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Flink DataStream样例程序(Java)
更新时间:2024-08-05 GMT+08:00
Flink DataStream样例程序(Java)
功能介绍
统计连续网购时间超过2个小时的女性网民信息,将统计结果直接打印。
代码样例
下面代码片段仅为演示,完整代码参见FlinkStreamJavaExample样例工程下的com.huawei.bigdata.flink.examples.FlinkStreamJavaExample:
// 参数解析: // <filePath>为文本读取路径,用逗号分隔。 // <windowTime>为统计数据的窗口跨度,时间单位都是分。 public class FlinkStreamJavaExample { public static void main(String[] args) throws Exception { // 打印出执行flink run的参考命令 System.out.println("use command as: "); System.out.println("./bin/flink run --class com.huawei.bigdata.flink.examples.FlinkStreamJavaExample /opt/test.jar --filePath /opt/log1.txt,/opt/log2.txt --windowTime 2"); System.out.println("******************************************************************************************"); System.out.println("<filePath> is for text file to read data, use comma to separate"); System.out.println("<windowTime> is the width of the window, time as minutes"); System.out.println("******************************************************************************************"); // 读取文本路径信息,并使用逗号分隔 final String[] filePaths = ParameterTool.fromArgs(args).get("filePath", "/opt/log1.txt,/opt/log2.txt").split(","); assert filePaths.length > 0; // windowTime设置窗口时间大小,默认2分钟一个窗口足够读取文本内的所有数据了 final int windowTime = ParameterTool.fromArgs(args).getInt("windowTime", 2); // 构造执行环境,使用eventTime处理窗口数据 final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); env.setParallelism(1); // 读取文本数据流 DataStream<String> unionStream = env.readTextFile(filePaths[0]); if (filePaths.length > 1) { for (int i = 1; i < filePaths.length; i++) { unionStream = unionStream.union(env.readTextFile(filePaths[i])); } } // 数据转换,构造整个数据处理的逻辑,计算并得出结果打印出来 unionStream.map(new MapFunction<String, UserRecord>() { @Override public UserRecord map(String value) throws Exception { return getRecord(value); } }).assignTimestampsAndWatermarks( new Record2TimestampExtractor() ).filter(new FilterFunction<UserRecord>() { @Override public boolean filter(UserRecord value) throws Exception { return value.sexy.equals("female"); } }).keyBy( new UserRecordSelector() ).window( TumblingEventTimeWindows.of(Time.minutes(windowTime)) ).reduce(new ReduceFunction<UserRecord>() { @Override public UserRecord reduce(UserRecord value1, UserRecord value2) throws Exception { value1.shoppingTime += value2.shoppingTime; return value1; } }).filter(new FilterFunction<UserRecord>() { @Override public boolean filter(UserRecord value) throws Exception { return value.shoppingTime > 120; } }).print(); // 调用execute触发执行 env.execute("FemaleInfoCollectionPrint java"); } // 构造keyBy的关键字作为分组依据 private static class UserRecordSelector implements KeySelector<UserRecord, Tuple2<String, String>> { @Override public Tuple2<String, String> getKey(UserRecord value) throws Exception { return Tuple2.of(value.name, value.sexy); } } // 解析文本行数据,构造UserRecord数据结构 private static UserRecord getRecord(String line) { String[] elems = line.split(","); assert elems.length == 3; return new UserRecord(elems[0], elems[1], Integer.parseInt(elems[2])); } // UserRecord数据结构的定义,并重写了toString打印方法 public static class UserRecord { private String name; private String sexy; private int shoppingTime; public UserRecord(String n, String s, int t) { name = n; sexy = s; shoppingTime = t; } public String toString() { return "name: " + name + " sexy: " + sexy + " shoppingTime: " + shoppingTime; } } // 构造继承AssignerWithPunctuatedWatermarks的类,用于设置eventTime以及waterMark private static class Record2TimestampExtractor implements AssignerWithPunctuatedWatermarks<UserRecord> { // add tag in the data of datastream elements @Override public long extractTimestamp(UserRecord element, long previousTimestamp) { return System.currentTimeMillis(); } // give the watermark to trigger the window to execute, and use the value to check if the window elements is ready @Override public Watermark checkAndGetNextWatermark(UserRecord element, long extractedTimestamp) { return new Watermark(extractedTimestamp - 1); } } }
执行之后打印结果如下所示:
name: FangBo sexy: female shoppingTime: 320 name: CaiXuyu sexy: female shoppingTime: 300
执行如图1所示。
父主题: Flink DataStream样例程序