82 lines
3.0 KiB
Scala
82 lines
3.0 KiB
Scala
package com.twitter.home_mixer
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package functional_component.feature_hydrator
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import com.twitter.finagle.stats.StatsReceiver
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import com.twitter.home_mixer.model.HomeFeatures.AuthorIdFeature
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import com.twitter.home_mixer.param.HomeMixerInjectionNames.MetricCenterUserCountingFeatureRepository
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import com.twitter.home_mixer.util.ObservedKeyValueResultHandler
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import com.twitter.onboarding.relevance.features.{thriftjava => rf}
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import com.twitter.product_mixer.component_library.model.candidate.TweetCandidate
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import com.twitter.product_mixer.core.feature.Feature
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import com.twitter.product_mixer.core.feature.featuremap.FeatureMap
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import com.twitter.product_mixer.core.feature.featuremap.FeatureMapBuilder
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import com.twitter.product_mixer.core.functional_component.feature_hydrator.BulkCandidateFeatureHydrator
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import com.twitter.product_mixer.core.model.common.CandidateWithFeatures
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import com.twitter.product_mixer.core.model.common.identifier.FeatureHydratorIdentifier
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import com.twitter.product_mixer.core.pipeline.PipelineQuery
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import com.twitter.servo.keyvalue.KeyValueResult
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import com.twitter.servo.repository.KeyValueRepository
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import com.twitter.stitch.Stitch
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import com.twitter.util.Future
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import javax.inject.Inject
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import javax.inject.Named
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import javax.inject.Singleton
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object MetricCenterUserCountingFeature
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extends Feature[TweetCandidate, Option[rf.MCUserCountingFeatures]]
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@Singleton
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class MetricCenterUserCountingFeatureHydrator @Inject() (
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@Named(MetricCenterUserCountingFeatureRepository) client: KeyValueRepository[Seq[
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Long
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], Long, rf.MCUserCountingFeatures],
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override val statsReceiver: StatsReceiver)
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extends BulkCandidateFeatureHydrator[PipelineQuery, TweetCandidate]
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with ObservedKeyValueResultHandler {
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override val identifier: FeatureHydratorIdentifier =
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FeatureHydratorIdentifier("MetricCenterUserCounting")
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override val features: Set[Feature[_, _]] = Set(MetricCenterUserCountingFeature)
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override val statScope: String = identifier.toString
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override def apply(
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query: PipelineQuery,
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candidates: Seq[CandidateWithFeatures[TweetCandidate]]
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): Stitch[Seq[FeatureMap]] = {
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Stitch.callFuture {
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val possiblyAuthorIds = extractKeys(candidates)
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val userIds = possiblyAuthorIds.flatten
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val response: Future[KeyValueResult[Long, rf.MCUserCountingFeatures]] = if (userIds.isEmpty) {
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Future.value(KeyValueResult.empty)
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} else {
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client(userIds)
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}
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response.map { result =>
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possiblyAuthorIds.map { possiblyAuthorId =>
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val value = observedGet(key = possiblyAuthorId, keyValueResult = result)
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FeatureMapBuilder()
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.add(MetricCenterUserCountingFeature, value)
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.build()
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}
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}
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}
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}
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private def extractKeys(
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candidates: Seq[CandidateWithFeatures[TweetCandidate]]
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): Seq[Option[Long]] = {
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candidates.map { candidate =>
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candidate.features
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.getTry(AuthorIdFeature)
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.toOption
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.flatten
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}
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}
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}
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