Configuration
fleche looks for a configuration file in the following order:
fleche.tomlin the current working directory (local config)A global config file:
If
$XDG_CONFIG_HOMEis set:$XDG_CONFIG_HOME/fleche/cache.tomlOtherwise:
~/.fleche.toml
The first file found is used. If no configuration file exists, fleche falls back to a default in-memory cache.
Reserved Cache Names
memory
The name memory is a reserved cache name. When requested, fleche will provide a transient in-memory cache. This cache is persistent for the duration of the process, but is not shared with other processes and is lost when the current process exits.
Example:
from fleche import cache
with cache("memory"):
# Results will be cached in memory. The cache persists for the lifetime of the process.
...
void
The name void is a reserved cache name. When requested, fleche will provide a no-op cache that discards all stored values. This is useful for disabling caching entirely without changing your code.
Example:
from fleche import cache
with cache("void"):
# Results will not be cached at all. Every call executes the function.
...
The [default] section
The [default] section is used to configure the default behavior of fleche.
cache
The cache key specifies the name of the default cache to use.
Example:
[default]
cache = "mycache"
metadata
The metadata key specifies the default metadata chain to use. This is a list of strings, where each string is the name of a metadata class from the fleche.metadata module.
Example:
[default]
metadata = ["Runtime"]
Note: The Tags metadata cannot be configured from the config file, as it requires arguments.
Cache sections
You can define multiple cache configurations in the same file, each in its own section.
Each cache section must define two storage backends: values and calls. values is used to store the results of function calls, and calls is used to store the function call details.
Storage backends
Each storage backend is configured using a type key, see the table below. Other keys in the same dict are
passed as keyword arguments to the storage constructor.
Example:
[mycache]
values.type = "memory"
calls.type = "memory"
Available storage types
Type |
Description |
Required |
Optional |
|---|---|---|---|
|
In-memory dictionary
( |
— |
|
|
— |
— |
|
|
Filesystem backend, standard |
|
|
|
Filesystem backend, |
|
same as |
|
Filesystem backend, |
|
same as |
|
HDF5 files via |
|
|
|
SQL via SQLAlchemy ( |
|
|
Key descriptions
rootPath to the storage directory (string;
~is expanded).compress(bool, default
false) — gzip-compress each stored file.lock_timeout(float, default
1.0) — maximum seconds to wait for a concurrent write lock before attempting a read anyway.secret_key(list of hex strings) — HMAC-SHA256 signing keys for tamper detection; see Security for details. If omitted, falls back to the
FLECHE_SECRET_KEYenvironment variable.urlSQLAlchemy connection URL, e.g.
"sqlite:///~/.cache/fleche/calls.db". Leading~is expanded to the home directory insqlite:///URLs.echo(bool, default
false) — log all SQL statements to stderr (useful for debugging).version_validator(str, default omitted) — version validation strategy passed to
bagofholding’sH5Bag.load. One of"exact","semantic-minor","semantic-major", or"none". When omitted,bagofholding’s own default applies.remaining_depth(int, default
0) — destructuring depth; see Destructuring below.
Destructuring
All value backends except "sql" (which is call-only) store collections
(list, tuple, dict) by destructuring them: each element is
stored independently under its own cache key, and on load the original structure is
reassembled. This avoids redundant storage of shared sub-structures across different
cached calls.
The optional remaining_depth key (integer, default 0) controls the granularity:
remaining_depth = 0— maximum splitting: every element at every nesting level is stored as a separate entry.remaining_depth = N(positive) — elements at nesting levels shallower than N are stored inline within their parent entry rather than as separate entries. For example,remaining_depth = 1inlines scalars within their parent list or dict so each top-level collection is stored as a single entry.
Higher values mean fewer, larger storage entries and less structural sharing between calls.
Example:
[mycache]
values.type = "cloudpickle"
values.root = "~/.cache/fleche/values"
values.remaining_depth = 1 # inline scalars; one entry per top-level collection
calls.type = "cloudpickle"
calls.root = "~/.cache/fleche/calls"
Full Configuration Example
Below is an example of a complete configuration file demonstrating several features:
[default]
cache = "persistent"
metadata = ["Runtime"]
[persistent]
# Store values as cloudpickle files
values.type = "cloudpickle"
values.root = "~/.cache/fleche/values"
# Store call details as cloudpickle files
calls.type = "cloudpickle"
calls.root = "~/.cache/fleche/calls"
[fast]
# Simple in-memory cache
values.type = "memory"
calls.type = "memory"
[hdf5_values]
# HDF5 values backend with SQL call index
values.type = "bagofholding_hdf"
values.root = "~/.cache/fleche/hdf5_values"
calls.type = "sql"
calls.url = "sqlite:///~/.cache/fleche/calls.db"