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Important

This is an experimental feature. Experimental features and their APIs may change or be removed at any time. To learn more, click here.

Function decorator to memoize function executions.

Memoized data is stored in "pickled" form, which means that the return value of a memoized function must be pickleable.

Each caller of a memoized function gets its own copy of the cached data.

You can clear a memoized function's cache with f.clear().

Function signature

st.experimental_memo(func=None, *, persist=None, show_spinner=True, suppress_st_warning=False, max_entries=None, ttl=None)

Parameters

func (callable)

The function to memoize. Streamlit hashes the function's source code.

persist (str or None)

Optional location to persist cached data to. Currently, the only valid value is "disk", which will persist to the local disk.

show_spinner (boolean)

Enable the spinner. Default is True to show a spinner when there is a cache miss.

suppress_st_warning (boolean)

Suppress warnings about calling Streamlit functions from within the cached function.

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

@st.experimental_memo
 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.experimental_memo(persist="disk")
 def fetch_and_clean_data(url):
     # Fetch data from URL here, and then clean it up.
     return data

By default, all parameters to a memoized function must be hashable. Any parameter whose name begins with _ will not be hashed. You can use this as an "escape hatch" for parameters that are not hashable:

@st.experimental_memo
 def fetch_and_clean_data(_db_connection, num_rows):
     # Fetch data from _db_connection here, and then clean it up.
     return data

connection = make_database_connection()
d1 = fetch_and_clean_data(connection, num_rows=10)
# Actually executes the function, since this is the first time it was
# encountered.

another_connection = make_database_connection()
d2 = fetch_and_clean_data(another_connection, num_rows=10)
# Does not execute the function. Instead, returns its previously computed
# value - even though the _database_connection parameter was different
# in both calls.

A memoized function's cache can be procedurally cleared:

@st.experimental_memo
 def fetch_and_clean_data(_db_connection, num_rows):
     # Fetch data from _db_connection here, and then clean it up.
     return data

fetch_and_clean_data.clear()
# Clear all cached entries for this function.

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