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[[PageOutline]] = 實作二： HDFS 指令操作練習 = == 前言 == * 此部份接續實做一 == Content 1. 基本操作 == === 1.1 瀏覽你HDFS目錄 === {{{ /opt/hadoop$ bin/hadoop fs -ls }}} === 1.2 上傳資料到HDFS目錄 === * 上傳 {{{ /opt/hadoop$ bin/hadoop fs -put conf input }}} * 檢查 {{{ /opt/hadoop$ bin/hadoop fs -ls /opt/hadoop$ bin/hadoop fs -ls input }}} === 1.3 下載HDFS的資料到本地目錄 === * 下載 {{{ /opt/hadoop$ bin/hadoop fs -get input fromHDFS }}} * 檢查 {{{ /opt/hadoop$ ls -al | grep fromHDFS /opt/hadoop$ ls -al fromHDFS }}} === 1.4 刪除檔案 === {{{ /opt/hadoop$ bin/hadoop fs -ls input /opt/hadoop$ bin/hadoop fs -rm input/masters }}} === 1.5 直接看檔案 === {{{ /opt/hadoop$ bin/hadoop fs -ls input /opt/hadoop$ bin/hadoop fs -cat input/slaves }}} === 1.6 更多指令操作 === {{{ hadooper@vPro:/opt/hadoop$ bin/hadoop fs Usage: java FsShell [-ls ] [-lsr ] [-du ] [-dus ] [-count[-q] ] [-mv ] [-cp ] [-rm ] [-rmr ] [-expunge] [-put ... ] [-copyFromLocal ... ] [-moveFromLocal ... ] [-get [-ignoreCrc] [-crc] ] [-getmerge [addnl]] [-cat ] [-text ] [-copyToLocal [-ignoreCrc] [-crc] ] [-moveToLocal [-crc] ] [-mkdir ] [-setrep [-R] [-w] ] [-touchz ] [-test -[ezd] ] [-stat [format] ] [-tail [-f] ] [-chmod [-R] PATH...] [-chown [-R] [OWNER][:[GROUP]] PATH...] [-chgrp [-R] GROUP PATH...] [-help [cmd]] Generic options supported are -conf specify an application configuration file -D use value for given property -fs specify a namenode -jt specify a job tracker -files specify comma separated files to be copied to the map reduce cluster -libjars specify comma separated jar files to include in the classpath. -archives specify comma separated archives to be unarchived on the compute machines. The general command line syntax is bin/hadoop command [genericOptions] [commandOptions] }}} === 1.7 自我練習 === * 刪除在 hdfs 內的一整個的資料夾 input == Content 2. Hadoop 運算命令 == === 2.1 Hadoop運算命令 grep === * grep 這個命令是擷取文件裡面特定的字元，在Hadoop example中此指令可以擷取文件中有此指定文字的字串，並作計數統計 {{{ /opt/hadoop$ bin/hadoop jar hadoop-*-examples.jar grep input grep_output 'dfs[a-z.]+' }}} 運作的畫面如下： {{{ 09/03/24 12:33:45 INFO mapred.FileInputFormat: Total input paths to process : 9 09/03/24 12:33:45 INFO mapred.FileInputFormat: Total input paths to process : 9 09/03/24 12:33:45 INFO mapred.JobClient: Running job: job_200903232025_0003 09/03/24 12:33:46 INFO mapred.JobClient: map 0% reduce 0% 09/03/24 12:33:47 INFO mapred.JobClient: map 10% reduce 0% 09/03/24 12:33:49 INFO mapred.JobClient: map 20% reduce 0% 09/03/24 12:33:51 INFO mapred.JobClient: map 30% reduce 0% 09/03/24 12:33:52 INFO mapred.JobClient: map 40% reduce 0% 09/03/24 12:33:54 INFO mapred.JobClient: map 50% reduce 0% 09/03/24 12:33:55 INFO mapred.JobClient: map 60% reduce 0% 09/03/24 12:33:57 INFO mapred.JobClient: map 70% reduce 0% 09/03/24 12:33:59 INFO mapred.JobClient: map 80% reduce 0% 09/03/24 12:34:00 INFO mapred.JobClient: map 90% reduce 0% 09/03/24 12:34:02 INFO mapred.JobClient: map 100% reduce 0% 09/03/24 12:34:10 INFO mapred.JobClient: map 100% reduce 10% 09/03/24 12:34:12 INFO mapred.JobClient: map 100% reduce 13% 09/03/24 12:34:15 INFO mapred.JobClient: map 100% reduce 20% 09/03/24 12:34:20 INFO mapred.JobClient: map 100% reduce 23% 09/03/24 12:34:22 INFO mapred.JobClient: Job complete: job_200903232025_0003 09/03/24 12:34:22 INFO mapred.JobClient: Counters: 16 09/03/24 12:34:22 INFO mapred.JobClient: File Systems 09/03/24 12:34:22 INFO mapred.JobClient: HDFS bytes read=48245 09/03/24 12:34:22 INFO mapred.JobClient: HDFS bytes written=1907 09/03/24 12:34:22 INFO mapred.JobClient: Local bytes read=1549 09/03/24 12:34:22 INFO mapred.JobClient: Local bytes written=3584 09/03/24 12:34:22 INFO mapred.JobClient: Job Counters ...... }}} * 接著查看結果 {{{ /opt/hadoop$ bin/hadoop fs -ls grep_output /opt/hadoop$ bin/hadoop fs -cat grep_output/part-00000 }}} 結果如下 {{{ 3 dfs.class 3 dfs. 2 dfs.period 1 dfs.http.address 1 dfs.balance.bandwidth 1 dfs.block.size 1 dfs.blockreport.initial 1 dfs.blockreport.interval 1 dfs.client.block.write.retries 1 dfs.client.buffer.dir 1 dfs.data.dir 1 dfs.datanode.address 1 dfs.datanode.dns.interface 1 dfs.datanode.dns.nameserver 1 dfs.datanode.du.pct 1 dfs.datanode.du.reserved 1 dfs.datanode.handler.count 1 dfs.datanode.http.address 1 dfs.datanode.https.address 1 dfs.datanode.ipc.address 1 dfs.default.chunk.view.size 1 dfs.df.interval 1 dfs.file 1 dfs.heartbeat.interval 1 dfs.hosts 1 dfs.hosts.exclude 1 dfs.https.address 1 dfs.impl 1 dfs.max.objects 1 dfs.name.dir 1 dfs.namenode.decommission.interval 1 dfs.namenode.decommission.interval. 1 dfs.namenode.decommission.nodes.per.interval 1 dfs.namenode.handler.count 1 dfs.namenode.logging.level 1 dfs.permissions 1 dfs.permissions.supergroup 1 dfs.replication 1 dfs.replication.consider 1 dfs.replication.interval 1 dfs.replication.max 1 dfs.replication.min 1 dfs.replication.min. 1 dfs.safemode.extension 1 dfs.safemode.threshold.pct 1 dfs.secondary.http.address 1 dfs.servers 1 dfs.web.ugi 1 dfsmetrics.log }}} === 2.2 Hadoop運算命令 WordCount === * 如名稱，WordCount會對所有的字作字數統計，並且從a-z作排列 {{{ /opt/hadoop$ bin/hadoop jar hadoop-*-examples.jar wordcount input wc_output }}} 檢查輸出結果的方法同2.1的方法 === 2.3 更多運算命令 === 可執行的指令一覽表： || aggregatewordcount || An Aggregate based map/reduce program that counts the words in the input files. || || aggregatewordhist || An Aggregate based map/reduce program that computes the histogram of the words in the input files. || || grep || A map/reduce program that counts the matches of a regex in the input. || || join || A job that effects a join over sorted, equally partitioned datasets || || multifilewc || A job that counts words from several files. || || pentomino || A map/reduce tile laying program to find solutions to pentomino problems. || || pi || A map/reduce program that estimates Pi using monte-carlo method. || || randomtextwriter || A map/reduce program that writes 10GB of random textual data per node. || || randomwriter || A map/reduce program that writes 10GB of random data per node. || || sleep || A job that sleeps at each map and reduce task. || || sort || A map/reduce program that sorts the data written by the random writer. || || sudoku || A sudoku solver. || || wordcount || A map/reduce program that counts the words in the input files. || 請參考 [http://hadoop.apache.org/core/docs/current/api/org/apache/hadoop/examples/package-summary.html org.apache.hadoop.examples] {{{ #!html a.html

Class Summary
AggregateWordCount	This is an example Aggregated Hadoop Map/Reduce application. It reads the text input files, breaks each line into words and counts them. The output is a locally sorted list of words and the count of how often they occurred. To run: bin/hadoop jar hadoop-*-examples.jar aggregatewordcount in-dir out-dir numOfReducers textinputformat
AggregateWordHistogram	This is an example Aggregated Hadoop Map/Reduce application. Computes the histogram of the words in the input texts. To run: bin/hadoop jar hadoop-*-examples.jar aggregatewordhist in-dir out-dir numOfReducers textinputformat
ExampleDriver	A description of an example program based on its class and a human-readable description.
Grep
Join	This is the trivial map/reduce program that does absolutely nothing other than use the framework to fragment and sort the input values. To run: bin/hadoop jar build/hadoop-examples.jar join [-m maps] [-r reduces] [-inFormat input format class] [-outFormat output format class] [-outKey output key class] [-outValue output value class] [-joinOp ] [in-dir]* in-dir out-dir
RandomTextWriter	This program uses map/reduce to just run a distributed job where there is no interaction between the tasks and each task writes a large unsorted random sequence of words.To run: bin/hadoop jar hadoop-${version}-examples.jar randomtextwriter [-outFormat output format class] output
RandomWriter	This program uses map/reduce to just run a distributed job where there is no interaction between the tasks and each task write a large unsorted random binary sequence file of BytesWritable.To run: bin/hadoop jar hadoop-${version}-examples.jar randomwriter [-outFormat output format class] output
Sort<K,V>	This is the trivial map/reduce program that does absolutely nothing other than use the framework to fragment and sort the input values.To run: bin/hadoop jar build/hadoop-examples.jar sort [-m maps] [-r reduces] [-inFormat input format class] [-outFormat output format class] [-outKey output key class] [-outValue output value class] [-totalOrder pcnt num samples max splits] in-dir out-dir
WordCount	This is an example Hadoop Map/Reduce application.

}}} === 2.4 練習 === == Content 3. 使用網頁Gui瀏覽資訊 == * [http://localhost:50030 Map/Reduce Administration] * [http://localhost:50070 NameNode ] === 3.1 練習 === * 用網頁秀出你在 wordcount練習的輸出結果