feat:添加日志

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/feature/FeatureService.java

@@ -79,10 +79,12 @@ public class FeatureService {
             protos.add(genWithMid("alg_mid_feature_share_tags", param.getMid()));
         }
 
+        long start = System.currentTimeMillis();
         Map<String, String> featureMap = remoteService.getFeature(protos);
         featureMap = this.featureStrCover(featureMap);
-        Feature feature = new Feature();
+        log.info("svc=invokeFeatureService, elapsed: {}", System.currentTimeMillis() - start);
 
+        Feature feature = new Feature();
         for (Map.Entry<String, String> entry : featureMap.entrySet()) {
             String key = entry.getKey();
             String value = entry.getValue();

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/predict/model/threshold/RandomPredictModel.java

@@ -1,5 +1,6 @@
 package com.tzld.piaoquan.ad.engine.service.predict.model.threshold;
 
+import com.alibaba.fastjson.JSONObject;
 import com.tzld.piaoquan.ad.engine.commons.util.DateUtils;
 import com.tzld.piaoquan.ad.engine.commons.util.JSONUtils;
 import com.tzld.piaoquan.ad.engine.service.predict.container.RandWContainer;
@@ -46,6 +47,14 @@ public class RandomPredictModel extends ThresholdPredictModel {
         result.put("score", score);
         result.put("threshold", threshold);
         result.put("model", "random");
+
+        JSONObject logJson = new JSONObject();
+        logJson.putAll(result);
+        logJson.put("mid", modelParam.getMid());
+        logJson.put("expId", "599");
+        logJson.put("appType", appType);
+        logJson.put("thresholdParamKey", thresholdParamKey);
+
         log.info("广告跳出选择 -- 599实验结果: {}, 参数: {}, {}, {}",
                 JSONUtils.toJson(result), appType, modelParam.getMid(), thresholdParamKey);
         return result;

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/RankService680.java

@@ -43,7 +43,10 @@ public class RankService680 {
 
 
         // 特征处理
+        long start = System.currentTimeMillis();
         Feature feature = this.getFeature(scoreParam, request);
+        log.info("svc=getFeature, elapsed: {}", System.currentTimeMillis() - start);
+
         Map<String, Map<String, String>> userFeature = feature.getUserFeature();
         Map<String, Map<String, String>> videoFeature = feature.getVideoFeature();
         Map<String, Map<String, Map<String, String>>> allAdVerFeature = feature.getAdVerFeature();

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/impl/RankServiceImpl.java

@@ -90,18 +90,12 @@ public class RankServiceImpl implements RankService {
 
     private AdRankItem rankBy680(RankRecommendRequestParam request) {
         ScoreParam scoreParam = RequestConvert.requestConvert(request);
+        long start = System.currentTimeMillis();
         List<AdRankItem> adRankItems = fmRankService.adItemRank(request, scoreParam);
-        // List<JSONObject> collect = adRankItems.stream().map(i -> {
-        //     JSONObject json = new JSONObject();
-        //     json.put("cid", i.getAdId());
-        //     json.put("lrScore", i.getLrScore());
-        //     json.put("score", i.getScore());
-        //     json.put("metaFeatureMap", i.getMetaFeatureMap());
-        //     json.put("allFeatureMap", i.getFeatureMap());
-        //     return json;
-        // }).collect(Collectors.toList());
-        // log.info("LR模型打分结果: {}", JSON.toJSONString(collect));
+        log.info("svc=680Rank, elapsed: {}", System.currentTimeMillis() - start);
+        start = System.currentTimeMillis();
         logHubService.scoreLogUpload(scoreParam, request.getAdIdList(), adRankItems, request, "LRModelScore", "680");
+        log.info("svc=scoreLogUpload, elapsed: {}", System.currentTimeMillis() - start);
         return adRankItems.get(0);
     }