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Define a CURRENT_JUPYTER_HANDLER context var (#1251)
* [Enhancement] Define a CURRENT_JUPYTER_HANDLER context var * add type to context var * Introduce CallContext class * Add CallContext to API docs * Alphabetize submodules * Unit test contextvar in the kernel shutdown flow * Update tests/services/sessions/test_call_context.py Co-authored-by: Kevin Bates <[email protected]> * revert unit test back to using kernel_model * Relocate to base package * Update location in docs as well --------- Co-authored-by: Kevin Bates <[email protected]>
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"""Provides access to variables pertaining to specific call contexts.""" | ||
# Copyright (c) Jupyter Development Team. | ||
# Distributed under the terms of the Modified BSD License. | ||
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from contextvars import Context, ContextVar, copy_context | ||
from typing import Any, Dict, List | ||
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class CallContext: | ||
"""CallContext essentially acts as a namespace for managing context variables. | ||
Although not required, it is recommended that any "file-spanning" context variable | ||
names (i.e., variables that will be set or retrieved from multiple files or services) be | ||
added as constants to this class definition. | ||
""" | ||
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# Add well-known (file-spanning) names here. | ||
#: Provides access to the current request handler once set. | ||
JUPYTER_HANDLER: str = "JUPYTER_HANDLER" | ||
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# A map of variable name to value is maintained as the single ContextVar. This also enables | ||
# easier management over maintaining a set of ContextVar instances, since the Context is a | ||
# map of ContextVar instances to their values, and the "name" is no longer a lookup key. | ||
_NAME_VALUE_MAP = "_name_value_map" | ||
_name_value_map: ContextVar[Dict[str, Any]] = ContextVar(_NAME_VALUE_MAP) | ||
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@classmethod | ||
def get(cls, name: str) -> Any: | ||
"""Returns the value corresponding the named variable relative to this context. | ||
If the named variable doesn't exist, None will be returned. | ||
Parameters | ||
---------- | ||
name : str | ||
The name of the variable to get from the call context | ||
Returns | ||
------- | ||
value: Any | ||
The value associated with the named variable for this call context | ||
""" | ||
name_value_map = CallContext._get_map() | ||
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if name in name_value_map: | ||
return name_value_map[name] | ||
return None # TODO - should this raise `LookupError` (or a custom error derived from said) | ||
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@classmethod | ||
def set(cls, name: str, value: Any) -> None: | ||
"""Sets the named variable to the specified value in the current call context. | ||
Parameters | ||
---------- | ||
name : str | ||
The name of the variable to store into the call context | ||
value : Any | ||
The value of the variable to store into the call context | ||
Returns | ||
------- | ||
None | ||
""" | ||
name_value_map = CallContext._get_map() | ||
name_value_map[name] = value | ||
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@classmethod | ||
def context_variable_names(cls) -> List[str]: | ||
"""Returns a list of variable names set for this call context. | ||
Returns | ||
------- | ||
names: List[str] | ||
A list of variable names set for this call context. | ||
""" | ||
name_value_map = CallContext._get_map() | ||
return list(name_value_map.keys()) | ||
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@classmethod | ||
def _get_map(cls) -> Dict[str, Any]: | ||
"""Get the map of names to their values from the _NAME_VALUE_MAP context var. | ||
If the map does not exist in the current context, an empty map is created and returned. | ||
""" | ||
ctx: Context = copy_context() | ||
if CallContext._name_value_map not in ctx: | ||
CallContext._name_value_map.set({}) | ||
return CallContext._name_value_map.get() |
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import asyncio | ||
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from jupyter_server import CallContext | ||
from jupyter_server.auth.utils import get_anonymous_username | ||
from jupyter_server.base.handlers import JupyterHandler | ||
from jupyter_server.services.kernels.kernelmanager import AsyncMappingKernelManager | ||
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async def test_jupyter_handler_contextvar(jp_fetch, monkeypatch): | ||
# Create some mock kernel Ids | ||
kernel1 = "x-x-x-x-x" | ||
kernel2 = "y-y-y-y-y" | ||
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# We'll use this dictionary to track the current user within each request. | ||
context_tracker = { | ||
kernel1: {"started": "no user yet", "ended": "still no user", "user": None}, | ||
kernel2: {"started": "no user yet", "ended": "still no user", "user": None}, | ||
} | ||
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# Monkeypatch the get_current_user method in Tornado's | ||
# request handler to return a random user name for | ||
# each request | ||
async def get_current_user(self): | ||
return get_anonymous_username() | ||
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monkeypatch.setattr(JupyterHandler, "get_current_user", get_current_user) | ||
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# Monkeypatch the kernel_model method to show that | ||
# the current context variable is truly local and | ||
# not contaminated by other asynchronous parallel requests. | ||
# Note that even though the current implementation of `kernel_model()` | ||
# is synchronous, we can convert this into an async method because the | ||
# kernel handler wraps the call to `kernel_model()` in `ensure_async()`. | ||
async def kernel_model(self, kernel_id): | ||
# Get the Jupyter Handler from the current context. | ||
current: JupyterHandler = CallContext.get(CallContext.JUPYTER_HANDLER) | ||
# Get the current user | ||
context_tracker[kernel_id]["user"] = current.current_user | ||
context_tracker[kernel_id]["started"] = current.current_user | ||
await asyncio.sleep(1.0) | ||
# Track the current user a few seconds later. We'll | ||
# verify that this user was unaffected by other parallel | ||
# requests. | ||
context_tracker[kernel_id]["ended"] = current.current_user | ||
return {"id": kernel_id, "name": "blah"} | ||
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monkeypatch.setattr(AsyncMappingKernelManager, "kernel_model", kernel_model) | ||
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# Make two requests in parallel. | ||
await asyncio.gather( | ||
jp_fetch("api", "kernels", kernel1), | ||
jp_fetch("api", "kernels", kernel2), | ||
) | ||
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# Assert that the two requests had different users | ||
assert context_tracker[kernel1]["user"] != context_tracker[kernel2]["user"] | ||
# Assert that the first request started+ended with the same user | ||
assert context_tracker[kernel1]["started"] == context_tracker[kernel1]["ended"] | ||
# Assert that the second request started+ended with the same user | ||
assert context_tracker[kernel2]["started"] == context_tracker[kernel2]["ended"] | ||
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async def test_context_variable_names(): | ||
CallContext.set("foo", "bar") | ||
CallContext.set("foo2", "bar2") | ||
names = CallContext.context_variable_names() | ||
assert len(names) == 2 | ||
assert set(names) == {"foo", "foo2"} | ||
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async def test_same_context_operations(): | ||
CallContext.set("foo", "bar") | ||
CallContext.set("foo2", "bar2") | ||
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foo = CallContext.get("foo") | ||
assert foo == "bar" | ||
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CallContext.set("foo", "bar2") | ||
assert CallContext.get("foo") == CallContext.get("foo2") | ||
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async def test_multi_context_operations(): | ||
async def context1(): | ||
"""The "slower" context. This ensures that, following the sleep, the | ||
context variable set prior to the sleep is still the expected value. | ||
If contexts are not managed properly, we should find that context2() has | ||
corrupted context1(). | ||
""" | ||
CallContext.set("foo", "bar1") | ||
await asyncio.sleep(1.0) | ||
assert CallContext.get("foo") == "bar1" | ||
context1_names = CallContext.context_variable_names() | ||
assert len(context1_names) == 1 | ||
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async def context2(): | ||
"""The "faster" context. This ensures that CallContext reflects the | ||
appropriate values of THIS context. | ||
""" | ||
CallContext.set("foo", "bar2") | ||
assert CallContext.get("foo") == "bar2" | ||
CallContext.set("foo2", "bar2") | ||
context2_names = CallContext.context_variable_names() | ||
assert len(context2_names) == 2 | ||
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await asyncio.gather(context1(), context2()) | ||
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# Assert that THIS context doesn't have any variables defined. | ||
names = CallContext.context_variable_names() | ||
assert len(names) == 0 |