team-10/env/Lib/site-packages/streamlit/runtime/caching/legacy_cache_api.py
2025-08-02 07:34:44 +02:00

169 lines
5.9 KiB
Python

# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2025)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A library of caching utilities."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Callable, TypeVar
from streamlit import deprecation_util
from streamlit.runtime.caching import CACHE_DOCS_URL
from streamlit.runtime.metrics_util import gather_metrics
if TYPE_CHECKING:
from streamlit.runtime.caching.hashing import HashFuncsDict
# Type-annotate the decorator function.
# (See https://mypy.readthedocs.io/en/stable/generics.html#decorator-factories)
F = TypeVar("F", bound=Callable[..., Any])
@gather_metrics("cache")
def cache(
func: F | None = None,
persist: bool = False,
allow_output_mutation: bool = False,
show_spinner: bool = True,
suppress_st_warning: bool = False, # noqa: ARG001
hash_funcs: HashFuncsDict | None = None,
max_entries: int | None = None,
ttl: float | None = None,
) -> F:
"""Legacy caching decorator (deprecated).
Legacy caching with ``st.cache`` has been removed from Streamlit. This is
now an alias for ``st.cache_data`` and ``st.cache_resource``.
Parameters
----------
func : callable
The function to cache. Streamlit hashes the function's source code.
persist : bool
Whether to persist the cache on disk.
allow_output_mutation : bool
Whether to use ``st.cache_data`` or ``st.cache_resource``. If this is
``False`` (default), the arguments are passed to ``st.cache_data``. If
this is ``True``, the arguments are passed to ``st.cache_resource``.
show_spinner : bool
Enable the spinner. Default is ``True`` to show a spinner when there is
a "cache miss" and the cached data is being created.
suppress_st_warning : bool
This is not used.
hash_funcs : dict or None
Mapping of types or fully qualified names to hash functions. This is used to
override the behavior of the hasher inside Streamlit's caching mechanism: when
the hasher encounters an object, it will first check to see if its type matches
a key in this dict and, if so, will use the provided function to generate a hash
for it. See below for an example of how this can be used.
max_entries : int or None
The maximum number of entries to keep in the cache, or ``None``
for an unbounded cache. (When a new entry is added to a full cache,
the oldest cached entry will be removed.) The default is ``None``.
ttl : float or None
The maximum number of seconds to keep an entry in the cache, or
None if cache entries should not expire. The default is None.
Example
-------
>>> import streamlit as st
>>>
>>> @st.cache
... def fetch_and_clean_data(url):
... # Fetch data from URL here, and then clean it up.
... return data
>>>
>>> d1 = fetch_and_clean_data(DATA_URL_1)
>>> # Actually executes the function, since this is the first time it was
>>> # encountered.
>>>
>>> d2 = fetch_and_clean_data(DATA_URL_1)
>>> # Does not execute the function. Instead, returns its previously computed
>>> # value. This means that now the data in d1 is the same as in d2.
>>>
>>> d3 = fetch_and_clean_data(DATA_URL_2)
>>> # This is a different URL, so the function executes.
To set the ``persist`` parameter, use this command as follows:
>>> @st.cache(persist=True)
... def fetch_and_clean_data(url):
... # Fetch data from URL here, and then clean it up.
... return data
To disable hashing return values, set the ``allow_output_mutation`` parameter to
``True``:
>>> @st.cache(allow_output_mutation=True)
... def fetch_and_clean_data(url):
... # Fetch data from URL here, and then clean it up.
... return data
To override the default hashing behavior, pass a custom hash function.
You can do that by mapping a type (e.g. ``MongoClient``) to a hash function (``id``)
like this:
>>> @st.cache(hash_funcs={MongoClient: id})
... def connect_to_database(url):
... return MongoClient(url)
Alternatively, you can map the type's fully-qualified name
(e.g. ``"pymongo.mongo_client.MongoClient"``) to the hash function instead:
>>> @st.cache(hash_funcs={"pymongo.mongo_client.MongoClient": id})
... def connect_to_database(url):
... return MongoClient(url)
"""
import streamlit as st
deprecation_util.show_deprecation_warning(
f"""
`st.cache` is deprecated and will be removed soon. Please use one of Streamlit's new
caching commands, `st.cache_data` or `st.cache_resource`. More information
[in our docs]({CACHE_DOCS_URL}).
**Note**: The behavior of `st.cache` was updated in Streamlit 1.36 to the new caching
logic used by `st.cache_data` and `st.cache_resource`. This might lead to some problems
or unexpected behavior in certain edge cases.
"""
)
# suppress_st_warning is unused since its not supported by the new caching commands
if allow_output_mutation:
return st.cache_resource( # type: ignore
func,
show_spinner=show_spinner,
hash_funcs=hash_funcs,
max_entries=max_entries,
ttl=ttl,
)
return st.cache_data( # type: ignore
func,
persist=persist,
show_spinner=show_spinner,
hash_funcs=hash_funcs,
max_entries=max_entries,
ttl=ttl,
)