- Removed redundant query to analytics_predict_samples
- Analysers API now uses recordsets to iterate through the analysable
elements. They take the last analysed time into account.
- New method for targets so there is no need to always update the last
analysis time. Useful for lightweight targets.
These coding style issues were spotted while working on the issue. No
real changes here, just the coding style fixes attached to the patchset
for convenience.
Similarly to how the scheduled tasks work, we now automatically check
and make sure that all the models specified in the component's
db/analytics.php file exist during the installation or upgrade of the
component.
The functionality of the \core_analytics\manager::add_builtin_models()
method is to be replaced with automatic update of models provided by the
core moodle component. There is no need to call this method explicitly
any more. Instead, adding new models will be done by updating the
lib/db/analytics.php file and bumping the core version.
Added static caching of classes to reduce load times and reduce calls to `get_component_classes`
by altering to accept a null component value to search classmap only once.
The new recordset support for Postgres requires transactions and
will cause errors if recordsets are not closed correctly. This
commit fixes problems that were identified during unit tests, and
via some basic code analysis, across all core code. Most of these
are incorrect usage of recordset (forgetting to close them).
This was supposed to be split into multiple commits to make it easier to understand
but I failed to do it properly. So this is the list of changes:
- New analytics_indicator_calc db table to store indicators calculations
- Reuse previous calculations during prediction/training; other models
previous calculations should also be reused as long as they belong to
the same sample (sampleid depends on sampleorigin), time range and indicator
- Allow bulk inserting of these calculations as this can hurt database performance
- Block the same analysable to be analysed for training and for prediction
- Use a new instance of the target and use it for is_valid_* functions
as using ::is_valid_sample can lead to problems if people
uses it to cache stuff
- Split model::predict in parts
- JS promises updated according to eslint-plugin-promise
- New API methods replacing direct DB queries
- Reduce insights nav link display cost
- Increase time limit as well as memory for big processes
- Move prediction action event to core
- Dataset write locking and others
- Refine last time range end time
- Removed dodgy splitting method id to int
- Replace admin_setting_predictor output_html overwrite for write_setting overwrite
- New APIs for access control
- Discard invalid samples also during prediction
