flume将数据导入到hbase中
接着前面的练习，flume已经安装完成并可以正常运行。
运行/opt/flume/bin/flume-ng version可以查看安装的版本。
1.把/opt/hbase/lib目录下的以下文件复制替换到/opt/flume/lib目录下面
[root@elephant lib]# cp protobuf-java-2.5.0.jar /opt/flume/lib
cp: overwrite `/opt/flume/lib/protobuf-java-2.5.0.jar'? yes
[root@elephant lib]# cp hbase-protocol-1.2.6.jar /opt/flume/lib
[root@elephant lib]# cp hbase-client-1.2.6.jar /opt/flume/lib
[root@elephant lib]# cp hbase-common-1.2.6.jar /opt/flume/lib
[root@elephant lib]# cp hbase-server-1.2.6.jar /opt/flume/lib
[root@elephant lib]# cp hbase-hadoop2-compat-1.2.6.jar /opt/flume/lib
[root@elephant lib]# cp hbase-hadoop-compat-1.2.6.jar /opt/flume/lib
[root@elephant lib]# cp htrace-core-3.1.0-incubating.jar /opt/flume/lib
2.启动hbase,创建表[root@elephant bin]# hbase shell
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/hbase/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
HBase Shell; enter 'help' for list of supported commands.
Type "exit" to leave the HBase Shell
Version 1.2.6, rUnknown, Mon May 29 02:25:32 CDT 2017
hbase(main):001:0> create 'test_idoall_org','uid','name'
0 row(s) in 3.1930 seconds
=> Hbase::Table - test_idoall_org
hbase(main):002:0> put 'test_idoall_org','10086','name:idoall','idoallvalue'
0 row(s) in 0.3450 seconds
hbase(main):003:0> list
TABLE
test_idoall_org
1 row(s) in 0.0230 seconds
3.在/opt/flume/conf/目录下，创建hbase_simple.conf文件，设置如下
[root@elephant conf]# cat hbase_simple.confa1.sources = r1
a1.sinks = k1
a1.channels = c1
# Describe/configure the source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /tmp/logs/data.txt
a1.sources.r1.port = 44444
a1.sources.r1.host = 192.168.205.136
a1.sources.r1.channels = c1
# Describe the sink
a1.sinks.k1.type = logger
a1.sinks.k1.type = hbase
a1.sinks.k1.table = test_idoall_org
a1.sinks.k1.columnFamily = name
a1.sinks.k1.column = idoall
a1.sinks.k1.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer
a1.sinks.k1.channel = memoryChannel
# 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
4.启动flume agent
[root@elephant conf]# bin/flume-ng agent --conf-file conf/hbase_simple.conf -n a1 -Dflume.root.logger=INFO,console
5.生成数据
[root@elephant kafka]# cd /tmp/logs
[root@elephant logs]# ls
test.log test.log.COMPLETED
[root@elephant logs]# touch data.txt
[root@elephant logs]# ls
data.txt test.log test.log.COMPLETED
[root@elephant logs]# echo "hello idoall.org from flume">>data.txt
[root@elephant logs]# echo "hello this is a flume to hbase test code">>data.txt
6.查看hbase，可以看到数据已经插入hbase对应的表格
hbase(main):004:0> scan 'test_idoall_org'
ROW COLUMN+CELL
10086 column=name:idoall, timestamp=1522723531960, value=idoallvalue
1522724221086-BaBHa2Hs6n-0 column=name:payload, timestamp=1522724224327, value=hello idoall.org from flume
2 row(s) in 0.0830 seconds
hbase(main):005:0> scan 'test_idoall_org'
ROW COLUMN+CELL
10086 column=name:idoall, timestamp=1522723531960, value=idoallvalue
1522724221086-BaBHa2Hs6n-0 column=name:payload, timestamp=1522724224327, value=hello idoall.org from flume
2 row(s) in 0.0570 seconds