Vendor DataLoader from aiodataloader and also move get_event_loop behavior from __init__ to a property which only gets resolved when actually needed (this will solve PyTest-related early get_event_loop() issues)

Author: flipbit03

graphene/utils/dataloader.py

@@ -0,0 +1,282 @@
+from asyncio import (
+    gather,
+    ensure_future,
+    get_event_loop,
+    iscoroutine,
+    iscoroutinefunction,
+)
+from collections import namedtuple
+from collections.abc import Iterable
+from functools import partial
+
+from typing import List  # flake8: noqa
+
+Loader = namedtuple("Loader", "key,future")
+
+
+def iscoroutinefunctionorpartial(fn):
+    return iscoroutinefunction(fn.func if isinstance(fn, partial) else fn)
+
+
+class DataLoader(object):
+    batch = True
+    max_batch_size = None  # type: int
+    cache = True
+
+    def __init__(
+        self,
+        batch_load_fn=None,
+        batch=None,
+        max_batch_size=None,
+        cache=None,
+        get_cache_key=None,
+        cache_map=None,
+        loop=None,
+    ):
+
+        # Create empty _loop which will be populated with asyncio's event loop as soon as it's needed.
+        self._loop = None
+
+        if batch_load_fn is not None:
+            self.batch_load_fn = batch_load_fn
+
+        assert iscoroutinefunctionorpartial(
+            self.batch_load_fn
+        ), "batch_load_fn must be coroutine. Received: {}".format(self.batch_load_fn)
+
+        if not callable(self.batch_load_fn):
+            raise TypeError(
+                (
+                    "DataLoader must be have a batch_load_fn which accepts "
+                    "Iterable<key> and returns Future<Iterable<value>>, but got: {}."
+                ).format(batch_load_fn)
+            )
+
+        if batch is not None:
+            self.batch = batch
+
+        if max_batch_size is not None:
+            self.max_batch_size = max_batch_size
+
+        if cache is not None:
+            self.cache = cache
+
+        self.get_cache_key = get_cache_key or (lambda x: x)
+
+        self._cache = cache_map if cache_map is not None else {}
+        self._queue = []  # type: List[Loader]
+
+    @property
+    def loop(self):
+        if not self._loop:
+            self._loop = get_event_loop()
+
+        return self._loop
+
+    def load(self, key=None):
+        """
+        Loads a key, returning a `Future` for the value represented by that key.
+        """
+        if key is None:
+            raise TypeError(
+                (
+                    "The loader.load() function must be called with a value, "
+                    "but got: {}."
+                ).format(key)
+            )
+
+        cache_key = self.get_cache_key(key)
+
+        # If caching and there is a cache-hit, return cached Future.
+        if self.cache:
+            cached_result = self._cache.get(cache_key)
+            if cached_result:
+                return cached_result
+
+        # Otherwise, produce a new Future for this value.
+        future = self.loop.create_future()
+        # If caching, cache this Future.
+        if self.cache:
+            self._cache[cache_key] = future
+
+        self.do_resolve_reject(key, future)
+        return future
+
+    def do_resolve_reject(self, key, future):
+        # Enqueue this Future to be dispatched.
+        self._queue.append(Loader(key=key, future=future))
+        # Determine if a dispatch of this queue should be scheduled.
+        # A single dispatch should be scheduled per queue at the time when the
+        # queue changes from "empty" to "full".
+        if len(self._queue) == 1:
+            if self.batch:
+                # If batching, schedule a task to dispatch the queue.
+                enqueue_post_future_job(self.loop, self)
+            else:
+                # Otherwise dispatch the (queue of one) immediately.
+                dispatch_queue(self)
+
+    def load_many(self, keys):
+        """
+        Loads multiple keys, returning a list of values
+
+        >>> a, b = await my_loader.load_many([ 'a', 'b' ])
+
+        This is equivalent to the more verbose:
+
+        >>> a, b = await gather(
+        >>>    my_loader.load('a'),
+        >>>    my_loader.load('b')
+        >>> )
+        """
+        if not isinstance(keys, Iterable):
+            raise TypeError(
+                (
+                    "The loader.load_many() function must be called with Iterable<key> "
+                    "but got: {}."
+                ).format(keys)
+            )
+
+        return gather(*[self.load(key) for key in keys])
+
+    def clear(self, key):
+        """
+        Clears the value at `key` from the cache, if it exists. Returns itself for
+        method chaining.
+        """
+        cache_key = self.get_cache_key(key)
+        self._cache.pop(cache_key, None)
+        return self
+
+    def clear_all(self):
+        """
+        Clears the entire cache. To be used when some event results in unknown
+        invalidations across this particular `DataLoader`. Returns itself for
+        method chaining.
+        """
+        self._cache.clear()
+        return self
+
+    def prime(self, key, value):
+        """
+        Adds the provied key and value to the cache. If the key already exists, no
+        change is made. Returns itself for method chaining.
+        """
+        cache_key = self.get_cache_key(key)
+
+        # Only add the key if it does not already exist.
+        if cache_key not in self._cache:
+            # Cache a rejected future if the value is an Error, in order to match
+            # the behavior of load(key).
+            future = self.loop.create_future()
+            if isinstance(value, Exception):
+                future.set_exception(value)
+            else:
+                future.set_result(value)
+
+            self._cache[cache_key] = future
+
+        return self
+
+
+def enqueue_post_future_job(loop, loader):
+    async def dispatch():
+        dispatch_queue(loader)
+
+    loop.call_soon(ensure_future, dispatch())
+
+
+def get_chunks(iterable_obj, chunk_size=1):
+    chunk_size = max(1, chunk_size)
+    return (
+        iterable_obj[i : i + chunk_size]
+        for i in range(0, len(iterable_obj), chunk_size)
+    )
+
+
+def dispatch_queue(loader):
+    """
+    Given the current state of a Loader instance, perform a batch load
+    from its current queue.
+    """
+    # Take the current loader queue, replacing it with an empty queue.
+    queue = loader._queue
+    loader._queue = []
+
+    # If a max_batch_size was provided and the queue is longer, then segment the
+    # queue into multiple batches, otherwise treat the queue as a single batch.
+    max_batch_size = loader.max_batch_size
+
+    if max_batch_size and max_batch_size < len(queue):
+        chunks = get_chunks(queue, max_batch_size)
+        for chunk in chunks:
+            ensure_future(dispatch_queue_batch(loader, chunk))
+    else:
+        ensure_future(dispatch_queue_batch(loader, queue))
+
+
+async def dispatch_queue_batch(loader, queue):
+    # Collect all keys to be loaded in this dispatch
+    keys = [loaded.key for loaded in queue]
+
+    # Call the provided batch_load_fn for this loader with the loader queue's keys.
+    batch_future = loader.batch_load_fn(keys)
+
+    # Assert the expected response from batch_load_fn
+    if not batch_future or not iscoroutine(batch_future):
+        return failed_dispatch(
+            loader,
+            queue,
+            TypeError(
+                (
+                    "DataLoader must be constructed with a function which accepts "
+                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
+                    "not return a Coroutine: {}."
+                ).format(batch_future)
+            ),
+        )
+
+    try:
+        values = await batch_future
+        if not isinstance(values, Iterable):
+            raise TypeError(
+                (
+                    "DataLoader must be constructed with a function which accepts "
+                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
+                    "not return a Future of a Iterable: {}."
+                ).format(values)
+            )
+
+        values = list(values)
+        if len(values) != len(keys):
+            raise TypeError(
+                (
+                    "DataLoader must be constructed with a function which accepts "
+                    "Iterable<key> and returns Future<Iterable<value>>, but the function did "
+                    "not return a Future of a Iterable with the same length as the Iterable "
+                    "of keys."
+                    "\n\nKeys:\n{}"
+                    "\n\nValues:\n{}"
+                ).format(keys, values)
+            )
+
+        # Step through the values, resolving or rejecting each Future in the
+        # loaded queue.
+        for loaded, value in zip(queue, values):
+            if isinstance(value, Exception):
+                loaded.future.set_exception(value)
+            else:
+                loaded.future.set_result(value)
+
+    except Exception as e:
+        return failed_dispatch(loader, queue, e)
+
+
+def failed_dispatch(loader, queue, error):
+    """
+    Do not cache individual loads if the entire batch dispatch fails,
+    but still reject each request so they do not hang.
+    """
+    for loaded in queue:
+        loader.clear(loaded.key)
+        loaded.future.set_exception(error)

setup.py

@@ -53,7 +53,6 @@ def run_tests(self):
     "snapshottest>=0.6,<1",
     "coveralls>=3.3,<4",
     "promise>=2.3,<3",
-    "aiodataloader<1",
     "mock>=4,<5",
     "pytz==2022.1",
     "iso8601>=1,<2",

tests_asyncio/test_dataloader.py

@@ -1,7 +1,8 @@
 from collections import namedtuple
 from unittest.mock import Mock
+
+from graphene.utils.dataloader import DataLoader
 from pytest import mark
-from aiodataloader import DataLoader
 
 from graphene import ObjectType, String, Schema, Field, List