加监控

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/common/base/RankItem.java

@@ -30,7 +30,7 @@ public class RankItem implements Comparable<RankItem> {
     private Video video;
     private double scoreRos; // 记录ros的score
     private double scoreStr; // 记录str的score
-    private double scoreRov; // 记录str的score
+    private double scoreRov; // 记录rov的score
 
     // 记录Item侧用到的特征
     private ItemFeature itemFeature;

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/recall/strategy/FlowPoolWithLevelRecallStrategyTomson.java

@@ -113,65 +113,74 @@ public class FlowPoolWithLevelRecallStrategyTomson extends AbstractFlowPoolWithL
 
     @Override
     public List<Video> recall(RecallParam param) {
-        Pair<String, String> flowPoolKeyAndLevel = flowPoolKeyAndLevel(param);
-        String flowPoolKey = flowPoolKeyAndLevel.getLeft();
-        String level = flowPoolKeyAndLevel.getRight();
-        Set<String> data = redisTemplate.opsForSet().members(flowPoolKey);
-        if (CollectionUtils.isEmpty(data)) {
-            return null;
-        }
-        Map<String, String> videoFlowPoolMap = new LinkedHashMap<>();
-        Map<Long, String> videoFlowPoolMap_ = new LinkedHashMap<>();
-        for (String value : data) {
-            String[] values = value.split("-");
-            videoFlowPoolMap.put(values[0], values[1]);
-            videoFlowPoolMap_.put(NumberUtils.toLong(values[0], 0), values[1]);
-        }
-        Map<Long, Double> resultmap = null;
-        if ("1".equals(level) && ifOneLevelRandom){
-            // 流量池一层改为全随机
-            int limitSize = 60;
-            List<Long> keyList = new ArrayList<>(videoFlowPoolMap_.keySet());
-            Collections.shuffle(keyList);
-            resultmap = keyList.stream().limit(limitSize).collect(Collectors.toMap(
-                    key -> key,
-                    key -> Math.random()
-            ));
-        }else{
-            ScorerPipeline4Recall pipeline = ScorerUtils.getScorerPipeline4Recall("feeds_recall_config_tomson.conf");
-            List<List<Pair<Long, Double>>> results = pipeline.recall(videoFlowPoolMap);
-            List<Pair<Long, Double>> result = results.get(0);
-            resultmap = result.stream()
-                .collect(Collectors.toMap(
-                        Pair::getLeft, // 键是Pair的left值
-                        Pair::getRight, // 值是Pair的right值
-                        (existingValue, newValue) -> existingValue, // 如果键冲突，选择保留现有的值（或者你可以根据需要定义其他合并策略）
-                        LinkedHashMap::new // 使用LinkedHashMap来保持插入顺序（如果需要的话）
+        try {
+
+
+            Pair<String, String> flowPoolKeyAndLevel = flowPoolKeyAndLevel(param);
+            String flowPoolKey = flowPoolKeyAndLevel.getLeft();
+            String level = flowPoolKeyAndLevel.getRight();
+            Set<String> data = redisTemplate.opsForSet().members(flowPoolKey);
+            if (CollectionUtils.isEmpty(data)) {
+                return null;
+            }
+            Map<String, String> videoFlowPoolMap = new LinkedHashMap<>();
+            Map<Long, String> videoFlowPoolMap_ = new LinkedHashMap<>();
+            for (String value : data) {
+                String[] values = value.split("-");
+                videoFlowPoolMap.put(values[0], values[1]);
+                videoFlowPoolMap_.put(NumberUtils.toLong(values[0], 0), values[1]);
+            }
+            Map<Long, Double> resultmap = null;
+            if ("1".equals(level) && ifOneLevelRandom) {
+                // 流量池一层改为全随机
+                int limitSize = 60;
+                List<Long> keyList = new ArrayList<>(videoFlowPoolMap_.keySet());
+                Collections.shuffle(keyList);
+                resultmap = keyList.stream().limit(limitSize).collect(Collectors.toMap(
+                        key -> key,
+                        key -> Math.random()
                 ));
-        }
+            } else {
+                ScorerPipeline4Recall pipeline = ScorerUtils.getScorerPipeline4Recall("feeds_recall_config_tomson.conf");
+                List<List<Pair<Long, Double>>> results = pipeline.recall(videoFlowPoolMap);
+                List<Pair<Long, Double>> result = results.get(0);
+                resultmap = result.stream()
+                        .collect(Collectors.toMap(
+                                Pair::getLeft, // 键是Pair的left值
+                                Pair::getRight, // 值是Pair的right值
+                                (existingValue, newValue) -> existingValue, // 如果键冲突，选择保留现有的值（或者你可以根据需要定义其他合并策略）
+                                LinkedHashMap::new // 使用LinkedHashMap来保持插入顺序（如果需要的话）
+                        ));
+            }
 
-        // 3 召回内部过滤
-        FilterParam filterParam = FilterParamFactory.create(param, new ArrayList<>(resultmap.keySet()));
-        filterParam.setForceTruncation(10000);
-        filterParam.setConcurrent(true);
-        filterParam.setNotUsePreView(false);
-        FilterResult filterResult = filterService.filter(filterParam);
-        List<Video> videosResult = new ArrayList<>();
-        if (filterResult != null && CollectionUtils.isNotEmpty(filterResult.getVideoIds())) {
-            Map<Long, Double> finalResultmap = resultmap;
-            filterResult.getVideoIds().forEach(vid -> {
-                Video recallData = new Video();
-                recallData.setVideoId(vid);
-                recallData.setAbCode(param.getAbCode());
-                recallData.setRovScore(finalResultmap.getOrDefault(vid, 0.0));
-                recallData.setPushFrom(pushFrom());
-                recallData.setFlowPool(videoFlowPoolMap_.get(vid));
-                recallData.setFlowPoolAbtestGroup(param.getFlowPoolAbtestGroup());
-                recallData.setLevel(level);
-                videosResult.add(recallData);
-            });
+            // 3 召回内部过滤
+            FilterParam filterParam = FilterParamFactory.create(param, new ArrayList<>(resultmap.keySet()));
+            filterParam.setForceTruncation(10000);
+            filterParam.setConcurrent(true);
+            filterParam.setNotUsePreView(false);
+            FilterResult filterResult = filterService.filter(filterParam);
+            List<Video> videosResult = new ArrayList<>();
+            if (filterResult != null && CollectionUtils.isNotEmpty(filterResult.getVideoIds())) {
+                Map<Long, Double> finalResultmap = resultmap;
+                filterResult.getVideoIds().forEach(vid -> {
+                    Video recallData = new Video();
+                    recallData.setVideoId(vid);
+                    recallData.setAbCode(param.getAbCode());
+                    recallData.setRovScore(finalResultmap.getOrDefault(vid, 0.0));
+                    recallData.setPushFrom(pushFrom());
+                    recallData.setFlowPool(videoFlowPoolMap_.get(vid));
+                    recallData.setFlowPoolAbtestGroup(param.getFlowPoolAbtestGroup());
+                    recallData.setLevel(level);
+                    videosResult.add(recallData);
+                });
+            }
+            videosResult.sort(Comparator.comparingDouble(o -> -o.getRovScore()));
+
+            return videosResult;
+        }
+        catch (Exception e){
+            log.error("liuliangchi", e);
         }
-        videosResult.sort(Comparator.comparingDouble(o -> -o.getRovScore()));
-        return videosResult;
+        return Collections.emptyList();
     }
 }