适配Nacos动态更新schema(GAL-144)
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@@ -30,13 +30,11 @@ public class StreamAggregateTopology {
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try {
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final StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
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// environment.enableCheckpointing(5000);
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//两个输出之间的最大时间 (单位milliseconds)
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environment.setBufferTimeout(StreamAggregateConfig.BUFFER_TIMEOUT);
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DataStream<String> streamSource = environment.addSource(KafkaConsumer.getKafkaConsumer())
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.setParallelism(StreamAggregateConfig.SOURCE_PARALLELISM);
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.setParallelism(StreamAggregateConfig.SOURCE_PARALLELISM).name(StreamAggregateConfig.SOURCE_KAFKA_TOPIC);
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SingleOutputStreamOperator<Tuple3<String, String, String>> parseDataMap = streamSource.map(new ParseMapFunction())
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.name("ParseDataMap")
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@@ -55,8 +53,11 @@ public class StreamAggregateTopology {
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SingleOutputStreamOperator<String> secondCountWindow = secondWindow.process(new SecondCountWindowFunction())
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.name("SecondCountWindow").setParallelism(StreamAggregateConfig.SECOND_WINDOW_PARALLELISM);
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secondCountWindow.flatMap(new ResultFlatMapFunction()).name("ResultFlatMap").setParallelism(StreamAggregateConfig.SINK_PARALLELISM)
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.addSink(KafkaProducer.getKafkaProducer()).name("LogSinkKafka").setParallelism(StreamAggregateConfig.SINK_PARALLELISM);
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SingleOutputStreamOperator<String> resultFlatMap = secondCountWindow.flatMap(new ResultFlatMapFunction())
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.name("ResultFlatMap").setParallelism(StreamAggregateConfig.SINK_PARALLELISM);
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resultFlatMap.addSink(KafkaProducer.getKafkaProducer()).name("LogSinkKafka")
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.setParallelism(StreamAggregateConfig.SINK_PARALLELISM).name(StreamAggregateConfig.SINK_KAFKA_TOPIC);
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environment.execute(args[0]);
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} catch (Exception e) {
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