It combines:

• a Python runtime for writing virtual-user scenarios;
• an orchestrator that manages test lifecycle and aggregates metrics;
• worker processes that execute VUs;
• Redis as the shared coordination layer;
• a built-in web UI served by the orchestrator.

Architecture

At a high level, vikhry has four runtime parts:

1. orchestrator Handles the test state machine, exposes HTTP/WebSocket APIs, serves the UI, and coordinates workers.
2. worker Runs VU tasks, processes orchestrator commands, publishes metrics, and manages acquired resources.
3. redis Stores shared state, worker presence, user assignments, resources, and metric streams.
4. ui A React frontend bundled into the Python package and served by the orchestrator.

Lifecycle flow:

IDLE -> PREPARING -> RUNNING -> STOPPING -> IDLE

Install and run

Install from PyPI:

pip install vikhry

Start local infrastructure:

vikhry infra up --worker-count 3 --scenario my_scenario:DemoVU

This command:

• checks that Docker is available;
• starts Redis in a Docker container;
• starts the orchestrator;
• starts the requested number of workers.

Open:

• UI: http://127.0.0.1:8080/
• API: http://127.0.0.1:8080

Stop everything:

vikhry infra down

Example test file

Create my_scenario.py:

from __future__ import annotations

from typing import Any

from vikhry import ReqwestClient, VU, between, resource, step


@resource(name="users")
async def create_user(resource_id: int | str, _ctx: object) -> dict[str, Any]:
    rid = str(resource_id)
    return {
        "resource_id": rid,
        "username": f"user_{rid}",
        "password": "password",
    }


class DemoVU(VU):
    http = ReqwestClient(timeout=5.0)

    async def on_init(self, base_url: str) -> None:
        self.http = self.http(base_url=base_url)

    async def on_start(self) -> None:
        self.user = await self.resources.acquire("users")

    async def on_stop(self) -> None:
        await self.resources.release("users", str(self.user["resource_id"]))

    @step(name="login", weight=1.0, every_s=between(10.0, 15.0), timeout=5.0)
    async def login(self) -> None:
        response = await self.http.post(
            "/auth",
            json={
                "username": self.user["username"],
                "password": self.user["password"],
            },
        )
        if response.status >= 400:
            raise RuntimeError(f"login returned HTTP {response.status}")

    @step(name="catalog", weight=3.0, requires=("login",), every_s=between(0.2, 1.0))
    async def catalog(self) -> None:
        response = await self.http.get("/catalog")
        if response.status >= 400:
            raise RuntimeError(f"catalog returned HTTP {response.status}")

Run it:

vikhry infra up --worker-count 3 --scenario my_scenario:DemoVU

Test authoring capabilities

vikhry scenarios are plain Python classes built on top of the VU DSL.

What you can define:

• VU.on_init(...) Accept runtime parameters for each VU instance.
• VU.on_start() / VU.on_stop() Allocate and release state or resources around the VU lifecycle.
• @step(...) Define executable load steps.
• @resource(name="...") Define global resource factories managed through Redis-backed pools.
• ReqwestClient Use an async HTTP client for relative or absolute requests.
• emit_metric(...) and @metric(...) Publish custom metrics in addition to automatic HTTP and step metrics.

Step controls:

• weight Weighted random scheduling between eligible steps.
• requires Declare prerequisites by step name.
• every_s Throttle step execution to a fixed or randomized interval.
• timeout Fail a step if it exceeds its maximum runtime.

Resource model:

• resource factories create globally tracked objects;
• workers acquire resources with self.resources.acquire(name);
• workers release them with self.resources.release(name, resource_id).

Packaging and release

The Python package includes the built UI assets.

Build locally:

./scripts/build_frontend.sh
uv build

Release automation:

• .github/workflows/release-artifacts.yml Builds the frontend, creates wheel and sdist, and publishes the package to PyPI using PYPI_TOKEN.
• .github/workflows/docker-image.yml Builds and publishes the runtime Docker image to ghcr.io/<owner>/<repo> on every branch push.

The package version is taken from project.version in pyproject.toml.