大数据中台在公司中的应用与实践

// 使用Flink创建数据流处理任务

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

DataStream stream = env.socketTextStream("localhost", 9999);

// 处理数据流

DataStream alarms = stream.map(new MapFunction() {

@Override

public Alarm map(String value) throws Exception {

// 解析数据并生成报警对象

return new Alarm(value);

}

});

// 发送到Kafka

alarms.addSink(new FlinkKafkaProducer<>("alarm-topic", new SimpleStringSchema(), properties));

// 在Spark中读取Kafka数据

JavaSparkContext sc = new JavaSparkContext(conf);

JavaDStream kafkaStream = KafkaUtils.createDirectStream(

sc,

LocationStrategies.PreferConsistent(),

ConsumerStrategies.Subscribe(topics, kafkaParams)

);

// 进行数据分析

kafkaStream.foreachRDD(rdd -> {

rdd.foreachPartition(partition -> {

while (partition.hasNext()) {

Alarm alarm = partition.next();

// 分析并记录异常信息

analyzeAndLog(alarm);

}

});

});

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