项目中打算使用Flume把数据直接传到Hive表而不是HDFS上，使用Hive作为Sink，Flume版本为1.9.0。
前期启动遇到各种报错：
NoClassDefFoundError: org/apache/hadoop/hive/ql/session/SessionState
NoClassDefFoundError: org/apache/hadoop/hive/cli/CliSessionState
NoClassDefFoundError: org/apache/hadoop/hive/conf/HiveConf
NoClassDefFoundError: org/apache/hadoop/conf/Configuration
NoClassDefFoundError: org/apache/hadoop/hive/conf/HiveConf
java.lang.NoClassDefFoundError: com/esotericsoftware/kryo/Serializer
java.lang.ClassNotFoundException: com.esotericsoftware.kryo.Serializer
NoClassDefFoundError: org/antlr/runtime/RecognitionException
解决：
将相应的jar包一股脑拷过去：
例如，CDH中的jar包目录是：
/data/cloudera/parcels/CDH-5.11.2-1.cdh5.11.2.p0.4/jars
进入到目录后：
scp hive-* root@172.28.65.106:/usr/local/flume/lib
scp hadoop-* root@172.28.65.106:/usr/local/flume/lib
scp antlr-* root@172.28.65.106:/usr/local/flume/lib
scp kryo-2.22.jar root@172.28.65.106:/usr/local/flume/lib
设置flume配置文件：
# example.conf: A single-node Flume configuration
# Name the components on this agent
video_hive.sources = r1
video_hive.sinks = k1
video_hive.channels = c1
# Describe/configure the source
video_hive.sources.r1.type = netcat
video_hive.sources.r1.bind = localhost
video_hive.sources.r1.port = 44444
# Describe the sink
video_hive.sinks.k1.type = hive
video_hive.sinks.k1.channel = c1
video_hive.sinks.k1.hive.metastore = thrift://dev07.hadoop.openpf:9083
#video_hive.sinks.k1.hive.metastore = thrift://172.28.23.21:9083
video_hive.sinks.k1.hive.database = recommend_video
video_hive.sinks.k1.hive.table = video_test
#video_hive.sinks.k1.hive.table = user_video_action_log
video_hive.sinks.k1.hive.partition = %Y-%m-%d
#video_hive.sinks.k1.autoCreatePartitions = false
video_hive.sinks.k1.useLocalTimeStamp = true
video_hive.sinks.k1.batchSize = 1500
#video_hive.sinks.k1.round = true
#video_hive.sinks.k1.roundValue = 10
#video_hive.sinks.k1.roundUnit = minute
video_hive.sinks.k1.serializer = DELIMITED
video_hive.sinks.k1.serializer.delimiter = ","
video_hive.sinks.k1.serializer.serdeSeparator = ','
video_hive.sinks.k1.serializer.fieldnames = timestamp,userid,videoid,totaltime,playtime,hits,rate,praise
# Use a channel which buffers events in memory
video_hive.channels.c1.type = memory
video_hive.channels.c1.capacity = 2000
video_hive.channels.c1.transactionCapacity = 1500
# Bind the source and sink to the channel
video_hive.sources.r1.channels = c1
video_hive.sinks.k1.channel = c1
注意建表语句（clustered分桶、transactional事务、orc存储格式）：
create table if not exists video_test ( `timestamp` string,
`userid` string,
`videoid` string,
`totaltime` string,
`playtime` string,
`rate` string,
`hits` string,
`praise` string ) COMMENT '用户视频日志'
partitioned by (date string)
clustered by (userid) into 5 buckets
row format delimited fields terminated by ','
stored as orc
tblproperties("transactional"='true');
终极大招：
最后还不行的话，将hdfs-site.xml，hive-conf.properties，hive-site.xml，hive-env.sh拷到flume的配置文件下。如：/usr/local/flume/conf
大功搞成！！
附上 启动flume后台的脚本编写：
#!/bin/sh
FLUME_HOME=/usr/local/flume
#
#nohup java ${JAVA_OPT} -jar ${APPLICATION_JAR} --spring.profiles.active=dev --spring.config.location="config/" >/dev/null 2>&1 &
nohup flume-ng agent --conf ${FLUME_HOME}/conf --conf-file ${FLUME_HOME}/conf/flume-hive.properties --name video_hive -Dflume.root.logger=INFO,LOGFILE &
重点：hive问题点：
flume对接的hive表必须要求是orc格式存储的，通过日志文件读取文档到hive表后，使用where语句查询会有问题（不用条件筛选查询正常）。而且没有找到设置其他存储格式的配置方法，所以最终放弃hive，转向hdfs。。。
附查询错误截图：
如果flume直接对接textfile格式的hive表，也会有问题：
总体感觉，flume对接hive方案还不成熟，存在各种小问题等。
欢迎各路大神讨论交流！！！
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flume对接hdfs：
# example.conf: A single-node Flume configuration
# Name the components on this agent
video_hdfs.sources = s1
video_hdfs.sinks = k1
video_hdfs.channels = c1
# Describe/configure the source
##netcat
#video_hdfs.sources.s1.type = netcat
#video_hdfs.sources.s1.bind = localhost
#video_hdfs.sources.s1.port = 44444
##exec
#video_hdfs.sources = s1
#video_hdfs.sources.s1.type = exec
#video_hdfs.sources.s1.command = tail -F /home/recommend/recom-video/logs/user-video.log
#video_hdfs.sources.s1.channels = c1
##TAILDIR
video_hdfs.sources.s1.type = TAILDIR
# 元数据位置
video_hdfs.sources.s1.positionFile = /usr/local/flume/conf/taildir_position.json
# 监控的目录
video_hdfs.sources.s1.filegroups = f1
video_hdfs.sources.s1.filegroups.f1 = /home/recommend/recom-video/logs/user-video.log
video_hdfs.sources.s1.fileHeader = true
# Describe the sink
video_hdfs.sinks.k1.type = hdfs
video_hdfs.sinks.k1.channel = c1
video_hdfs.sinks.k1.type = hdfs
video_hdfs.sinks.k1.hdfs.path = hdfs://nameservice1/user/hive/warehouse/recommend_video.db/video_test/dayid=%Y%m%d
video_hdfs.sinks.k1.hdfs.fileType = DataStream
video_hdfs.sinks.k1.hdfs.writeFormat=TEXT
video_hdfs.sinks.k1.hdfs.filePrefix = events-
video_hdfs.sinks.k1.hdfs.fileSuffix = .log
video_hdfs.sinks.k1.hdfs.useLocalTimeStamp = true
video_hdfs.sinks.k1.hdfs.round = true
video_hdfs.sinks.k1.hdfs.roundValue = 1
video_hdfs.sinks.k1.hdfs.roundUnit = hour
# Use a channel which buffers events in memory
video_hdfs.channels.c1.type = memory
video_hdfs.channels.c1.capacity = 20000
video_hdfs.channels.c1.transactionCapacity = 15000
# Bind the source and sink to the channel
video_hdfs.sources.s1.channels = c1
video_hdfs.sinks.k1.channel = c1
将日志文件直接存储在hdfs中对应的hive表及分区下。但是这样直接通过hive语句查询不到数据。
通过测试，手动创建对应的分区后可以查询到数据。。。
建表语句使用默认textfile即可。
create table if not exists video_test (
timestamp string,
userid string,
videoid string,
totaltime string,
playtime string,
rate string,
hits string,
praise string ) COMMENT '用户视频日志'
partitioned by (dayid string)
row format delimited fields terminated by ',' ;
待补充。
为了将hive表与同步日志关联，需要定时创建表分区。首先编写脚本如下：
create_partitions_video_log.sh
#!/bin/bash
#start
now=`date +%Y%m%d%H%M%S`
echo "now: "${now}
echo "START... NOW DATE:" $(date +"%Y-%m-%d %H:%M:%S")
#获得后一天的日期
#todayday=`date -d now +%Y%m%d`
tomorrowday=`date -d "+1 day" +%Y%m%d`
echo "the date is: ${tomorrowday}..."
#echo "the date is: ${todayday}..."
#
#hive -e "USE recommend_video; ALTER TABLE t_di_user_action_log_id ADD IF NOT EXISTS PARTITION (dayid='${tomorrowday}');"
/usr/bin/hive -e "USE recommend_video; ALTER TABLE video_test ADD IF NOT EXISTS PARTITION (dayid='${tomorrowday}');"
linux服务器crontab定时任务设置如下：
#02 21 * * 1 sh /data/ceshi/hbase-hive-music.sh > /data/ceshi/music.log &
#22 20 * * 1 /data/ceshi/hbase-hive-novel.sh > /data/ceshi/novel.log &
#30 11 * * 1 sh /data/ceshi/hbase-hive-video.sh
#45 11 * * 1 sh /data/ceshi/hbase-hive-mother.sh
#0 12 * * 1 sh /data/ceshi/hbase-hive-sports.sh
06 16 * * * /bin/bash /root/lpz/create_partitions_video_log.sh >> /root/lpz/video_log.log 2>&1
中间会有各种报错（根据不同服务器，可能不一样）：
Unable to determine Hadoop version information.
'hadoop version' returned:
Error: JAVA_HOME is not set and could not be found.
解决方案：
/etc/profile文件中增加：export HADOOP_VERSION=2.6.0-cdh5.11.2
/usr/bin/hive文件中增加：export JAVA_HOME=/data/jdk1.8.0_171
不知道为啥要特意添加这些常量，其实profile已经配置过了。。。。
附录：优化后的kafka对接hdfs配置文件：
# The configuration file needs to define the sources,
# the channels and the sinks.
# Sources, channels and sinks are defined per agent,
# in this case called 'agent'
# Name the components on this agent
kafka_hdfs.sources = s1
kafka_hdfs.sinks = k1
kafka_hdfs.channels = c1
#kafka sources
kafka_hdfs.sources.s1.type = org.apache.flume.source.kafka.KafkaSource
kafka_hdfs.sources.s1.batchSize = 100
kafka_hdfs.sources.s1.batchDurationMillis = 10000
kafka_hdfs.sources.s1.kafka.bootstrap.servers = dev05.hadoop.openpf:9092, dev06.hadoop.openpf:9092, dev07.hadoop.openpf:9092
kafka_hdfs.sources.s1.kafka.topics = recom_video_hjq
kafka_hdfs.sources.s1.kafka.consumer.group.id = flume-cdh
kafka_hdfs.sources.s1.interceptors = i1
kafka_hdfs.sources.s1.interceptors.i1.type = com.cmcc.hy.recommend.flume.VideoLogInterceptor$Builder
# Describe the sink
kafka_hdfs.sinks.k1.type = hdfs
kafka_hdfs.sinks.k1.channel = c1
kafka_hdfs.sinks.k1.type = hdfs
#kafka_hdfs.sinks.k1.hdfs.path = hdfs://nameservice1/user/hive/warehouse/recommend_video.db/t_di_user_action_log_id/dayid=%Y%m%d
kafka_hdfs.sinks.k1.hdfs.path = hdfs://nameservice1/tmp/recommend/lpz-cdh/%Y%m%d
kafka_hdfs.sinks.k1.hdfs.fileType = DataStream
kafka_hdfs.sinks.k1.hdfs.writeFormat=TEXT
kafka_hdfs.sinks.k1.hdfs.filePrefix = events
kafka_hdfs.sinks.k1.hdfs.fileSuffix = .log
kafka_hdfs.sinks.k1.hdfs.useLocalTimeStamp = true
kafka_hdfs.sinks.k1.hdfs.rollInterval=600
kafka_hdfs.sinks.k1.hdfs.rollSize=1024000
kafka_hdfs.sinks.k1.hdfs.rollCount=1000
#kafka_hdfs.sinks.k1.hdfs.round = true
#kafka_hdfs.sinks.k1.hdfs.roundValue = 1
#kafka_hdfs.sinks.k1.hdfs.roundUnit = hour
# Use a channel which buffers events in memory
kafka_hdfs.channels.c1.type = memory
kafka_hdfs.channels.c1.capacity = 20000
kafka_hdfs.channels.c1.transactionCapacity = 1000
# Bind the source and sink to the channel
kafka_hdfs.sources.s1.channels = c1
kafka_hdfs.sinks.k1.channel = c1