add flink datasources and sinks

code/Flink/flink-time-watermark/pom.xml

@@ -144,7 +144,7 @@ under the License.
 							</filters>
 							<transformers>
 								<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
-									<mainClass>com.heibaiying.SampleJob</mainClass>
+									<mainClass>com.heibaiying.WaterMarkJob</mainClass>
 								</transformer>
 							</transformers>
 						</configuration>

code/Flink/flink-time-watermark/src/main/java/com/heibaiying/PeriodicWatermarksJob.java

@@ -0,0 +1,52 @@
+package com.heibaiying;
+
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.java.tuple.Tuple3;
+import org.apache.flink.streaming.api.TimeCharacteristic;
+import org.apache.flink.streaming.api.datastream.DataStreamSource;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarks;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.api.windowing.time.Time;
+
+public class PeriodicWatermarksJob {
+
+    public static void main(String[] args) throws Exception {
+
+        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
+        // 设置并行度为1
+        env.setParallelism(1);
+        // 设置以事件时间为基准
+        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
+        DataStreamSource<String> streamSource = env.socketTextStream("192.168.200.229", 8888, "\n", 3);
+        streamSource.map(new MapFunction<String, Tuple3<Long, String, Long>>() {
+            @Override
+            public Tuple3<Long, String, Long> map(String value) throws Exception {
+                String[] split = value.split(",");
+                return new Tuple3<>(Long.valueOf(split[0]), split[1], 1L);
+            }
+        }).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessGenerator())
+                .keyBy(1).timeWindow(Time.seconds(3)).sum(2).print();
+        env.execute();
+
+    }
+}
+
+
+class BoundedOutOfOrdernessGenerator implements AssignerWithPeriodicWatermarks<Tuple3<Long, String, Long>> {
+
+    private final long maxOutOfOrderness = 3000L;
+    private long currentMaxTimestamp = 0L;
+
+    @Override
+    public long extractTimestamp(Tuple3<Long, String, Long> element, long previousElementTimestamp) {
+        long timestamp = element.f0;
+        currentMaxTimestamp = Math.max(timestamp, currentMaxTimestamp);
+        return timestamp;
+    }
+
+    @Override
+    public Watermark getCurrentWatermark() {
+        return new Watermark(currentMaxTimestamp - maxOutOfOrderness);
+    }
+}

code/Flink/flink-time-watermark/src/main/java/com/heibaiying/PunctuatedWatermarksJob.java

@@ -0,0 +1,46 @@
+package com.heibaiying;
+
+import org.apache.flink.api.common.functions.MapFunction;
+import org.apache.flink.api.java.tuple.Tuple3;
+import org.apache.flink.streaming.api.TimeCharacteristic;
+import org.apache.flink.streaming.api.datastream.DataStreamSource;
+import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
+import org.apache.flink.streaming.api.functions.AssignerWithPunctuatedWatermarks;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.api.windowing.time.Time;
+
+public class PunctuatedWatermarksJob {
+
+    public static void main(String[] args) throws Exception {
+
+        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
+        env.setParallelism(1);
+        // 设置以事件时间为基准
+        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
+        DataStreamSource<String> streamSource = env.socketTextStream("192.168.200.229", 8888, "\n", 3);
+        streamSource.map(new MapFunction<String, Tuple3<Long, String, Long>>() {
+            @Override
+            public Tuple3<Long, String, Long> map(String value) throws Exception {
+                String[] split = value.split(",");
+                return new Tuple3<>(Long.valueOf(split[0]), split[1], 1L);
+            }
+        }).assignTimestampsAndWatermarks(new PunctuatedAssigner())
+                .keyBy(1).timeWindow(Time.seconds(3)).sum(2).print();
+        env.execute();
+
+    }
+}
+
+class PunctuatedAssigner implements AssignerWithPunctuatedWatermarks<Tuple3<Long, String, Long>> {
+
+    @Override
+    public long extractTimestamp(Tuple3<Long, String, Long> element, long previousElementTimestamp) {
+        return element.f0;
+    }
+
+    @Override
+    public Watermark checkAndGetNextWatermark(Tuple3<Long, String, Long> lastElement, long extractedTimestamp) {
+        return new Watermark(extractedTimestamp);
+    }
+}
+

code/Flink/flink-time-watermark/src/main/java/com/heibaiying/SampleJob.java

@@ -1,27 +0,0 @@
-package com.heibaiying;
-
-import org.apache.flink.api.common.functions.FlatMapFunction;
-import org.apache.flink.api.java.tuple.Tuple2;
-import org.apache.flink.streaming.api.datastream.DataStreamSource;
-import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
-import org.apache.flink.streaming.api.windowing.time.Time;
-import org.apache.flink.util.Collector;
-
-public class SampleJob {
-
-    public static void main(String[] args) throws Exception {
-
-        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
-        DataStreamSource<String> streamSource = env.socketTextStream("192.168.200.229", 9999, "\n", 3);
-        streamSource.flatMap(new FlatMapFunction<String, Tuple2<String, Long>>() {
-            @Override
-            public void flatMap(String value, Collector<Tuple2<String, Long>> out) throws Exception {
-                String[] words = value.split("\t");
-                for (String word : words) {
-                    out.collect(new Tuple2<>(word, 1L));
-                }
-            }
-        }).keyBy(0).timeWindow(Time.seconds(3)).sum(1).print();
-        env.execute("Flink Streaming");
-    }
-}

code/Flink/flink-time-watermark/src/main/resources/sample.txt

@@ -0,0 +1,18 @@
+1572501901000,hadoop
+1572501902000,hadoop
+1572501903000,hadoop
+1572501904000,flink
+1572501905000,spark
+1572501906000,spark ->  (1572501901000,hadoop,2)
+1572501907000,hive
+1572501908000,hive
+1572501909000,hive  ->  (1572501903000,hadoop,1)
+                        (1572501905000,spark ,1)
+                        (1572501904000,flink,1)
+1572501910000,spark
+1572501911000,storm
+1572501912000,storm ->  (1572501906000,spark,1)
+                        (1572501907000,hive,2)
+1572501915000,yarn  ->  (1572501911000,storm,1)
+                        (1572501909000,hive,1)
+                        (1572501910000,spark,1)