1.框架
2.log4j完成模擬日誌輸出
設置模擬日誌格式,log4j.properties:
log4j.rootLogger = INFO,stdout
log4j.appender.stdout = org.apache.log4j.ConsoleAppender
log4j.appender.stdout.target = System.out
log4j.appender.stdout.layout = org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern = %d{yyyy-MM-dd HH:mm:ss,SSS} [%t] [%c] [%p] - %m%n
模擬日誌輸出,LoggerGenerator.java:
import org.apache.log4j.Logger;
/**
* 模擬日誌產生
*/
public class LoggerGenerator {
private static Logger logger = Logger.getLogger(LoggerGenerator.class.getName());
public static void main(String[] args) throws Exception{
int index = 0;
while(true){
Thread.sleep(1000);
logger.info("value:" + index++);
}
}
}
運行結果:
2020-03-07 18:21:37,637 [main] [LoggerGenerator] [INFO] - current value is:0
2020-03-07 18:21:38,639 [main] [LoggerGenerator] [INFO] - current value is:1
2020-03-07 18:21:39,639 [main] [LoggerGenerator] [INFO] - current value is:2
2020-03-07 18:21:40,640 [main] [LoggerGenerator] [INFO] - current value is:3
2020-03-07 18:21:41,640 [main] [LoggerGenerator] [INFO] - current value is:4
2020-03-07 18:21:42,641 [main] [LoggerGenerator] [INFO] - current value is:5
2020-03-07 18:21:43,641 [main] [LoggerGenerator] [INFO] - current value is:6
2020-03-07 18:21:44,642 [main] [LoggerGenerator] [INFO] - current value is:7
2020-03-07 18:21:45,642 [main] [LoggerGenerator] [INFO] - current value is:8
2020-03-07 18:21:46,642 [main] [LoggerGenerator] [INFO] - current value is:9
2020-03-07 18:21:47,643 [main] [LoggerGenerator] [INFO] - current value is:10
3.Flume收集log4j日誌
$FLUME_HOME/conf/streaming.conf:
agent1.sources=avro-source
agent1.channels=logger-channel
agent1.sinks=log-sink
#define source
agent1.sources.avro-source.type=avro
agent1.sources.avro-source.bind=0.0.0.0
agent1.sources.avro-source.port=41414
#define channel
agent1.channels.logger-channel.type=memory
#define sink
agent1.sinks.log-sink.type=logger
agent1.sources.avro-source.channels=logger-channel
agent1.sinks.log-sink.channel=logger-channel
啓動Flume(注意輸出到控制檯上爲INFO,console,不是點【.】):
flume-ng agent \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/streaming.conf \
--name agent1 \
-Dflume.root.logger=INFO,console
pom.xml加上一個jar包:
<dependency>
<groupId>org.apache.flume.flume-ng-clients</groupId>
<artifactId>flume-ng-log4jappender</artifactId>
<version>1.6.0</version>
</dependency>
修改log4j.properties,使其與Flume鏈接:
log4j.rootLogger = INFO,stdout,flume
log4j.appender.stdout = org.apache.log4j.ConsoleAppender
log4j.appender.stdout.target = System.out
log4j.appender.stdout.layout = org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern = %d{yyyy-MM-dd HH:mm:ss,SSS} [%t] [%c] [%p] - %m%n
log4j.appender.flume = org.apache.flume.clients.log4jappender.Log4jAppender
log4j.appender.flume.Hostname = hadoop000
log4j.appender.flume.Port = 41414
log4j.appender.flume.UnsafeMode = true
啓動log4j:
Flume採集成功
4.KafkaSink鏈接Kafka與Flume
使用Kafka第一件事是把Zookeeper啓動起來~
./zkServer.sh start
啓動Kafka
./kafka-server-start.sh -daemon /home/hadoop/app/kafka_2.11-0.9.0.0/config/server.properties
看下Kafka列表(用./kafka-topics.sh會報錯,用“./”加文件名.sh執行時,必須給.sh文件加x執行權限):
kafka-topics.sh --list --zookeeper hadoop000:2181
創建一個topic:
kafka-topics.sh --create \
--zookeeper hadoop000:2181 \
--replication-factor 1 \
--partitions 1 \
--topic tp_streamingtopic
對接Flume與Kafka,設置Flume的conf,取名爲streaming2.conf:
Kafka sink需要的參數有(每個版本不一樣,具體可以查閱官網):
- sink類型填KafkaSink
- 需要鏈接的Kafka topic
- Kafka中間件broker的地址與端口號
- 是否使用握手機制
- 每次發送的數據大小
agent1.sources=avro-source
agent1.channels=logger-channel
agent1.sinks=kafka-sink
#define source
agent1.sources.avro-source.type=avro
agent1.sources.avro-source.bind=0.0.0.0
agent1.sources.avro-source.port=41414
#define channel
agent1.channels.logger-channel.type=memory
#define sink
agent1.sinks.kafka-sink.type=org.apache.flume.sink.kafka.KafkaSink
agent1.sinks.kafka-sink.topic = tp_streamingtopic
agent1.sinks.kafka-sink.brokerList = hadoop000:9092
agent1.sinks.kafka-sink.requiredAcks = 1
agent1.sinks.kafka-sink.batchSize = 20
agent1.sources.avro-source.channels=logger-channel
agent1.sinks.kafka-sink.channel=logger-channel
啓動Flume:
flume-ng agent \
--conf $FLUME_HOME/conf \
--conf-file $FLUME_HOME/conf/streaming2.conf \
--name agent1 \
-Dflume.root.logger=INFO,console
Kafka需要啓動一個消費者消費Flume中Kafka sink來的數據:
./kafka-console-consumer.sh --zookeeper hadoop000:2181 --topic tp_streamingtopic
啓動log4j:
成功傳輸~
5.Spark Streaming消費Kafka數據
package com.taipark.spark
import kafka.serializer.StringDecoder
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
* Spark Streaming 對接 Kafka
*/
object KafkaStreamingApp {
def main(args: Array[String]): Unit = {
if(args.length != 2){
System.err.println("Userage:KafkaStreamingApp<brokers><topics>");
System.exit(1);
}
val Array(brokers,topics) = args
val sparkConf = new SparkConf().setAppName("KafkaReceiverWordCount")
.setMaster("local[2]")
val ssc = new StreamingContext(sparkConf,Seconds(5))
val kafkaParams = Map[String,String]("metadata.broker.list"-> brokers)
val topicSet = topics.split(",").toSet
val messages = KafkaUtils.createDirectStream[String,String,StringDecoder,StringDecoder](
ssc,kafkaParams,topicSet
)
//第二位是字符串的值
messages.map(_._2).flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).print()
ssc.start()
ssc.awaitTermination()
}
}
入參是Kafka的broker地址與topic名稱:
本地Run一下:
成功接收,完成~