Spark2.3（三十四）：Spark Structured Streaming之withWaterMark和windows窗口是否可以实现最近一小时统计
WaterMark除了可以限定来迟数据范围，是否可以实现最近一小时统计？
WaterMark目的用来限定参数计算数据的范围：比如当前计算数据内max timestamp是12::00，waterMark限定数据分为是60 minutes，那么如果此时输入11:00之前的数据就会被舍弃不参与统计，视为来迟范围超出了60minutes限定范围。
那么，是否可以借助它实现最近一小时的数据统计呢？
代码示例：
package com.dx.streaming import java.sql.Timestamp
import java.text.SimpleDateFormat import org.apache.spark.sql.streaming.OutputMode
import org.apache.spark.sql.{Encoders, SparkSession}
import org.apache.log4j.{Level, Logger} case class MyEntity(id: String, timestamp: Timestamp, value: Integer) object Main {
Logger.getLogger("org.apache.spark").setLevel(Level.WARN);
Logger.getLogger("akka").setLevel(Level.ERROR);
Logger.getLogger("kafka").setLevel(Level.ERROR); def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("test").master("local[*]").getOrCreate()
val lines = spark.readStream.format("socket").option("host", "192.168.0.141").option("port", 19999).load() var sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
import spark.implicits._
lines.as(Encoders.STRING)
.map(row => {
val fields = row.split(",")
MyEntity(fields(0), new Timestamp(sdf.parse(fields(1)).getTime), Integer.valueOf(fields(2)))
})
.createOrReplaceTempView("tv_entity") spark.sql("select id,timestamp,value from tv_entity")
.withWatermark("timestamp", "60 minutes")
.createOrReplaceTempView("tv_entity_watermark") val resultDf = spark.sql(
s"""
|select id,sum(value) as sum_value
|from tv_entity_watermark
|group id
|""".stripMargin) val query = resultDf.writeStream.format("console").outputMode(OutputMode.Update()).start() query.awaitTermination()
query.stop()
}
}
当通过nc -lk 19999中依次（每组输入间隔几秒时间即可）输入如下数据时：
1,2018-12-01 12:00:01,100
2,2018-12-01 12:00:01,100 1,2018-12-01 12:05:01,100
2,2018-12-01 12:05:01,100 1,2018-12-01 12:15:01,100
2,2018-12-01 12:15:01,100 1,2018-12-01 12:25:01,100
2,2018-12-01 12:25:01,100 1,2018-12-01 12:35:01,100
2,2018-12-01 12:35:01,100 1,2018-12-01 12:45:01,100
2,2018-12-01 12:45:01,100 1,2018-12-01 12:55:01,100
2,2018-12-01 12:55:01,100 1,2018-12-01 13:05:02,100
2,2018-12-01 13:05:02,100 1,2018-12-01 13:15:01,100
2,2018-12-01 13:15:01,100
发现最终统计结果为：
id , sum_value
,
,
而不是期望的
id , sum_value
,
,
既然是不能限定数据统计范围是60minutes，是否需要借助于窗口函数window就可以实现呢？
是否需要借助于watermark和窗口函数window就可以实现最近1小时数据统计呢？
spark.sql("select id,timestamp,value from tv_entity")
.withWatermark("timestamp", "60 minutes")
.createOrReplaceTempView("tv_entity_watermark")
val resultDf = spark.sql(
s"""
|select id,sum(value) as sum_value
|from tv_entity_watermark
|group window(timestamp,'60 minutes','60 minutes'),id
|""".stripMargin)
val query = resultDf.writeStream.format("console").outputMode(OutputMode.Update()).start()
依然输入上边的测试数据，会发现超过1小时候数据会重新开辟（归零后重新统计）一个统计结果，而不是滚动的一小时统计。
就是把上边的测试数据分为了两组来分别统计：
第一组（小时）参与统计数据：
,-- ::,
,-- ::, ,-- ::,
,-- ::, ,-- ::,
,-- ::, ,-- ::,
,-- ::, ,-- ::,
,-- ::, ,-- ::,
,-- ::, ,-- ::,
,-- ::,
第二组（小时）参与统计数据：
,-- ::,
,-- ::, ,-- ::,
,-- ::,
猜测总结：
根据上边测试结果可以推出一个猜测结论：
在spark structured streaming中是不存储参数统计的数据的，只是对数据进行了maxTimestamp.avgTimestamp,minTimestamp存储，同时只是对数据的统计结果进行存储，下次再次触发统计时只是在原有的统计结果之上进行累加等操作，而参与统计的数据应该是没有存储，否则这类需求应该是可以实现。
但是以下代码尝试确实是可以实现，缺点太耗费资源：
package com.dx.streaming import java.sql.Timestamp
import java.text.SimpleDateFormat import org.apache.spark.sql.streaming.OutputMode
import org.apache.spark.sql.{Encoders, SparkSession}
import org.apache.log4j.{Level, Logger} case class MyEntity(id: String, timestamp: Timestamp, value: Integer) object Main {
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("akka").setLevel(Level.ERROR)
Logger.getLogger("kafka").setLevel(Level.ERROR) def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("test").master("local[*]").getOrCreate()
val lines = spark.readStream.format("socket").option("host", "192.168.0.141").option("port", 19999).load() var sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
import spark.implicits._
lines.as(Encoders.STRING)
.map(row => {
val fields = row.split(",")
MyEntity(fields(0), new Timestamp(sdf.parse(fields(1)).getTime), Integer.valueOf(fields(2)))
})
.createOrReplaceTempView("tv_entity") spark.sql("select id,timestamp,value from tv_entity")
.withWatermark("timestamp", "60 minutes")
.createOrReplaceTempView("tv_entity_watermark") var resultDf = spark.sql(
s"""
|select id,min(timestamp) min_timestamp,max(timestamp) max_timestamp,sum(value) as sum_value
|from tv_entity_watermark
|group by window(timestamp,'3600 seconds','60 seconds'),id
|""".stripMargin) val query = resultDf.writeStream.format("console").outputMode(OutputMode.Update()).start() query.awaitTermination()
query.stop()
}
}
使用spark streaming把历史结果保存到内存中实现最近一小时统计：
pom.xml
<!--Spark -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>2.2.0</version>
</dependency>
java code:
package com.dx.streaming; import java.io.Serializable;
import java.sql.Timestamp;
import java.text.SimpleDateFormat;
import java.util.ArrayList;
import java.util.Date;
import java.util.HashMap;
import java.util.List;
import java.util.Map; import org.apache.log4j.Level;
import org.apache.log4j.LogManager;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.VoidFunction;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext; public class Main {
private static List<MyEntity> store = new ArrayList<MyEntity>();
private static JavaStreamingContext jssc; public static void main(String[] args) throws Exception {
// set log4j programmatically
LogManager.getLogger("org.apache.spark").setLevel(Level.WARN);
LogManager.getLogger("akka").setLevel(Level.ERROR);
LogManager.getLogger("kafka").setLevel(Level.ERROR); SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
//
System.out.println(sdf.parse("2018-12-04 11:00:00").getTime() - sdf.parse("2018-12-04 10:00:00").getTime()); SparkConf conf = new SparkConf().setMaster("local[*]").setAppName("NetworkWordCount");
JavaSparkContext sc = new JavaSparkContext(conf);
// jssc = new JavaStreamingContext(conf, Durations.seconds(10));
jssc = new JavaStreamingContext(sc, Durations.seconds(10)); JavaReceiverInputDStream<String> lines = jssc.socketTextStream("192.168.0.141", 19999); JavaDStream<MyEntity> dStream = lines.map(new Function<String, MyEntity>() {
private static final long serialVersionUID = 1L; public MyEntity call(String line) throws Exception {
String[] fields = line.split(",");
MyEntity myEntity = new MyEntity();
myEntity.setId(Integer.valueOf(fields[0]));
myEntity.setTimestamp(Timestamp.valueOf(fields[1]));
myEntity.setValue(Long.valueOf(fields[2]));
return myEntity;
}
}); // 不确定是否必须repartition(1)，目的避免外边这层循环多次循环，确保只执行一次大循环。
dStream.repartition(1).foreachRDD(new VoidFunction<JavaRDD<MyEntity>>() {
public void call(JavaRDD<MyEntity> tItems) throws Exception {
System.out.println("print...");
tItems.foreach(new VoidFunction<MyEntity>() {
public void call(MyEntity t) throws Exception {
System.out.println(">>>>>>>>>>>>>" + t.toString());
store.add(t);
System.out.println(store.size());
}
}); System.out.println("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@");
for (MyEntity myEntity : store) {
System.out.println("++++++++++++++++++++++" + myEntity.toString());
} // 第一步：从store中超過1小時之前的數據剔除；
MyEntity first = store.get(0);
MyEntity last = store.get(store.size() - 1);
// 超過一小時（这里为什么这么做，假设数据本身就是按照时间循序有序插入的，实际业务中如果相同可以这样做）
while (last.getTimestamp().getTime() - first.getTimestamp().getTime() > 3600000) {
store.remove(0);
first = store.get(0);
} // 第二步：執行業務統計代碼
Map<Integer, Long> statistics = new HashMap<Integer, Long>();
for (MyEntity myEntity : store) {
if (false == statistics.containsKey(myEntity.getId())) {
statistics.put(myEntity.getId(), myEntity.getValue());
} else {
statistics.put(myEntity.getId(), myEntity.getValue() + statistics.get(myEntity.getId()));
}
} // 第三步：将结果写入关系数据库
System.out.println("#######################print result##########################");
for (Map.Entry<Integer, Long> kv : statistics.entrySet()) {
System.out.println(kv.getKey() + "," + kv.getValue());
}
}
}); jssc.start(); // Start the computation
jssc.awaitTermination(); // Wait for the computation to terminate
}
} class MyEntity implements Serializable {
private final SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
private int id;
private Timestamp timestamp;
private long value; public int getId() {
return id;
} public void setId(int id) {
this.id = id;
} public Timestamp getTimestamp() {
return timestamp;
} public void setTimestamp(Timestamp timestamp) {
this.timestamp = timestamp;
} public long getValue() {
return value;
} public void setValue(long value) {
this.value = value;
} @Override
public String toString() {
return getId() + "," + sdf.format(new Date(getTimestamp().getTime())) + "," + getValue();
}
}
输出日志
// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@62d73ead{/streaming/batch,null,AVAILABLE,@Spark}
// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@228cea97{/streaming/batch/json,null,AVAILABLE,@Spark}
// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3db663d0{/static/streaming,null,AVAILABLE,@Spark}
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
[Stage :> ( + ) / ]>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
[Stage :> ( + ) / ]>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
// :: WARN storage.RandomBlockReplicationPolicy: Expecting replicas with only peer/s.
// :: WARN storage.BlockManager: Block input-- replicated to only peer(s) instead of peers
print...
>>>>>>>>>>>>>,-- ::,
>>>>>>>>>>>>>,-- ::,
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
print...
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
++++++++++++++++++++++,-- ::,
#######################print result##########################
,
,
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