Merge branch 'ljd/feature-20251224' of algorithm/ad-engine into master

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/score/model/PAIModelV1.java

@@ -126,9 +126,18 @@ public class PAIModelV1 {
     public List<Float> score(final List<AdRankItem> items,
                              final Map<String, String> userFeatureMap,
                              final Map<String, String> sceneFeatureMap) {
+        long totalStart = System.currentTimeMillis();
+        long buildUserFeatureTime = 0;
+        long buildItemFeatureTime = 0;
+        long buildRequestTime = 0;
+        long predictTime = 0;
+        long parseResponseTime = 0;
+        
         try {
             TFRequest request = new TFRequest();
 
+            // 阶段1: 构建用户特征
+            long stageStart = System.currentTimeMillis();
             for (String feature : sparseUserStrFeatures) {
                 String v = userFeatureMap.getOrDefault(feature, "");
                 request.addFeed(feature, TFDataType.DT_STRING, new long[]{1}, new String[]{v});
@@ -144,11 +153,14 @@ public class PAIModelV1 {
                 request.addFeed(feature, TFDataType.DT_INT64, new long[]{1}, new long[]{v});
             }
 
-
             for (String feature : userFeatures) {
                 double v = NumberUtils.toDouble(userFeatureMap.getOrDefault(feature, "0.0"), 0.0);
                 request.addFeed(feature.toLowerCase(), TFDataType.DT_DOUBLE, new long[]{1}, new double[]{v});
             }
+            buildUserFeatureTime = System.currentTimeMillis() - stageStart;
+
+            // 阶段2: 构建广告Item特征
+            stageStart = System.currentTimeMillis();
             Map<String, double[]> doubleFeed = new HashMap<>();
             Map<String, long[]> longFeed = new HashMap<>();
             Map<String, String[]> strFeed = new HashMap<>();
@@ -184,6 +196,10 @@ public class PAIModelV1 {
                     strs[i] = v;
                 }
             }
+            buildItemFeatureTime = System.currentTimeMillis() - stageStart;
+
+            // 阶段3: 构建请求体
+            stageStart = System.currentTimeMillis();
             for (Map.Entry<String, double[]> entry : doubleFeed.entrySet()) {
                 request.addFeed(entry.getKey(), TFDataType.DT_DOUBLE, new long[]{items.size()}, entry.getValue());
             }
@@ -196,13 +212,35 @@ public class PAIModelV1 {
                 request.addFeed(entry.getKey(), TFDataType.DT_STRING, new long[]{items.size()}, entry.getValue());
             }
             request.addFetch("probs");
+            buildRequestTime = System.currentTimeMillis() - stageStart;
+
+            // 阶段4: PAI-EAS 远程调用
+            stageStart = System.currentTimeMillis();
             TFResponse response = client.predict(request);
+            predictTime = System.currentTimeMillis() - stageStart;
+
+            // 阶段5: 解析响应
+            stageStart = System.currentTimeMillis();
             List<Float> result = response.getFloatVals("probs");
+            parseResponseTime = System.currentTimeMillis() - stageStart;
+
+            long totalTime = System.currentTimeMillis() - totalStart;
+            
+            // 输出耗时分析日志
+            LOGGER.info("PAIModelV1.score cost: total={}ms, itemSize={}, buildUserFeature={}ms, " +
+                        "buildItemFeature={}ms, buildRequest={}ms, predict={}ms, parseResponse={}ms",
+                        totalTime, items.size(), buildUserFeatureTime, buildItemFeatureTime, 
+                        buildRequestTime, predictTime, parseResponseTime);
+            
             if (!CollectionUtils.isEmpty(result)) {
                 return result;
             }
         } catch (Exception e) {
-            LOGGER.error("PAIModel score error", e);
+            long totalTime = System.currentTimeMillis() - totalStart;
+            LOGGER.error("PAIModelV1.score error: total={}ms, itemSize={}, buildUserFeature={}ms, " +
+                         "buildItemFeature={}ms, buildRequest={}ms, predict={}ms, parseResponse={}ms",
+                         totalTime, items.size(), buildUserFeatureTime, buildItemFeatureTime, 
+                         buildRequestTime, predictTime, parseResponseTime, e);
         }
         return new ArrayList<>(Collections.nCopies(items.size(), 0.0f));
     }

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

@@ -49,7 +49,7 @@ public class PAIScorer extends AbstractScorer {
 
         List<AdRankItem> result = rankByJava(sceneFeatureMap, userFeatureMap, rankItems);
 
-        LOGGER.debug("ctr ranker time java items size={}, time={} ", result != null ? result.size() : 0,
+        LOGGER.info("ctr ranker time java items size={}, time={} ", result != null ? result.size() : 0,
                 System.currentTimeMillis() - startTime);
 
         return result;