缓存细化

@@ -52,7 +52,8 @@ public interface CacheClient {
      * @param <T> 类型
      * @return key-response的map
      */
-    public <K, T> Map<K, T> getBulk(List<RedisKeyBuilder> keys, Class<T> clazz, Function<List<K>, Map<K, T>> nullCacheFun, Function<String, K> keyConvertFun, long timeOut, String cacheKeyPre, Map<String, K> cacheKeyAndStringMap);
+    public <K, T> Map<K, T> getBulk(List<RedisKeyBuilder> keys, Class<T> clazz, Function<List<K>, Map<K, T>> nullCacheFun, Function<String, K> keyConvertFun,
+                                    long timeOut, String cacheKeyPre, Map<String, K> cacheKeyAndStringMap);
 
 
     /**

@@ -246,17 +246,18 @@ public class RedisGwCacheClient implements CacheClient {
 					result.put(redisKeyBuilders.get(i).getKey(), JSON.parseObject(curItem, clazz));
 				}
 			}
+			Map<K, T> lastResult = new HashMap<>();
 
 			Map<K, T> apply = nullCacheFun.apply(nullCachekeys);      // 执行回调函数去数据库查询未缓存的键值集合
+
 			for(Map.Entry<K, T> item : apply.entrySet()) {
-				result.put(String.valueOf(item.getKey()), item.getValue());
+				lastResult.put(item.getKey(), item.getValue());
 			}
 
 			setBulk(apply, timeOut, cacheKeyPre, clazz);    // 将不在缓存的键值对批量放入缓存
 
-			Map<K, T> lastResult = new HashMap<>();
 			for(Map.Entry<String, T> entry : result.entrySet()) {
-				lastResult.put(keyConvertFun.apply(entry.getKey()), entry.getValue());
+				lastResult.put(cacheKeyAndStringMap.get(entry.getKey()), entry.getValue());
 			}
 
 			return lastResult;