本文主要是介绍Spark实战(五)spark streaming + flume(Python版)，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、flume安装
（一）概述
Flume是一个分布式、可靠、和高可用的海量日志采集、聚合和传输的系统。Flume可以采集文件，socket数据包等各种形式源数据，又可以将采集到的数据输出到HDFS、hbase、hive、kafka等众多外部存储系统中，一般的采集需求，通过对flume的简单配置即可实现，Flume针对特殊场景也具备良好的自定义扩展能力，因此flume可以适用于大部分的日常数据采集场景
（二）运行机制
1、 Flume分布式系统中最核心的角色是agent，flume采集系统就是由一个个agent所连接起来形成2、 每一个agent相当于一个数据传递员，内部有三个组件：
a) Source：采集源，用于跟数据源对接，以获取数据
b) Sink：下沉地，采集数据的传送目的，用于往下一级agent传递数据或者往最终存储系统传递数据
c) Channel：angent内部的数据传输通道，用于从source将数据传递到sink
（三）Flume采集系统结构图
1、简单结构
单个agent采集数据
2、复杂结构
多级agent之间串联
（四）Flume的安装部署
1、去apache官网上下载安装包，并解压tar -zxvf apache-flume-1.8.0-bin，并修改conf目录下flume-env.sh，在里面配置JAVA_HOME 2、根据数据采集的需求配置采集方案，描述在配置文件中(文件名可任意自定义)
3、指定采集方案配置文件，在相应的节点上启动flume agent
二、flume push方式
1、spark streaming程序
首先是flume通过push方式将采集到的数据传递到spark程序上，这种方式基本不用。spark代码如下：
import pyspark
from pyspark.sql import SparkSession
from pyspark.streaming import StreamingContext
from pyspark.streaming.flume import FlumeUtilsif __name__ == "__main__":spark = SparkSession\.builder\.appName("PythonWordCount") \.master("local[2]") \.getOrCreate()sc = spark.sparkContextssc = StreamingContext(sc, 5)# hostname = sys.argv[1]# port = int(sys.argv[2])flumeStream = FlumeUtils.createStream(ssc, "localhost", 8888, pyspark.StorageLevel.MEMORY_AND_DISK_SER_2)line = flumeStream.map(lambda x: x[1])words = line.flatMap(lambda x: x.split(" "))datas = words.map(lambda x: (x, 1))result = datas.reduceByKey(lambda agg, obj: agg + obj)result.pprint()ssc.start()ssc.awaitTermination()
注意：要指定并行度，如在本地运行设置setMaster(“local[2]”)，相当于启动两个线程，一个给receiver，一个给computer。否则会出现如下问题
2019-01-09 19:36:16 INFO ReceiverSupervisorImpl:54 - Called receiver 0 onStart
2019-01-09 19:36:16 INFO ReceiverSupervisorImpl:54 - Waiting for receiver to be stopped
2019-01-09 19:36:20 INFO JobScheduler:54 - Added jobs for time 1547033780000 ms
2019-01-09 19:36:25 INFO JobScheduler:54 - Added jobs for time 1547033785000 ms
2019-01-09 19:36:30 INFO JobScheduler:54 - Added jobs for time 1547033790000 ms
2019-01-09 19:36:35 INFO JobScheduler:54 - Added jobs for time 1547033795000 ms
2019-01-09 19:36:40 INFO JobScheduler:54 - Added jobs for time 1547033800000 ms
如果是在集群中运行，必须要求集群中可用core数大于1
2、flume conf文件
<font size=4>   在flume的conf目录下新建flume-push.conf内容如下</font></br>
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1# source
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /home/hadoop/log/flume
a1.sources.r1.fileHeader = true# Describe the sink
a1.sinks.k1.type = avro
#这是接收方
a1.sinks.k1.hostname = 192.168.62.131
a1.sinks.k1.port = 8888# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
需要先将spark程序运行，使用以下命令：
spark/bin/spark-submit --driver-class-path /home/hadoop/spark/jars/*:/home/hadoop/jar/flume/* /tmp/pycharm_project_563/day5/FlumePushWordCount.py
可能会出现以下问题
Spark Streaming's Flume libraries not found in class path. Try one of the following.1. Include the Flume library and its dependencies with in thespark-submit command as$ bin/spark-submit --packages org.apache.spark:spark-streaming-flume:2.4.0 ...2. Download the JAR of the artifact from Maven Central http://search.maven.org/,Group Id = org.apache.spark, Artifact Id = spark-streaming-flume-assembly, Version = 2.4.0.Then, include the jar in the spark-submit command as$ bin/spark-submit --jars <spark-streaming-flume-assembly.jar> ...
Traceback (most recent call last):File "/tmp/pycharm_project_563/day5/FlumePushWordCount.py", line 12, in <module>flumeStream = FlumeUtils.createStream(ssc, "192.168.62.131", "8888")File "/home/hadoop/spark/python/pyspark/streaming/flume.py", line 67, in createStreamhelper = FlumeUtils._get_helper(ssc._sc)File "/home/hadoop/spark/python/pyspark/streaming/flume.py", line 130, in _get_helperreturn sc._jvm.org.apache.spark.streaming.flume.FlumeUtilsPythonHelper()
TypeError: 'JavaPackage' object is not callable
然后运行flume
bin/flume-ng agent -n a1 -c conf/ -f conf/flume-push.conf -Dflume.root.logger=WARN,console
三、poll方式
1、spark streaming程序
这种方式是有spark主动去flume拉取数据，代码如下：
from pyspark.sql import SparkSession
from pyspark.streaming import StreamingContext
from pyspark.streaming.flume import FlumeUtilsif __name__ == "__main__":spark = SparkSession\.builder\.appName("PythonWordCount") \.master("local[2]") \.getOrCreate()sc = spark.sparkContextssc = StreamingContext(sc, 5)addresses = [("localhost", 8888)]flumeStream = FlumeUtils.createPollingStream(ssc, addresses)line = flumeStream.map(lambda x: x[1])words = line.flatMap(lambda x: x.split(" "))datas = words.map(lambda x: (x, 1))result = datas.reduceByKey(lambda agg, obj: agg + obj)result.pprint()ssc.start()ssc.awaitTermination()
如果是本地模式同样需要指定并行度，如果是在集群中运行，必须要求集群中可用core数大于1
2、flume conf文件
在flume的conf目录下新建flume-poll.conf内容如下:
# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
# source
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /home/hadoop/log/flume
a1.sources.r1.fileHeader = true# Describe the sink
a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink
a1.sinks.k1.hostname = localhost
a1.sinks.k1.port = 8888# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1
bin/flume-ng agent -n a1 -c conf/ -f conf/flume-poll.conf -Dflume.root.logger=WARN,console
spark/bin/spark-submit --driver-class-path /home/hadoop/spark/jars/*:/home/hadoop/jar/flume/* /tmp/pycharm_project_563/day5/FlumePollWordCount.py
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