Sqoop是一种旨在有效地在Apache Hadoop和诸如关系数据库等结构化数据存储之间传输大量数据的工具
原理: 将导入或导出命令翻译成Mapreduce程序来实现。 在翻译出的Mapreduce中主要是对InputFormat和OutputFormat进行定制
sqoop import --connect jdbc:mysql://hadoop102:3306/company --username root --password 123456 --table staff --target-dir /user/company --delete-target-dir --num-mappers 1 --fields-terminated-by "\t"
sqoop import --connect jdbc:mysql://hadoop102:3306/company --username root --password 123456 --table staff --num-mappers 1 --hive-import --fields-terminated-by "\t" --hive-overwrite --hive-table staff_hive
sqoop export --connect jdbc:mysql://hadoop102:3306/company --username root --password 123456 --table staff --num-mappers 1 --export-dir /user/hive/warehouse/staff_hive --input-fields-terminated-by "\t"
Flume高可用的,高可靠的,分布式的海量日志采集、聚合和传输的系统。Flume基于流式架构,灵活简单, 主要作用就是将服务器里的磁盘数据写入HDFS
vim flume-dir-hdfs.conf添加如下内容
a3.sources = r3a3.sinks = k3a3.channels = c3# Describe/configure the sourcea3.sources.r3.type = spooldira3.sources.r3.spoolDir = /opt/module/flume/uploada3.sources.r3.fileSuffix = .COMPLETEDa3.sources.r3.fileHeader = true#忽略所有以.tmp结尾的文件,不上传a3.sources.r3.ignorePattern = ([^ ]*\.tmp)# Describe the sinka3.sinks.k3.type = hdfsa3.sinks.k3.hdfs.path = hdfs://hadoop101:9000/flume/upload/%Y%m%d/%H#上传文件的前缀a3.sinks.k3.hdfs.filePrefix = upload-#是否按照时间滚动文件夹a3.sinks.k3.hdfs.round = true#多少时间单位创建一个新的文件夹a3.sinks.k3.hdfs.roundValue = 1#重新定义时间单位a3.sinks.k3.hdfs.roundUnit = hour#是否使用本地时间戳a3.sinks.k3.hdfs.useLocalTimeStamp = true#积攒多少个Event才flush到HDFS一次a3.sinks.k3.hdfs.batchSize = 100#设置文件类型,可支持压缩a3.sinks.k3.hdfs.fileType = DataStream#多久生成一个新的文件a3.sinks.k3.hdfs.rollInterval = 30#设置每个文件的滚动大小大概是128Ma3.sinks.k3.hdfs.rollSize = 134217700#文件的滚动与Event数量无关a3.sinks.k3.hdfs.rollCount = 0# Use a channel which buffers events in memorya3.channels.c3.type = memorya3.channels.c3.capacity = 1000a3.channels.c3.transactionCapacity = 100# Bind the source and sink to the channela3.sources.r3.channels = c3a3.sinks.k3.channel = c3
启动监控文件夹命令
flume-ng agent --conf conf/ --name a3 --conf-file job/flume-dir-hdfs.conf
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