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ea66c45df9Datasette previously only supported one type of faceting: exact column value counting. With this change, faceting logic is extracted out into one or more separate classes which can implement other patterns of faceting - this is discussed in #427, but potential upcoming facet types include facet-by-date, facet-by-JSON-array, facet-by-many-2-many and more. A new plugin hook, register_facet_classes, can be used by plugins to add in additional facet classes. Each class must implement two methods: suggest(), which scans columns in the table to decide if they might be worth suggesting for faceting, and facet_results(), which executes the facet operation and returns results ready to be displayed in the UI.
22 lines
504 B
Python
22 lines
504 B
Python
import importlib
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import pluggy
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import sys
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from . import hookspecs
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DEFAULT_PLUGINS = (
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"datasette.publish.heroku",
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"datasette.publish.now",
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"datasette.facets",
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)
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pm = pluggy.PluginManager("datasette")
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pm.add_hookspecs(hookspecs)
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if not hasattr(sys, "_called_from_test"):
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# Only load plugins if not running tests
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pm.load_setuptools_entrypoints("datasette")
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# Load default plugins
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for plugin in DEFAULT_PLUGINS:
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mod = importlib.import_module(plugin)
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pm.register(mod, plugin)
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