66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
from django.db import models
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from funkwhale_api.common import fields
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from funkwhale_api.favorites.models import TrackFavorite
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from funkwhale_api.history.models import Listening
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def combined_recent(limit, **kwargs):
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datetime_field = kwargs.pop("datetime_field", "creation_date")
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source_querysets = {qs.model._meta.label: qs for qs in kwargs.pop("querysets")}
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querysets = {
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k: qs.annotate(
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__type=models.Value(qs.model._meta.label, output_field=models.CharField())
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).values("pk", datetime_field, "__type")
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for k, qs in source_querysets.items()
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}
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_qs_list = list(querysets.values())
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union_qs = _qs_list[0].union(*_qs_list[1:])
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records = []
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for row in union_qs.order_by(f"-{datetime_field}")[:limit]:
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records.append(
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{"type": row["__type"], "when": row[datetime_field], "pk": row["pk"]}
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)
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# Now we bulk-load each object type in turn
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to_load = {}
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for record in records:
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to_load.setdefault(record["type"], []).append(record["pk"])
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fetched = {}
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for key, pks in to_load.items():
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for item in source_querysets[key].filter(pk__in=pks):
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fetched[(key, item.pk)] = item
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# Annotate 'records' with loaded objects
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for record in records:
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record["object"] = fetched[(record["type"], record["pk"])]
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return records
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def get_activity(user, limit=20):
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query = fields.privacy_level_query(
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user, "actor__user__privacy_level", "actor__user"
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)
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querysets = [
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Listening.objects.filter(query)
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.select_related(
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"track",
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"actor",
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)
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.prefetch_related(
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"track__artist_credit__artist",
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"track__album__artist_credit__artist",
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),
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TrackFavorite.objects.filter(query)
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.select_related(
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"track",
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"actor",
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)
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.prefetch_related(
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"track__artist_credit__artist",
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"track__album__artist_credit__artist",
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),
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]
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records = combined_recent(limit=limit, querysets=querysets)
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return [r["object"] for r in records]
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