这篇文章主要介绍“Flink简单项目整体流程是怎样的”，在日常操作中，相信很多人在Flink简单项目整体流程是怎样的问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”Flink简单项目整体流程是怎样的”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
项目概述
CDN热门分发网络，日志数据分析，日志数据内容包括
计算
安全
数据库
网络和加速
企业服务
aliyun
CN
E
[17/Jul/2018:17:07:50 +0800]
223.104.18.110
v2.go2yd.com
17168接入的数据类型就是日志
离线：Flume==>HDFS
实时: Kafka==>流处理引擎==>ES==>Kibana
数据查询
| 接口名 | 功能描述 |
|---|---|
| 汇总统计查询 | 峰值带宽 总流量 总请求数 |
项目功能
统计一分钟内每个域名访问产生的流量，Flink接收Kafka的数据进行处理
统计一分钟内每个用户产生的流量，域名和用户是有对应关系的，Flink接收Kafka的数据进行处理+Flink读取域名和用户的配置数据(在MySQL中)进行处理
项目架构
Mock数据
@Component@Slf4jpublic class KafkaProducer {private static final String TOPIC = "pktest"; @Autowired private KafkaTemplate<String,String> kafkaTemplate; @SuppressWarnings("unchecked")public void produce(String message) {try {
ListenableFuture future = kafkaTemplate.send(TOPIC, message); SuccessCallback<SendResult<String,String>> successCallback = new SuccessCallback<SendResult<String, String>>() {@Override public void onSuccess(@Nullable SendResult<String, String> result) {log.info("发送消息成功"); }
}; FailureCallback failureCallback = new FailureCallback() {@Override public void onFailure(Throwable ex) {log.error("发送消息失败",ex); produce(message); }
}; future.addCallback(successCallback,failureCallback); } catch (Exception e) {log.error("发送消息异常",e); }
}@Scheduled(fixedRate = 1000 * 2)public void send() {
StringBuilder builder = new StringBuilder(); builder.append("aliyun").append("\t")
.append("CN").append("\t")
.append(getLevels()).append("\t")
.append(new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
.format(new Date())).append("\t")
.append(getIps()).append("\t")
.append(getDomains()).append("\t")
.append(getTraffic()).append("\t"); log.info(builder.toString()); produce(builder.toString()); }/** * 生产Level数据 * @return */ private String getLevels() {
List<String> levels = Arrays.asList("M","E"); return levels.get(new Random().nextInt(levels.size())); }/** * 生产IP数据 * @return */ private String getIps() {
List<String> ips = Arrays.asList("222.104.18.111", "223.101.75.185", "27.17.127.133", "183.225.121.16", "112.1.65.32", "175.147.222.190", "183.227.43.68", "59.88.168.87", "117.28.44.29", "117.59.34.167"); return ips.get(new Random().nextInt(ips.size())); }/** * 生产域名数据 * @return */ private String getDomains() {
List<String> domains = Arrays.asList("v1.go2yd.com", "v2.go2vd.com", "v3.go2yd.com", "v4.go2yd.com", "vmi.go2yd.com"); return domains.get(new Random().nextInt(domains.size())); }/** * 生产流量数据 * @return */ private int getTraffic() {return new Random().nextInt(10000); }
}关于Springboot Kafka其他配置请参考Springboot2整合Kafka
打开Kafka服务器消费者，可以看到
说明Kafka数据发送成功
Flink消费者
public class LogAnalysis {public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); String topic = "pktest"; Properties properties = new Properties(); properties.setProperty("bootstrap.servers","外网ip:9092"); properties.setProperty("group.id","test"); DataStreamSource<String> data = env.addSource(new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties)); data.print().setParallelism(1); env.execute("LogAnalysis"); }
}接收到的消息
aliyun CN M 2021-01-31 23:43:07 222.104.18.111 v1.go2yd.com 4603
aliyun CN E 2021-01-31 23:43:09 222.104.18.111 v4.go2yd.com 6313
aliyun CN E 2021-01-31 23:43:11 222.104.18.111 v2.go2vd.com 4233
aliyun CN E 2021-01-31 23:43:13 222.104.18.111 v4.go2yd.com 2691
aliyun CN E 2021-01-31 23:43:15 183.225.121.16 v1.go2yd.com 212
aliyun CN E 2021-01-31 23:43:17 183.225.121.16 v4.go2yd.com 7744
aliyun CN M 2021-01-31 23:43:19 175.147.222.190 vmi.go2yd.com 1318数据清洗
数据清洗就是按照我们的业务规则把原始输入的数据进行一定业务规则的处理，使得满足我们业务需求为准
@Slf4jpublic class LogAnalysis {public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); String topic = "pktest"; Properties properties = new Properties(); properties.setProperty("bootstrap.servers","外网ip:9092"); properties.setProperty("group.id","test"); DataStreamSource<String> data = env.addSource(new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties)); data.map(new MapFunction<String, Tuple4<String, Long, String, String>>() {@Override public Tuple4<String, Long, String, String> map(String value) throws Exception {
String[] splits = value.split("\t"); String level = splits[2]; String timeStr = splits[3]; Long time = 0L; try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime(); } catch (ParseException e) {log.error("time转换错误:" + timeStr + "," + e.getMessage()); }
String domain = splits[5]; String traffic = splits[6]; return new Tuple4<>(level,time,domain,traffic); }
}).filter(x -> (Long) x.getField(1) != 0) //此处我们只需要Level为E的数据 .filter(x -> x.getField(0).equals("E")) //抛弃level .map(new MapFunction<Tuple4<String,Long,String,String>, Tuple3<Long,String,Long>>() { @Override public Tuple3<Long, String, Long> map(Tuple4<String, Long, String, String> value) throws Exception { return new Tuple3<>(value.getField(1),value.getField(2),Long.parseLong(value.getField(3))); }
}) .print().setParallelism(1); env.execute("LogAnalysis"); }
}运行结果
(1612130315000,v1.go2yd.com,533)
(1612130319000,v4.go2yd.com,8657)
(1612130321000,vmi.go2yd.com,4353)
(1612130327000,v1.go2yd.com,9566)
(1612130329000,v2.go2vd.com,1460)
(1612130331000,vmi.go2yd.com,1444)
(1612130333000,v3.go2yd.com,6955)
(1612130337000,v1.go2yd.com,9612)
(1612130341000,vmi.go2yd.com,1732)
(1612130345000,v3.go2yd.com,694)Scala代码
import java.text.SimpleDateFormatimport java.util.Propertiesimport org.apache.flink.api.common.serialization.SimpleStringSchemaimport org.apache.flink.streaming.api.scala.StreamExecutionEnvironmentimport org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerimport org.slf4j.LoggerFactoryimport org.apache.flink.api.scala._object LogAnalysis { val log = LoggerFactory.getLogger(LogAnalysis.getClass) def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment val topic = "pktest" val properties = new Properties
properties.setProperty("bootstrap.servers", "外网ip:9092")
properties.setProperty("group.id","test")val data = env.addSource(new FlinkKafkaConsumer[String](topic, new SimpleStringSchema, properties))
data.map(x => { val splits = x.split("\t") val level = splits(2) val timeStr = splits(3) var time: Long = 0l try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime
}catch {case e: Exception => { log.error(s"time转换错误: $timeStr",e.getMessage)
}
} val domain = splits(5) val traffic = splits(6)
(level,time,domain,traffic)
}).filter(_._2 != 0)
.filter(_._1 == "E")
.map(x => (x._2,x._3,x._4.toLong))
.print().setParallelism(1)
env.execute("LogAnalysis")
}
}数据分析
现在我们要分析的是在一分钟内的域名流量
@Slf4jpublic class LogAnalysis {public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); String topic = "pktest"; Properties properties = new Properties(); properties.setProperty("bootstrap.servers","外网ip:9092"); properties.setProperty("group.id","test"); DataStreamSource<String> data = env.addSource(new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties)); data.map(new MapFunction<String, Tuple4<String, Long, String, String>>() {@Override public Tuple4<String, Long, String, String> map(String value) throws Exception {
String[] splits = value.split("\t"); String level = splits[2]; String timeStr = splits[3]; Long time = 0L; try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime(); } catch (ParseException e) {log.error("time转换错误:" + timeStr + "," + e.getMessage()); }
String domain = splits[5]; String traffic = splits[6]; return new Tuple4<>(level,time,domain,traffic); }
}).filter(x -> (Long) x.getField(1) != 0) //此处我们只需要Level为E的数据 .filter(x -> x.getField(0).equals("E")) //抛弃level .map(new MapFunction<Tuple4<String,Long,String,String>, Tuple3<Long,String,Long>>() { @Override public Tuple3<Long, String, Long> map(Tuple4<String, Long, String, String> value) throws Exception { return new Tuple3<>(value.getField(1),value.getField(2),Long.parseLong(value.getField(3))); }
})
.setParallelism(1).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks<Tuple3<Long, String, Long>>() {private Long maxOutOfOrderness = 10000L; private Long currentMaxTimestamp = 0L; @Nullable @Override public Watermark getCurrentWatermark() {return new Watermark(currentMaxTimestamp - maxOutOfOrderness); }@Override public long extractTimestamp(Tuple3<Long, String, Long> element, long previousElementTimestamp) {
Long timestamp = element.getField(0); currentMaxTimestamp = Math.max(timestamp,currentMaxTimestamp); return timestamp; }
}).keyBy(x -> (String) x.getField(1))
.timeWindow(Time.minutes(1)) //输出格式：一分钟的时间间隔，域名，该域名在一分钟内的总流量 .apply(new WindowFunction<Tuple3<Long,String,Long>, Tuple3<String,String,Long>, String, TimeWindow>() { @Override public void apply(String s, TimeWindow window, Iterable<Tuple3<Long, String, Long>> input, Collector<Tuple3<String, String, Long>> out) throws Exception {
List<Tuple3<Long,String,Long>> list = (List) input; Long sum = list.stream().map(x -> (Long) x.getField(2)).reduce((x, y) -> x + y).get(); SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); out.collect(new Tuple3<>(format.format(window.getStart()) + " - " + format.format(window.getEnd()),s,sum)); }
})
.print().setParallelism(1); env.execute("LogAnalysis"); }
}运行结果
(2021-02-01 07:14:00 - 2021-02-01 07:15:00,vmi.go2yd.com,6307)
(2021-02-01 07:15:00 - 2021-02-01 07:16:00,v4.go2yd.com,15474)
(2021-02-01 07:15:00 - 2021-02-01 07:16:00,v2.go2vd.com,9210)
(2021-02-01 07:15:00 - 2021-02-01 07:16:00,v3.go2yd.com,190)
(2021-02-01 07:15:00 - 2021-02-01 07:16:00,v1.go2yd.com,12787)
(2021-02-01 07:15:00 - 2021-02-01 07:16:00,vmi.go2yd.com,14250)
(2021-02-01 07:16:00 - 2021-02-01 07:17:00,v4.go2yd.com,33298)
(2021-02-01 07:16:00 - 2021-02-01 07:17:00,v1.go2yd.com,37140)Scala代码
import java.text.SimpleDateFormatimport java.util.Propertiesimport org.apache.flink.api.common.serialization.SimpleStringSchemaimport org.apache.flink.streaming.api.scala.StreamExecutionEnvironmentimport org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerimport org.slf4j.LoggerFactoryimport org.apache.flink.api.scala._import org.apache.flink.streaming.api.TimeCharacteristicimport org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarksimport org.apache.flink.streaming.api.scala.function.WindowFunctionimport org.apache.flink.streaming.api.watermark.Watermarkimport org.apache.flink.streaming.api.windowing.time.Timeimport org.apache.flink.streaming.api.windowing.windows.TimeWindowimport org.apache.flink.util.Collectorobject LogAnalysis { val log = LoggerFactory.getLogger(LogAnalysis.getClass) def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)val topic = "pktest" val properties = new Properties
properties.setProperty("bootstrap.servers", "外网ip:9092")
properties.setProperty("group.id","test")val data = env.addSource(new FlinkKafkaConsumer[String](topic, new SimpleStringSchema, properties))
data.map(x => { val splits = x.split("\t") val level = splits(2) val timeStr = splits(3) var time: Long = 0l try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime
}catch {case e: Exception => { log.error(s"time转换错误: $timeStr",e.getMessage)
}
} val domain = splits(5) val traffic = splits(6)
(level,time,domain,traffic)
}).filter(_._2 != 0)
.filter(_._1 == "E")
.map(x => (x._2,x._3,x._4.toLong))
.setParallelism(1).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks[(Long, String, Long)] { var maxOutOfOrderness: Long = 10000l var currentMaxTimestamp: Long = _ override def getCurrentWatermark: Watermark = {new Watermark(currentMaxTimestamp - maxOutOfOrderness)
} override def extractTimestamp(element: (Long, String, Long), previousElementTimestamp: Long): Long = {val timestamp = element._1currentMaxTimestamp = Math.max(timestamp,currentMaxTimestamp)
timestamp
}
}).keyBy(_._2)
.timeWindow(Time.minutes(1))
.apply(new WindowFunction[(Long,String,Long),(String,String,Long),String,TimeWindow] { override def apply(key: String, window: TimeWindow, input: Iterable[(Long, String, Long)], out: Collector[(String, String, Long)]): Unit = {val list = input.toListval sum = list.map(_._3).sumval format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
out.collect((format.format(window.getStart) + " - " + format.format(window.getEnd),key,sum))
}
})
.print().setParallelism(1)
env.execute("LogAnalysis")
}
}Sink到Elasticsearch
安装ES
我们这里使用的版本为6.2.4
wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-6.2.4.tar.gz解压缩后进入config目录，编辑elasticsearch.yml，修改
network.host: 0.0.0.0增加一个非root用户
useradd es
将ES目录下的所有文件更改为es所有者
chown -R es:es elasticsearch-6.2.4修改/etc/security/limits.conf，将最下方的内容改为
es soft nofile 65536 es hard nofile 65536
修改/etc/sysctl.conf，增加
vm.max_map_count=655360执行命令
sysctl -p进入es的bin文件夹，并切换用户es
su es
在es用户下执行
./elasticsearch -d
此时可以在Web界面中看到ES的信息(外网ip:9200)
给Flink添加ES Sink,先添加依赖
<dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-elasticsearch7_2.11</artifactId> <version>${flink.version}</version></dependency>@Slf4jpublic class LogAnalysis {public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); String topic = "pktest"; Properties properties = new Properties(); properties.setProperty("bootstrap.servers","外网ip:9092"); properties.setProperty("group.id","test"); List<HttpHost> httpHosts = new ArrayList<>(); httpHosts.add(new HttpHost("外网ip",9200,"http")); ElasticsearchSink.Builder<Tuple3<String,String,Long>> builder = new ElasticsearchSink.Builder<>(httpHosts, new ElasticsearchSinkFunction<Tuple3<String, String, Long>>() {@Override public void process(Tuple3<String, String, Long> value, RuntimeContext runtimeContext, RequestIndexer indexer) {
Map<String,Object> json = new HashMap<>(); json.put("time",value.getField(0)); json.put("domain",value.getField(1)); json.put("traffic",value.getField(2)); String id = value.getField(0) + "-" + value.getField(1); indexer.add(Requests.indexRequest()
.index("cdn")
.type("traffic")
.id(id)
.source(json)); }
}); //设置批量写数据的缓冲区大小 builder.setBulkFlushMaxActions(1); DataStreamSource<String> data = env.addSource(new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties)); data.map(new MapFunction<String, Tuple4<String, Long, String, String>>() {@Override public Tuple4<String, Long, String, String> map(String value) throws Exception {
String[] splits = value.split("\t"); String level = splits[2]; String timeStr = splits[3]; Long time = 0L; try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime(); } catch (ParseException e) {log.error("time转换错误:" + timeStr + "," + e.getMessage()); }
String domain = splits[5]; String traffic = splits[6]; return new Tuple4<>(level,time,domain,traffic); }
}).filter(x -> (Long) x.getField(1) != 0) //此处我们只需要Level为E的数据 .filter(x -> x.getField(0).equals("E")) //抛弃level .map(new MapFunction<Tuple4<String,Long,String,String>, Tuple3<Long,String,Long>>() { @Override public Tuple3<Long, String, Long> map(Tuple4<String, Long, String, String> value) throws Exception { return new Tuple3<>(value.getField(1),value.getField(2),Long.parseLong(value.getField(3))); }
})
.setParallelism(1).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks<Tuple3<Long, String, Long>>() {private Long maxOutOfOrderness = 10000L; private Long currentMaxTimestamp = 0L; @Nullable @Override public Watermark getCurrentWatermark() {return new Watermark(currentMaxTimestamp - maxOutOfOrderness); }@Override public long extractTimestamp(Tuple3<Long, String, Long> element, long previousElementTimestamp) {
Long timestamp = element.getField(0); currentMaxTimestamp = Math.max(timestamp,currentMaxTimestamp); return timestamp; }
}).keyBy(x -> (String) x.getField(1))
.timeWindow(Time.minutes(1)) //输出格式：一分钟的时间间隔，域名，该域名在一分钟内的总流量 .apply(new WindowFunction<Tuple3<Long,String,Long>, Tuple3<String,String,Long>, String, TimeWindow>() { @Override public void apply(String s, TimeWindow window, Iterable<Tuple3<Long, String, Long>> input, Collector<Tuple3<String, String, Long>> out) throws Exception {
List<Tuple3<Long,String,Long>> list = (List) input; Long sum = list.stream().map(x -> (Long) x.getField(2)).reduce((x, y) -> x + y).get(); SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); out.collect(new Tuple3<>(format.format(window.getStart()) + " - " + format.format(window.getEnd()),s,sum)); }
})
.addSink(builder.build()); env.execute("LogAnalysis"); }
}执行后可以在ES中查询到数据
http://外网ip:9200/cdn/traffic/_search
Scala代码
import java.text.SimpleDateFormatimport java.utilimport java.util.Propertiesimport org.apache.flink.api.common.functions.RuntimeContextimport org.apache.flink.api.common.serialization.SimpleStringSchemaimport org.apache.flink.streaming.api.scala.StreamExecutionEnvironmentimport org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerimport org.slf4j.LoggerFactoryimport org.apache.flink.api.scala._import org.apache.flink.streaming.api.TimeCharacteristicimport org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarksimport org.apache.flink.streaming.api.scala.function.WindowFunctionimport org.apache.flink.streaming.api.watermark.Watermarkimport org.apache.flink.streaming.api.windowing.time.Timeimport org.apache.flink.streaming.api.windowing.windows.TimeWindowimport org.apache.flink.streaming.connectors.elasticsearch.{ElasticsearchSinkFunction, RequestIndexer}import org.apache.flink.streaming.connectors.elasticsearch7.ElasticsearchSinkimport org.apache.flink.util.Collectorimport org.apache.http.HttpHostimport org.elasticsearch.client.Requestsobject LogAnalysis { val log = LoggerFactory.getLogger(LogAnalysis.getClass) def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)val topic = "pktest" val properties = new Properties
properties.setProperty("bootstrap.servers", "外网ip:9092")
properties.setProperty("group.id","test")val httpHosts = new util.ArrayList[HttpHost]
httpHosts.add(new HttpHost("外网ip",9200,"http"))val builder = new ElasticsearchSink.Builder[(String,String,Long)](httpHosts,new ElasticsearchSinkFunction[(String, String, Long)] { override def process(t: (String, String, Long), runtimeContext: RuntimeContext, indexer: RequestIndexer): Unit = {val json = new util.HashMap[String,Any]
json.put("time",t._1)
json.put("domain",t._2)
json.put("traffic",t._3)val id = t._1 + "-" + t._2
indexer.add(Requests.indexRequest()
.index("cdn")
.`type`("traffic")
.id(id)
.source(json))
}
})
builder.setBulkFlushMaxActions(1)val data = env.addSource(new FlinkKafkaConsumer[String](topic, new SimpleStringSchema, properties))
data.map(x => { val splits = x.split("\t") val level = splits(2) val timeStr = splits(3) var time: Long = 0l try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime
}catch {case e: Exception => { log.error(s"time转换错误: $timeStr",e.getMessage)
}
} val domain = splits(5) val traffic = splits(6)
(level,time,domain,traffic)
}).filter(_._2 != 0)
.filter(_._1 == "E")
.map(x => (x._2,x._3,x._4.toLong))
.setParallelism(1).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks[(Long, String, Long)] { var maxOutOfOrderness: Long = 10000l var currentMaxTimestamp: Long = _ override def getCurrentWatermark: Watermark = {new Watermark(currentMaxTimestamp - maxOutOfOrderness)
} override def extractTimestamp(element: (Long, String, Long), previousElementTimestamp: Long): Long = {val timestamp = element._1currentMaxTimestamp = Math.max(timestamp,currentMaxTimestamp)
timestamp
}
}).keyBy(_._2)
.timeWindow(Time.minutes(1))
.apply(new WindowFunction[(Long,String,Long),(String,String,Long),String,TimeWindow] { override def apply(key: String, window: TimeWindow, input: Iterable[(Long, String, Long)], out: Collector[(String, String, Long)]): Unit = {val list = input.toListval sum = list.map(_._3).sumval format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
out.collect((format.format(window.getStart) + " - " + format.format(window.getEnd),key,sum))
}
})
.addSink(builder.build)
env.execute("LogAnalysis")
}
}Kibana图形展示
安装kibana
wget https://artifacts.elastic.co/downloads/kibana/kibana-6.2.4-linux-x86_64.tar.gzkibana要跟ES保持版本相同，解压缩后进入config目录，编辑kibana.yml
server.host: "host2"
elasticsearch.url: "http://host2:9200"这里面的内容会根据版本不同会有一些不同，保存后，进入bin目录
切换es用户，执行
./kibana &
访问Web页面，外网ip:5601
这里我做了一个表，一个柱状图
第二个需求，统计一分钟内每个用户产生的流量
在MySQL数据库中新增一张表user_domain_config,字段如下
表中内容如下
数据清洗
/** * 自定义MySQL数据源 */public class MySQLSource extends RichParallelSourceFunction<Tuple2<String,String>> {private Connection connection; private PreparedStatement pstmt; private Connection getConnection() {
Connection conn = null; try {
Class.forName("com.mysql.cj.jdbc.Driver"); String url = "jdbc:mysql://外网ip:3306/flink"; conn = DriverManager.getConnection(url,"root","******"); }catch (Exception e) {
e.printStackTrace(); }return conn; }@Override public void open(Configuration parameters) throws Exception {super.open(parameters); connection = getConnection(); String sql = "select user_id,domain from user_domain_config"; pstmt = connection.prepareStatement(sql); }@Override @SuppressWarnings("unchecked")public void run(SourceContext<Tuple2<String,String>> ctx) throws Exception {
ResultSet rs = pstmt.executeQuery(); while (rs.next()) {
Tuple2 tuple2 = new Tuple2(rs.getString("domain"),rs.getString("user_id")); ctx.collect(tuple2); }pstmt.close(); }@Override public void cancel() {
}@Override public void close() throws Exception {super.close(); if (pstmt != null) {pstmt.close(); }if (connection != null) {connection.close(); }
}
}@Slf4jpublic class LogAnalysisWithMySQL {public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); String topic = "pktest"; Properties properties = new Properties(); properties.setProperty("bootstrap.servers","外网ip:9092"); properties.setProperty("group.id","test"); DataStreamSource<String> data = env.addSource(new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties)); SingleOutputStreamOperator<Tuple3<Long, String, Long>> logData = data.map(new MapFunction<String, Tuple4<String, Long, String, String>>() {@Override public Tuple4<String, Long, String, String> map(String value) throws Exception {
String[] splits = value.split("\t"); String level = splits[2]; String timeStr = splits[3]; Long time = 0L; try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime(); } catch (ParseException e) {log.error("time转换错误:" + timeStr + "," + e.getMessage()); }
String domain = splits[5]; String traffic = splits[6]; return new Tuple4<>(level, time, domain, traffic); }
}).filter(x -> (Long) x.getField(1) != 0)//此处我们只需要Level为E的数据 .filter(x -> x.getField(0).equals("E"))//抛弃level .map(new MapFunction<Tuple4<String, Long, String, String>, Tuple3<Long, String, Long>>() {@Override public Tuple3<Long, String, Long> map(Tuple4<String, Long, String, String> value) throws Exception {return new Tuple3<>(value.getField(1), value.getField(2), Long.parseLong(value.getField(3))); }
}); DataStreamSource<Tuple2<String, String>> mysqlData = env.addSource(new MySQLSource()); //双流汇聚 logData.connect(mysqlData).flatMap(new CoFlatMapFunction<Tuple3<Long,String,Long>, Tuple2<String,String>, Tuple4<Long,String,Long,String>>() {private Map<String,String> userDomainMap = new HashMap<>(); @Override public void flatMap1(Tuple3<Long, String, Long> value, Collector<Tuple4<Long,String,Long,String>> out) throws Exception {
String domain = value.getField(1); String userId = userDomainMap.getOrDefault(domain,""); out.collect(new Tuple4<>(value.getField(0),value.getField(1),value.getField(2),userId)); }@Override public void flatMap2(Tuple2<String, String> value, Collector<Tuple4<Long,String,Long,String>> out) throws Exception {userDomainMap.put(value.getField(0),value.getField(1)); }
}).print().setParallelism(1); env.execute("LogAnalysisWithMySQL"); }
}运行结果
(1612239325000,vmi.go2yd.com,7115,80000001)
(1612239633000,v4.go2yd.com,8412,80000001)
(1612239635000,v3.go2yd.com,3527,80000000)
(1612239639000,v1.go2yd.com,7385,80000000)
(1612239643000,vmi.go2yd.com,8650,80000001)
(1612239645000,vmi.go2yd.com,2642,80000001)
(1612239647000,vmi.go2yd.com,1525,80000001)
(1612239649000,v2.go2vd.com,8832,80000000)Scala代码
import java.sql.{Connection, DriverManager, PreparedStatement}import org.apache.flink.configuration.Configurationimport org.apache.flink.streaming.api.functions.source.{RichParallelSourceFunction, SourceFunction}class MySQLSource extends RichParallelSourceFunction[(String,String)]{ var connection: Connection = null var pstmt: PreparedStatement = null def getConnection:Connection = {var conn: Connection = null Class.forName("com.mysql.cj.jdbc.Driver")val url = "jdbc:mysql://外网ip:3306/flink" conn = DriverManager.getConnection(url, "root", "******")
conn
} override def open(parameters: Configuration): Unit = {connection = getConnectionval sql = "select user_id,domain from user_domain_config" pstmt = connection.prepareStatement(sql)
} override def cancel() = {} override def run(ctx: SourceFunction.SourceContext[(String, String)]) = {val rs = pstmt.executeQuery()while (rs.next) { val tuple2 = (rs.getString("domain"),rs.getString("user_id"))
ctx.collect(tuple2)
}pstmt.close()
} override def close(): Unit = {if (pstmt != null) { pstmt.close()
}if (connection != null) { connection.close()
}
}
}import java.text.SimpleDateFormatimport java.util.Propertiesimport com.guanjian.flink.scala.until.MySQLSourceimport org.apache.flink.api.common.serialization.SimpleStringSchemaimport org.apache.flink.api.scala._import org.apache.flink.streaming.api.functions.co.CoFlatMapFunctionimport org.apache.flink.streaming.api.scala.StreamExecutionEnvironmentimport org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerimport org.apache.flink.util.Collectorimport org.slf4j.LoggerFactoryimport scala.collection.mutableobject LogAnalysisWithMySQL { val log = LoggerFactory.getLogger(LogAnalysisWithMySQL.getClass) def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment val topic = "pktest" val properties = new Properties
properties.setProperty("bootstrap.servers", "外网ip:9092")
properties.setProperty("group.id","test")val data = env.addSource(new FlinkKafkaConsumer[String](topic, new SimpleStringSchema, properties))val logData = data.map(x => { val splits = x.split("\t") val level = splits(2) val timeStr = splits(3) var time: Long = 0l try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime
}catch {case e: Exception => { log.error(s"time转换错误: $timeStr",e.getMessage)
}
} val domain = splits(5) val traffic = splits(6)
(level,time,domain,traffic)
}).filter(_._2 != 0)
.filter(_._1 == "E")
.map(x => (x._2,x._3,x._4.toLong))val mysqlData = env.addSource(new MySQLSource)
logData.connect(mysqlData).flatMap(new CoFlatMapFunction[(Long,String,Long),(String,String),(Long,String,Long,String)] { var userDomainMap = mutable.HashMap[String,String]() override def flatMap1(value: (Long, String, Long), out: Collector[(Long, String, Long, String)]) = {val domain = value._2val userId = userDomainMap.getOrElse(domain,"")
out.collect((value._1,value._2,value._3,userId))
} override def flatMap2(value: (String, String), out: Collector[(Long, String, Long, String)]) = {userDomainMap += value._1 -> value._2
}
}).print().setParallelism(1)
env.execute("LogAnalysisWithMySQL")
}
}数据分析
@Slf4jpublic class LogAnalysisWithMySQL {public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); String topic = "pktest"; Properties properties = new Properties(); properties.setProperty("bootstrap.servers","外网ip:9092"); properties.setProperty("group.id","test"); DataStreamSource<String> data = env.addSource(new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties)); SingleOutputStreamOperator<Tuple3<Long, String, Long>> logData = data.map(new MapFunction<String, Tuple4<String, Long, String, String>>() {@Override public Tuple4<String, Long, String, String> map(String value) throws Exception {
String[] splits = value.split("\t"); String level = splits[2]; String timeStr = splits[3]; Long time = 0L; try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime(); } catch (ParseException e) {log.error("time转换错误:" + timeStr + "," + e.getMessage()); }
String domain = splits[5]; String traffic = splits[6]; return new Tuple4<>(level, time, domain, traffic); }
}).filter(x -> (Long) x.getField(1) != 0)//此处我们只需要Level为E的数据 .filter(x -> x.getField(0).equals("E"))//抛弃level .map(new MapFunction<Tuple4<String, Long, String, String>, Tuple3<Long, String, Long>>() {@Override public Tuple3<Long, String, Long> map(Tuple4<String, Long, String, String> value) throws Exception {return new Tuple3<>(value.getField(1), value.getField(2), Long.parseLong(value.getField(3))); }
}); DataStreamSource<Tuple2<String, String>> mysqlData = env.addSource(new MySQLSource()); //双流汇聚 logData.connect(mysqlData).flatMap(new CoFlatMapFunction<Tuple3<Long,String,Long>, Tuple2<String,String>, Tuple4<Long,String,Long,String>>() {private Map<String,String> userDomainMap = new HashMap<>(); @Override public void flatMap1(Tuple3<Long, String, Long> value, Collector<Tuple4<Long,String,Long,String>> out) throws Exception {
String domain = value.getField(1); String userId = userDomainMap.getOrDefault(domain,""); out.collect(new Tuple4<>(value.getField(0),value.getField(1),value.getField(2),userId)); }@Override public void flatMap2(Tuple2<String, String> value, Collector<Tuple4<Long,String,Long,String>> out) throws Exception {userDomainMap.put(value.getField(0),value.getField(1)); }
}).setParallelism(1).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks<Tuple4<Long, String, Long,String>>() {private Long maxOutOfOrderness = 10000L; private Long currentMaxTimestamp = 0L; @Nullable @Override public Watermark getCurrentWatermark() {return new Watermark(currentMaxTimestamp - maxOutOfOrderness); }@Override public long extractTimestamp(Tuple4<Long, String, Long,String> element, long previousElementTimestamp) {
Long timestamp = element.getField(0); currentMaxTimestamp = Math.max(timestamp,currentMaxTimestamp); return timestamp; }
}).keyBy(x -> (String) x.getField(3))
.timeWindow(Time.minutes(1)) //输出格式：一分钟的时间间隔，用户，该用户在一分钟内的总流量 .apply(new WindowFunction<Tuple4<Long,String,Long,String>, Tuple3<String,String,Long>, String, TimeWindow>() { @Override public void apply(String s, TimeWindow window, Iterable<Tuple4<Long, String, Long, String>> input, Collector<Tuple3<String, String, Long>> out) throws Exception {
List<Tuple4<Long, String, Long,String>> list = (List) input; Long sum = list.stream().map(x -> (Long) x.getField(2)).reduce((x, y) -> x + y).get(); SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); out.collect(new Tuple3<>(format.format(window.getStart()) + " - " + format.format(window.getEnd()), s, sum)); }
}).print().setParallelism(1); env.execute("LogAnalysisWithMySQL"); }
}运行结果
(2021-02-02 13:58:00 - 2021-02-02 13:59:00,80000000,20933)
(2021-02-02 13:58:00 - 2021-02-02 13:59:00,80000001,6928)
(2021-02-02 13:59:00 - 2021-02-02 14:00:00,80000001,38202)
(2021-02-02 13:59:00 - 2021-02-02 14:00:00,80000000,39394)
(2021-02-02 14:00:00 - 2021-02-02 14:01:00,80000001,23070)
(2021-02-02 14:00:00 - 2021-02-02 14:01:00,80000000,41701)Scala代码
import java.text.SimpleDateFormatimport java.util.Propertiesimport com.guanjian.flink.scala.until.MySQLSourceimport org.apache.flink.api.common.serialization.SimpleStringSchemaimport org.apache.flink.api.scala._import org.apache.flink.streaming.api.TimeCharacteristicimport org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarksimport org.apache.flink.streaming.api.functions.co.CoFlatMapFunctionimport org.apache.flink.streaming.api.scala.StreamExecutionEnvironmentimport org.apache.flink.streaming.api.scala.function.WindowFunctionimport org.apache.flink.streaming.api.watermark.Watermarkimport org.apache.flink.streaming.api.windowing.time.Timeimport org.apache.flink.streaming.api.windowing.windows.TimeWindowimport org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerimport org.apache.flink.util.Collectorimport org.slf4j.LoggerFactoryimport scala.collection.mutableobject LogAnalysisWithMySQL { val log = LoggerFactory.getLogger(LogAnalysisWithMySQL.getClass) def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)val topic = "pktest" val properties = new Properties
properties.setProperty("bootstrap.servers", "外网ip:9092")
properties.setProperty("group.id","test")val data = env.addSource(new FlinkKafkaConsumer[String](topic, new SimpleStringSchema, properties))val logData = data.map(x => { val splits = x.split("\t") val level = splits(2) val timeStr = splits(3) var time: Long = 0l try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime
}catch {case e: Exception => { log.error(s"time转换错误: $timeStr",e.getMessage)
}
} val domain = splits(5) val traffic = splits(6)
(level,time,domain,traffic)
}).filter(_._2 != 0)
.filter(_._1 == "E")
.map(x => (x._2,x._3,x._4.toLong))val mysqlData = env.addSource(new MySQLSource)
logData.connect(mysqlData).flatMap(new CoFlatMapFunction[(Long,String,Long),(String,String),(Long,String,Long,String)] { var userDomainMap = mutable.HashMap[String,String]() override def flatMap1(value: (Long, String, Long), out: Collector[(Long, String, Long, String)]) = {val domain = value._2val userId = userDomainMap.getOrElse(domain,"")
out.collect((value._1,value._2,value._3,userId))
} override def flatMap2(value: (String, String), out: Collector[(Long, String, Long, String)]) = {userDomainMap += value._1 -> value._2
}
}).setParallelism(1).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks[(Long, String, Long, String)] { var maxOutOfOrderness: Long = 10000l var currentMaxTimestamp: Long = _ override def getCurrentWatermark: Watermark = {new Watermark(currentMaxTimestamp - maxOutOfOrderness)
} override def extractTimestamp(element: (Long, String, Long, String), previousElementTimestamp: Long): Long = {val timestamp = element._1currentMaxTimestamp = Math.max(timestamp,currentMaxTimestamp)
timestamp
}
}).keyBy(_._4)
.timeWindow(Time.minutes(1))
.apply(new WindowFunction[(Long,String,Long,String),(String,String,Long),String,TimeWindow] {override def apply(key: String, window: TimeWindow, input: Iterable[(Long, String, Long, String)], out: Collector[(String, String, Long)]): Unit = { val list = input.toList val sum = list.map(_._3).sum val format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
out.collect((format.format(window.getStart) + " - " + format.format(window.getEnd),key,sum))
}
}).print().setParallelism(1)
env.execute("LogAnalysisWithMySQL")
}
}Sink到ES
@Slf4jpublic class LogAnalysisWithMySQL {public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); String topic = "pktest"; Properties properties = new Properties(); properties.setProperty("bootstrap.servers","外网ip:9092"); properties.setProperty("group.id","test"); List<HttpHost> httpHosts = new ArrayList<>(); httpHosts.add(new HttpHost("外网ip",9200,"http")); ElasticsearchSink.Builder<Tuple3<String,String,Long>> builder = new ElasticsearchSink.Builder<>(httpHosts, new ElasticsearchSinkFunction<Tuple3<String, String, Long>>() {@Override public void process(Tuple3<String, String, Long> value, RuntimeContext runtimeContext, RequestIndexer indexer) {
Map<String,Object> json = new HashMap<>(); json.put("time",value.getField(0)); json.put("userId",value.getField(1)); json.put("traffic",value.getField(2)); String id = value.getField(0) + "-" + value.getField(1); indexer.add(Requests.indexRequest()
.index("user")
.type("traffic")
.id(id)
.source(json)); }
}); //设置批量写数据的缓冲区大小 builder.setBulkFlushMaxActions(1); DataStreamSource<String> data = env.addSource(new FlinkKafkaConsumer<>(topic, new SimpleStringSchema(), properties)); SingleOutputStreamOperator<Tuple3<Long, String, Long>> logData = data.map(new MapFunction<String, Tuple4<String, Long, String, String>>() {@Override public Tuple4<String, Long, String, String> map(String value) throws Exception {
String[] splits = value.split("\t"); String level = splits[2]; String timeStr = splits[3]; Long time = 0L; try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime(); } catch (ParseException e) {log.error("time转换错误:" + timeStr + "," + e.getMessage()); }
String domain = splits[5]; String traffic = splits[6]; return new Tuple4<>(level, time, domain, traffic); }
}).filter(x -> (Long) x.getField(1) != 0)//此处我们只需要Level为E的数据 .filter(x -> x.getField(0).equals("E"))//抛弃level .map(new MapFunction<Tuple4<String, Long, String, String>, Tuple3<Long, String, Long>>() {@Override public Tuple3<Long, String, Long> map(Tuple4<String, Long, String, String> value) throws Exception {return new Tuple3<>(value.getField(1), value.getField(2), Long.parseLong(value.getField(3))); }
}); DataStreamSource<Tuple2<String, String>> mysqlData = env.addSource(new MySQLSource()); //双流汇聚 logData.connect(mysqlData).flatMap(new CoFlatMapFunction<Tuple3<Long,String,Long>, Tuple2<String,String>, Tuple4<Long,String,Long,String>>() {private Map<String,String> userDomainMap = new HashMap<>(); @Override public void flatMap1(Tuple3<Long, String, Long> value, Collector<Tuple4<Long,String,Long,String>> out) throws Exception {
String domain = value.getField(1); String userId = userDomainMap.getOrDefault(domain,""); out.collect(new Tuple4<>(value.getField(0),value.getField(1),value.getField(2),userId)); }@Override public void flatMap2(Tuple2<String, String> value, Collector<Tuple4<Long,String,Long,String>> out) throws Exception {userDomainMap.put(value.getField(0),value.getField(1)); }
}).setParallelism(1).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks<Tuple4<Long, String, Long,String>>() {private Long maxOutOfOrderness = 10000L; private Long currentMaxTimestamp = 0L; @Nullable @Override public Watermark getCurrentWatermark() {return new Watermark(currentMaxTimestamp - maxOutOfOrderness); }@Override public long extractTimestamp(Tuple4<Long, String, Long,String> element, long previousElementTimestamp) {
Long timestamp = element.getField(0); currentMaxTimestamp = Math.max(timestamp,currentMaxTimestamp); return timestamp; }
}).keyBy(x -> (String) x.getField(3))
.timeWindow(Time.minutes(1)) //输出格式：一分钟的时间间隔，用户，该用户在一分钟内的总流量 .apply(new WindowFunction<Tuple4<Long,String,Long,String>, Tuple3<String,String,Long>, String, TimeWindow>() { @Override public void apply(String s, TimeWindow window, Iterable<Tuple4<Long, String, Long, String>> input, Collector<Tuple3<String, String, Long>> out) throws Exception {
List<Tuple4<Long, String, Long,String>> list = (List) input; Long sum = list.stream().map(x -> (Long) x.getField(2)).reduce((x, y) -> x + y).get(); SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); out.collect(new Tuple3<>(format.format(window.getStart()) + " - " + format.format(window.getEnd()), s, sum)); }
}).addSink(builder.build()); env.execute("LogAnalysisWithMySQL"); }
}运行结果
访问http://外网ip:9200/user/traffic/_search
Scala代码
port java.text.SimpleDateFormatimport java.utilimport java.util.Propertiesimport com.guanjian.flink.scala.until.MySQLSourceimport org.apache.flink.api.common.functions.RuntimeContextimport org.apache.flink.api.common.serialization.SimpleStringSchemaimport org.apache.flink.api.scala._import org.apache.flink.streaming.api.TimeCharacteristicimport org.apache.flink.streaming.api.functions.AssignerWithPeriodicWatermarksimport org.apache.flink.streaming.api.functions.co.CoFlatMapFunctionimport org.apache.flink.streaming.api.scala.StreamExecutionEnvironmentimport org.apache.flink.streaming.api.scala.function.WindowFunctionimport org.apache.flink.streaming.api.watermark.Watermarkimport org.apache.flink.streaming.api.windowing.time.Timeimport org.apache.flink.streaming.api.windowing.windows.TimeWindowimport org.apache.flink.streaming.connectors.elasticsearch.{ElasticsearchSinkFunction, RequestIndexer}import org.apache.flink.streaming.connectors.elasticsearch7.ElasticsearchSinkimport org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerimport org.apache.flink.util.Collectorimport org.apache.http.HttpHostimport org.elasticsearch.client.Requestsimport org.slf4j.LoggerFactoryimport scala.collection.mutableobject LogAnalysisWithMySQL { val log = LoggerFactory.getLogger(LogAnalysisWithMySQL.getClass) def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)val topic = "pktest" val properties = new Properties
properties.setProperty("bootstrap.servers", "外网ip:9092")
properties.setProperty("group.id","test")val httpHosts = new util.ArrayList[HttpHost]
httpHosts.add(new HttpHost("外网ip",9200,"http"))val builder = new ElasticsearchSink.Builder[(String,String,Long)](httpHosts,new ElasticsearchSinkFunction[(String, String, Long)] { override def process(t: (String, String, Long), runtimeContext: RuntimeContext, indexer: RequestIndexer): Unit = {val json = new util.HashMap[String,Any]
json.put("time",t._1)
json.put("userId",t._2)
json.put("traffic",t._3)val id = t._1 + "-" + t._2
indexer.add(Requests.indexRequest()
.index("user")
.`type`("traffic")
.id(id)
.source(json))
}
})
builder.setBulkFlushMaxActions(1)val data = env.addSource(new FlinkKafkaConsumer[String](topic, new SimpleStringSchema, properties))val logData = data.map(x => { val splits = x.split("\t") val level = splits(2) val timeStr = splits(3) var time: Long = 0l try {
time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").parse(timeStr).getTime
}catch {case e: Exception => { log.error(s"time转换错误: $timeStr",e.getMessage)
}
} val domain = splits(5) val traffic = splits(6)
(level,time,domain,traffic)
}).filter(_._2 != 0)
.filter(_._1 == "E")
.map(x => (x._2,x._3,x._4.toLong))val mysqlData = env.addSource(new MySQLSource)
logData.connect(mysqlData).flatMap(new CoFlatMapFunction[(Long,String,Long),(String,String),(Long,String,Long,String)] { var userDomainMap = mutable.HashMap[String,String]() override def flatMap1(value: (Long, String, Long), out: Collector[(Long, String, Long, String)]) = {val domain = value._2val userId = userDomainMap.getOrElse(domain,"")
out.collect((value._1,value._2,value._3,userId))
} override def flatMap2(value: (String, String), out: Collector[(Long, String, Long, String)]) = {userDomainMap += value._1 -> value._2
}
}).setParallelism(1).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks[(Long, String, Long, String)] { var maxOutOfOrderness: Long = 10000l var currentMaxTimestamp: Long = _ override def getCurrentWatermark: Watermark = {new Watermark(currentMaxTimestamp - maxOutOfOrderness)
} override def extractTimestamp(element: (Long, String, Long, String), previousElementTimestamp: Long): Long = {val timestamp = element._1currentMaxTimestamp = Math.max(timestamp,currentMaxTimestamp)
timestamp
}
}).keyBy(_._4)
.timeWindow(Time.minutes(1))
.apply(new WindowFunction[(Long,String,Long,String),(String,String,Long),String,TimeWindow] {override def apply(key: String, window: TimeWindow, input: Iterable[(Long, String, Long, String)], out: Collector[(String, String, Long)]): Unit = { val list = input.toList val sum = list.map(_._3).sum val format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
out.collect((format.format(window.getStart) + " - " + format.format(window.getEnd),key,sum))
}
}).addSink(builder.build)
env.execute("LogAnalysisWithMySQL")
}
}Kibana图表展示
这里我们就画一个环状图吧
到此，关于“Flink简单项目整体流程是怎样的”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注亿速云网站，小编会继续努力为大家带来更多实用的文章！
亿速云「云服务器」，即开即用、新一代英特尔至强铂金CPU、三副本存储NVMe SSD云盘，价格低至29元/月。点击查看>>
免责声明：本站发布的内容（图片、视频和文字）以原创、转载和分享为主，文章观点不代表本网站立场，如果涉及侵权请联系站长邮箱：is@yisu.com进行举报，并提供相关证据，一经查实，将立刻删除涉嫌侵权内容。
原文链接：https://my.oschina.net/u/3768341/blog/4939367
