Caching: implementation details¶
This section covers some details of the caching mechanism which are not discussed in the user guide. If you are developing a plugin and want to modify the caching behavior of your classes, we recommend you read this section first.
Below are some methods you can use to control how the hashes of calculation and data classes are computed:
- To ignore specific attributes, a
Nodesubclass can have a
_hash_ignored_attributesattribute. This is a list of attribute names, which are ignored when creating the hash.
- For calculations, the
_hash_ignored_inputsattribute lists inputs that should be ignored when creating the hash.
- To add things which should be considered in the hash, you can override the
_get_objects_to_hash()method. Note that doing so overrides the behavior described above, so you should make sure to use the
- Pass a keyword argument to
get_hash(). These are passed on to
There are two methods you can use to disable caching for particular nodes:
is_valid_cache()property determines whether a particular node can be used as a cache. This is used for example to disable caching from failed calculations.
- Node classes have a
_cachableattribute, which can be set to
Falseto completely switch off caching for nodes of that class. This avoids performing queries for the hash altogether.
As discussed in the user guide, nodes which can have
RETURN links cannot be cached.
This is enforced on two levels:
_cachableproperty is set to
ProcessNode, and only re-enabled in
CalcFunctionNode. This means that a
WorkflowNodewill not be cached.
_store_from_cachemethod, which is used to “clone” an existing node, will raise an error if the existing node has any
RETURNlinks. This extra safe-guard prevents cases where a user might incorrectly override the
_cachableproperty on a
When modifying the hashing/caching behaviour of your classes, keep in mind that cache matches can go wrong in two ways:
- False negatives, where two nodes should have the same hash but do not
- False positives, where two different nodes get the same hash by mistake
False negatives are highly preferrable because they only increase the runtime of your calculations, while false positives can lead to wrong results.