feat(recommend): 홈 추천 스냅샷 집계 쿼리를 추가한다
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package kr.co.vividnext.sodalive.v2.recommend.adapter.out.persistence
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import kr.co.vividnext.sodalive.v2.recommend.domain.RecommendationScoreSpec
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import kr.co.vividnext.sodalive.v2.recommend.domain.RecommendedSectionType
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import kr.co.vividnext.sodalive.v2.recommend.port.out.RecommendationSnapshotRecord
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import org.springframework.stereotype.Repository
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import java.time.LocalDateTime
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import javax.persistence.EntityManager
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@Repository
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class DefaultHomeRecommendationQueryRepository(
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private val entityManager: EntityManager
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) : HomeRecommendationQueryRepository {
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override fun findAiCharacterSnapshots(
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windowStart: LocalDateTime,
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snapshotAt: LocalDateTime,
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limit: Int
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): List<RecommendationSnapshotRecord> {
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val sql = """
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select cc.id as target_id,
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(((select count(cm.id)
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from chat_message cm
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join chat_participant cp on cp.id = cm.participant_id
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where cp.character_id = cc.id
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and cm.created_at >= :windowStart
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and cm.created_at <= :snapshotAt
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and cm.is_active = true
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and cp.is_active = true
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and cp.participant_type = 'CHARACTER') * ${RecommendationScoreSpec.AI_RECENT_CHAT_WEIGHT} +
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(select count(distinct up.member_id)
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from chat_message cm
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join chat_participant up on up.id = cm.participant_id
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join chat_participant cp on cp.chat_room_id = cm.chat_room_id
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join member m on m.id = up.member_id
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where cp.character_id = cc.id
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and cm.created_at >= :windowStart
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and cm.created_at <= :snapshotAt
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and cm.is_active = true
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and up.is_active = true
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and cp.is_active = true
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and up.participant_type = 'USER'
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and cp.participant_type = 'CHARACTER'
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and m.is_active = true) * ${RecommendationScoreSpec.AI_RECENT_ACTIVE_USER_WEIGHT}) *
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case
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when cc.created_at >= :boost10Start then ${RecommendationScoreSpec.NEW_BOOST_10_DAYS}
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when cc.created_at >= :boost20Start then ${RecommendationScoreSpec.NEW_BOOST_20_DAYS}
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when cc.created_at >= :boost30Start then ${RecommendationScoreSpec.NEW_BOOST_30_DAYS}
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else ${RecommendationScoreSpec.DEFAULT_NEW_BOOST}
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end) as score,
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rand() as random_tie_breaker
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from chat_character cc
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where cc.is_active = true
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and (exists (
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select 1
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from chat_message cm
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join chat_participant cp on cp.id = cm.participant_id
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where cp.character_id = cc.id
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and cm.created_at >= :windowStart
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and cm.created_at <= :snapshotAt
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and cm.is_active = true
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and cp.is_active = true
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and cp.participant_type = 'CHARACTER'
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) or exists (
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select 1
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from chat_message cm
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join chat_participant up on up.id = cm.participant_id
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join chat_participant cp on cp.chat_room_id = cm.chat_room_id
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join member m on m.id = up.member_id
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where cp.character_id = cc.id
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and cm.created_at >= :windowStart
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and cm.created_at <= :snapshotAt
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and cm.is_active = true
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and up.is_active = true
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and cp.is_active = true
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and up.participant_type = 'USER'
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and cp.participant_type = 'CHARACTER'
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and m.is_active = true
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))
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order by score desc, random_tie_breaker asc
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limit :limit
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""".trimIndent()
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return executeSnapshotQuery(sql, RecommendedSectionType.AI_CHARACTER, windowStart, snapshotAt, limit)
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}
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override fun findCheerCreatorSnapshots(
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windowStart: LocalDateTime,
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snapshotAt: LocalDateTime,
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limit: Int
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): List<RecommendationSnapshotRecord> {
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val sql = """
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with creator_debut as (
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select debut_events.creator_id as creator_id, min(debut_events.debut_at) as debut_at
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from (
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select ac.member_id as creator_id, ac.release_date as debut_at
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from content ac
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where ac.is_active = true
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and ac.release_date is not null
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and ac.release_date <= :snapshotAt
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union all
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select lr.member_id as creator_id, lr.begin_date_time as debut_at
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from live_room lr
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where lr.is_active = true
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and lr.channel_name is not null
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and lr.begin_date_time <= :snapshotAt
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) debut_events
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group by debut_events.creator_id
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),
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donation_stats as (
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select ucc.recipient_creator_id as creator_id,
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coalesce(sum(ucc.can), 0) as donation_amount,
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count(ucc.id) as donation_count
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from use_can_calculate ucc
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join use_can uc on uc.id = ucc.use_can_id
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where ucc.status = 'RECEIVED'
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and uc.is_refund = false
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and uc.can_usage = 'CHANNEL_DONATION'
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and ucc.created_at >= :windowStart
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and ucc.created_at <= :snapshotAt
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group by ucc.recipient_creator_id
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),
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fan_talk_stats as (
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select ch.creator_id as creator_id, count(ch.id) as fan_talk_count
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from creator_cheers ch
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where ch.is_active = true
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and ch.created_at >= :windowStart
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and ch.created_at <= :snapshotAt
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group by ch.creator_id
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)
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select m.id as target_id,
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((coalesce(ds.donation_amount, 0) * ${RecommendationScoreSpec.CHEER_DONATION_AMOUNT_WEIGHT} +
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coalesce(fts.fan_talk_count, 0) * ${RecommendationScoreSpec.CHEER_FAN_TALK_WEIGHT} +
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coalesce(ds.donation_count, 0) * ${RecommendationScoreSpec.CHEER_DONATION_COUNT_WEIGHT}) *
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case
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when cd.debut_at >= :boost10Start then ${RecommendationScoreSpec.NEW_BOOST_10_DAYS}
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when cd.debut_at >= :boost20Start then ${RecommendationScoreSpec.NEW_BOOST_20_DAYS}
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when cd.debut_at >= :boost30Start then ${RecommendationScoreSpec.NEW_BOOST_30_DAYS}
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else ${RecommendationScoreSpec.DEFAULT_NEW_BOOST}
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end) as score,
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rand() as random_tie_breaker
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from member m
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join creator_debut cd on cd.creator_id = m.id
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left join donation_stats ds on ds.creator_id = m.id
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left join fan_talk_stats fts on fts.creator_id = m.id
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where m.is_active = true
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and cd.debut_at is not null
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and (coalesce(ds.donation_count, 0) > 0 or coalesce(fts.fan_talk_count, 0) > 0)
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order by score desc, random_tie_breaker asc
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limit :limit
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""".trimIndent()
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return executeSnapshotQuery(sql, RecommendedSectionType.CHEER_CREATOR, windowStart, snapshotAt, limit)
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}
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override fun findPopularCommunitySnapshots(
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windowStart: LocalDateTime,
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snapshotAt: LocalDateTime,
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limit: Int
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): List<RecommendationSnapshotRecord> {
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val sql = """
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with creator_debut as (
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select debut_events.creator_id as creator_id, min(debut_events.debut_at) as debut_at
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from (
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select ac.member_id as creator_id, ac.release_date as debut_at
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from content ac
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where ac.is_active = true
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and ac.release_date is not null
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and ac.release_date <= :snapshotAt
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union all
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select lr.member_id as creator_id, lr.begin_date_time as debut_at
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from live_room lr
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where lr.is_active = true
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and lr.channel_name is not null
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and lr.begin_date_time <= :snapshotAt
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) debut_events
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group by debut_events.creator_id
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),
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like_stats as (
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select ccl.creator_community_id as community_id, count(distinct ccl.id) as like_count
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from creator_community_like ccl
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where ccl.is_active = true
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and ccl.created_at >= :windowStart
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and ccl.created_at <= :snapshotAt
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group by ccl.creator_community_id
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),
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comment_stats as (
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select ccc.creator_community_id as community_id, count(distinct ccc.id) as comment_count
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from creator_community_comment ccc
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where ccc.is_active = true
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and ccc.created_at >= :windowStart
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and ccc.created_at <= :snapshotAt
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group by ccc.creator_community_id
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),
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follower_stats as (
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select cf.creator_id as creator_id, count(distinct cf.id) as follower_count
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from creator_following cf
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where cf.is_active = true
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group by cf.creator_id
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)
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select cc.id as target_id,
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((coalesce(ls.like_count, 0) * ${RecommendationScoreSpec.COMMUNITY_LIKE_WEIGHT} +
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(case when cc.is_comment_available = true then coalesce(cs.comment_count, 0) else 0 end) * ${RecommendationScoreSpec.COMMUNITY_COMMENT_WEIGHT} +
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coalesce(fs.follower_count, 0) * ${RecommendationScoreSpec.COMMUNITY_FOLLOWER_WEIGHT}) *
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case
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when cd.debut_at >= :boost10Start then ${RecommendationScoreSpec.NEW_BOOST_10_DAYS}
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when cd.debut_at >= :boost20Start then ${RecommendationScoreSpec.NEW_BOOST_20_DAYS}
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when cd.debut_at >= :boost30Start then ${RecommendationScoreSpec.NEW_BOOST_30_DAYS}
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else ${RecommendationScoreSpec.DEFAULT_NEW_BOOST}
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end) as score,
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rand() as random_tie_breaker
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from creator_community cc
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join member m on m.id = cc.member_id
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join creator_debut cd on cd.creator_id = cc.member_id
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left join like_stats ls on ls.community_id = cc.id
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left join comment_stats cs on cs.community_id = cc.id
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left join follower_stats fs on fs.creator_id = cc.member_id
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where cc.is_active = true
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and m.is_active = true
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and cc.price = 0
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and cc.is_fixed = false
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and cc.created_at <= :snapshotAt
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and cd.debut_at is not null
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order by score desc, random_tie_breaker asc
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limit :limit
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""".trimIndent()
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return executeSnapshotQuery(sql, RecommendedSectionType.POPULAR_COMMUNITY, windowStart, snapshotAt, limit)
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}
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private fun executeSnapshotQuery(
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sql: String,
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sectionType: RecommendedSectionType,
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windowStart: LocalDateTime,
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snapshotAt: LocalDateTime,
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limit: Int
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): List<RecommendationSnapshotRecord> {
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val query = entityManager.createNativeQuery(sql)
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.setParameter("windowStart", windowStart)
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.setParameter("snapshotAt", snapshotAt)
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.setParameter("limit", limit)
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.setParameter(
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"boost10Start",
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snapshotAt.toLocalDate().minusDays(RecommendationScoreSpec.NEW_BOOST_10_DAY_LIMIT).atStartOfDay()
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)
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.setParameter(
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"boost20Start",
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snapshotAt.toLocalDate().minusDays(RecommendationScoreSpec.NEW_BOOST_20_DAY_LIMIT).atStartOfDay()
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)
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.setParameter(
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"boost30Start",
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snapshotAt.toLocalDate().minusDays(RecommendationScoreSpec.NEW_BOOST_30_DAY_LIMIT).atStartOfDay()
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)
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@Suppress("UNCHECKED_CAST")
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val rows = query.resultList as List<Array<Any>>
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return rows.map { row ->
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RecommendationSnapshotRecord(
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sectionType = sectionType,
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targetId = (row[0] as Number).toLong(),
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score = (row[1] as Number).toDouble(),
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snapshotAt = snapshotAt,
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randomTieBreaker = (row[2] as Number).toDouble()
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)
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}
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}
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}
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@@ -0,0 +1,7 @@
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package kr.co.vividnext.sodalive.v2.recommend.adapter.out.persistence
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import kr.co.vividnext.sodalive.v2.recommend.port.out.HomeRecommendationQueryPort
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import org.springframework.data.repository.NoRepositoryBean
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@NoRepositoryBean
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interface HomeRecommendationQueryRepository : HomeRecommendationQueryPort
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@@ -0,0 +1,23 @@
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package kr.co.vividnext.sodalive.v2.recommend.port.out
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import java.time.LocalDateTime
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interface HomeRecommendationQueryPort {
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fun findAiCharacterSnapshots(
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windowStart: LocalDateTime,
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snapshotAt: LocalDateTime,
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limit: Int
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): List<RecommendationSnapshotRecord>
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fun findCheerCreatorSnapshots(
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windowStart: LocalDateTime,
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snapshotAt: LocalDateTime,
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limit: Int
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): List<RecommendationSnapshotRecord>
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fun findPopularCommunitySnapshots(
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windowStart: LocalDateTime,
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snapshotAt: LocalDateTime,
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limit: Int
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): List<RecommendationSnapshotRecord>
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}
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