toJSONString替换为fastjson工具类

src/main/java/com/zdjizhi/topology/LogFlowWriteTopology.java

@@ -28,8 +28,8 @@ public class LogFlowWriteTopology {
         //开启Checkpoint，interval用于指定checkpoint的触发间隔(单位milliseconds)
 //        environment.enableCheckpointing(5000);
 
-        //
-        environment.setBufferTimeout(5000);
+        //两个输出之间的最大时间 (单位milliseconds)
+        environment.setBufferTimeout(FlowWriteConfig.BUFFER_TIMEOUT);
 
         DataStreamSource<String> streamSource = environment.addSource(Consumer.getKafkaConsumer())
                 .setParallelism(FlowWriteConfig.SOURCE_PARALLELISM);
@@ -41,26 +41,31 @@ public class LogFlowWriteTopology {
                     //对原始日志进行处理补全转换等，不对日志字段类型做校验。
                     cleaningLog = streamSource.map(new MapCompletedFunction()).name("MapCompletedFunction")
                             .setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
+
                     break;
                 case 1:
                     //对原始日志进行处理补全转换等，强制要求日志字段类型与schema一致。
                     cleaningLog = streamSource.map(new ObjectCompletedFunction()).name("ObjectCompletedFunction")
                             .setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
+
                     break;
                 case 2:
                     //对原始日志进行处理补全转换等，对日志字段类型做若校验，可根据schema进行强转。
                     cleaningLog = streamSource.map(new TypeMapCompletedFunction()).name("TypeMapCompletedFunction")
                             .setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
+
                     break;
                 default:
                     //对原始日志进行处理补全转换等，不对日志字段类型做校验。
                     cleaningLog = streamSource.map(new MapCompletedFunction()).name("MapCompletedFunction")
                             .setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
+
             }
 
-            //过滤空数据不发送到Kafka内
+//            //过滤空数据不发送到Kafka内
             DataStream<String> result = cleaningLog.filter(new FilterNullFunction()).name("FilterAbnormalData")
                     .setParallelism(FlowWriteConfig.TRANSFORM_PARALLELISM);
+
             //发送数据到Kafka
             result.addSink(Producer.getKafkaProducer()).name("LogSinkKafka")
                     .setParallelism(FlowWriteConfig.SINK_PARALLELISM);