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https://github.com/element-hq/synapse.git
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707d5e4e48
Currently we use `JsonEncoder.iterencode` to write JSON responses, which ensures that we don't block the main reactor thread when encoding huge objects. The downside to this is that `iterencode` falls back to using a pure Python encoder that is *much* less efficient and can easily burn a lot of CPU for huge responses. To fix this, while still ensuring we don't block the reactor loop, we encode the JSON on a threadpool using the standard `JsonEncoder.encode` functions, which is backed by a C library. Doing so, however, requires `respond_with_json` to have access to the reactor, which it previously didn't. There are two ways of doing this: 1. threading through the reactor object, which is a bit fiddly as e.g. `DirectServeJsonResource` doesn't currently take a reactor, but is exposed to modules and so is a PITA to change; or 2. expose the reactor in `SynapseRequest`, which requires updating a bunch of servlet types. I went with the latter as that is just a mechanical change, and I think makes sense as a request already has a reactor associated with it (via its http channel).
110 lines
3.1 KiB
Python
110 lines
3.1 KiB
Python
# Copyright 2014-2016 OpenMarket Ltd
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# Copyright 2020 The Matrix.org Foundation C.I.C.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import heapq
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from itertools import islice
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from typing import (
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Collection,
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Dict,
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Generator,
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Iterable,
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Iterator,
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Mapping,
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Set,
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Sized,
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Tuple,
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TypeVar,
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)
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from typing_extensions import Protocol
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T = TypeVar("T")
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S = TypeVar("S", bound="_SelfSlice")
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class _SelfSlice(Sized, Protocol):
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"""A helper protocol that matches types where taking a slice results in the
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same type being returned.
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This is more specific than `Sequence`, which allows another `Sequence` to be
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returned.
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"""
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def __getitem__(self: S, i: slice) -> S:
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...
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def batch_iter(iterable: Iterable[T], size: int) -> Iterator[Tuple[T, ...]]:
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"""batch an iterable up into tuples with a maximum size
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Args:
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iterable: the iterable to slice
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size: the maximum batch size
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Returns:
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an iterator over the chunks
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"""
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# make sure we can deal with iterables like lists too
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sourceiter = iter(iterable)
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# call islice until it returns an empty tuple
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return iter(lambda: tuple(islice(sourceiter, size)), ())
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def chunk_seq(iseq: S, maxlen: int) -> Iterator[S]:
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"""Split the given sequence into chunks of the given size
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The last chunk may be shorter than the given size.
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If the input is empty, no chunks are returned.
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"""
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return (iseq[i : i + maxlen] for i in range(0, len(iseq), maxlen))
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def sorted_topologically(
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nodes: Iterable[T],
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graph: Mapping[T, Collection[T]],
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) -> Generator[T, None, None]:
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"""Given a set of nodes and a graph, yield the nodes in toplogical order.
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For example `sorted_topologically([1, 2], {1: [2]})` will yield `2, 1`.
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"""
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# This is implemented by Kahn's algorithm.
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degree_map = {node: 0 for node in nodes}
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reverse_graph: Dict[T, Set[T]] = {}
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for node, edges in graph.items():
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if node not in degree_map:
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continue
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for edge in set(edges):
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if edge in degree_map:
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degree_map[node] += 1
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reverse_graph.setdefault(edge, set()).add(node)
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reverse_graph.setdefault(node, set())
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zero_degree = [node for node, degree in degree_map.items() if degree == 0]
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heapq.heapify(zero_degree)
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while zero_degree:
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node = heapq.heappop(zero_degree)
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yield node
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for edge in reverse_graph.get(node, []):
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if edge in degree_map:
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degree_map[edge] -= 1
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if degree_map[edge] == 0:
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heapq.heappush(zero_degree, edge)
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