wiki:III130615/Lab10

Version 1 (modified by jazz, 11 years ago) (diff)

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  1. 實作十 Lab10
    1. 準備輸入資料集 Input Dataset
    2. 觀察 Hadoop Streaming 執行身份與工作目錄
    3. 實作透過 Hadoop Streaming 執行 99 組 velvet 運算

◢ <實作九> | <回課程大綱> ▲ | <實作十一> ◣ 

請連線至 hadoop.nchc.org.tw 作練習

實作十 Lab10

HDFS、MapReduce 與 Hadoop Streaming 觀念驗證
Running Hadoop Streaming with HDFS and MapReduce

準備輸入資料集 Input Dataset

  • 首先準備輸入，包含兩部份：(1) 供 Velvet 計算的 *.fa 檔案，這裡為了方便示範起見，採用 test_long.fa 當範本，並複製 99 份不同檔名，當輸入 (2) 供 Mapper 運算的輸入檔案（內含 HDFS 的檔名路徑） 
    ~$ cp /usr/share/doc/velvet-example/examples/data/test_long.fa.gz .
~$ gunzip test_long.fa.gz
~$ for ((i=1;i<100;i++)); do hadoop fs -put test_long.fa input-$i.fa; done
~$ for ((i=1;i<20;i++)); do echo /user/$(whoami)/input-$i.fa; done > split-01.txt
~$ for ((i=20;i<40;i++)); do echo /user/$(whoami)/input-$i.fa; done > split-02.txt
~$ for ((i=40;i<60;i++)); do echo /user/$(whoami)/input-$i.fa; done > split-03.txt
~$ for ((i=60;i<80;i++)); do echo /user/$(whoami)/input-$i.fa; done > split-04.txt
~$ for ((i=80;i<100;i++)); do echo /user/$(whoami)/input-$i.fa; done > split-05.txt
~$ hadoop fs -mkdir lab10_input
~$ hadoop fs -put split-0* lab10_input
  • 檢查輸入檔案 
    ~$ hadoop fs -ls
~$ hadoop fs -ls lab10_input

觀察 Hadoop Streaming 執行身份與工作目錄

  • 撰寫 testmapper.sh 
    #!/bin/bash

id="h998"
mkdir -p /tmp/$id
host=`hostname`
pwd=`pwd`
uid=`whoami`

while read line; do 
  input=$line
  filename=`basename $input`
  echo "$uid@$host:$pwd> hadoop fs -get $input /tmp/$id/$filename"
  echo "$uid@$host:$pwd> velveth output-$filename 17 -fasta -short /tmp/$id/$filename"
  echo "$uid@$host:$pwd> hadoop fs -put output-$filename ."
done
rm -rf /tmp/$id
  • 接著，讓我們在本地端先驗證一下 testmapper.sh 的運作 
    ~$ head -n 2 split-01.txt > split-00.txt
~$ cat > testmapper.sh << EOF
#!/bin/bash

id="`whoami`"
mkdir -p /tmp/\$id
host=\`hostname\`
pwd=\`pwd\`
uid=\`whoami\`

while read line; do 
  input=\$line
  filename=\`basename \$input\`
  echo "\$uid@\$host:\$pwd> hadoop fs -get \$input /tmp/\$id/\$filename"
  echo "\$uid@\$host:\$pwd> velveth output-\$filename 17 -fasta -short /tmp/\$id/\$filename"
  echo "\$uid@\$host:\$pwd> hadoop fs -put output-\$filename ."
done
rm -rf /tmp/\$id
EOF
~$ chmod a+x testmapper.sh
~$ cat sample-00.txt | ./testmapper.sh 
h998@hadoop:/home/h998> hadoop fs -get /user/h998/input-1.fa /tmp/h998/input-1.fa
h998@hadoop:/home/h998> velveth output-input-1.fa 17 -fasta -short /tmp/h998/input-1.fa
h998@hadoop:/home/h998> hadoop fs -put output-input-1.fa .
h998@hadoop:/home/h998> hadoop fs -get /user/h998/input-2.fa /tmp/h998/input-2.fa
  • 讓我們用 Hadoop Streaming 的方式來執行 testmapper.sh 
    ~$ hadoop jar hadoop-streaming.jar -input lab10_input -output lab10_out1 -mapper testmapper.sh -file testmapper.sh
  • 觀察 lab10_out1 的結果，看與本機執行有何不同呢？ 
    ~$ hadoop fs -cat /user/$(whoami)/lab10_out1/part-00000 | head
hadoop@hadoop104:/var/lib/hadoop/cache/hadoop/mapred/local/taskTracker/jobcache/job_201106041247_1820/attempt_201106041247_1820_m_000002_0/work> hadoop fs -get /user/h998/input-60.fa /tmp/h998/input-60.fa	
hadoop@hadoop104:/var/lib/hadoop/cache/hadoop/mapred/local/taskTracker/jobcache/job_201106041247_1820/attempt_201106041247_1820_m_000002_0/work> hadoop fs -get /user/h998/input-61.fa /tmp/h998/input-61.fa	
hadoop@hadoop104:/var/lib/hadoop/cache/hadoop/mapred/local/taskTracker/jobcache/job_201106041247_1820/attempt_201106041247_1820_m_000002_0/work> hadoop fs -get /user/h998/input-62.fa /tmp/h998/input-62.fa

實作透過 Hadoop Streaming 執行 99 組 velvet 運算

  • 撰寫 mapper.sh 
    #!/bin/bash

id="h998"
mkdir -p /tmp/$id
host=`hostname`
pwd=`pwd`
uid=`whoami`

while read line; do 
  input=$line
  filename=`basename $input`
  echo "$uid@$host> hadoop fs -get $input /tmp/$id/$filename"
  hadoop fs -get $input /tmp/$id/$filename
  echo "$uid@$host> velveth output-$filename 17 -fasta -short /tmp/$id/$filename"
  velveth output-$filename 17 -fasta -short /tmp/$id/$filename
  echo "$uid@$host> hadoop fs -put output-$filename /user/$id/."
  hadoop fs -put output-$filename /user/$id/.
  hadoop fs -chown $id /user/$id/output-$filename
done
rm -rf /tmp/$id
  • 於本機測試 mapper.sh 
    ~$ cat > mapper.sh << EOF
#!/bin/bash

id="`whoami`"
mkdir -p /tmp/\$id
host=\`hostname\`
pwd=\`pwd\`
uid=\`whoami\`

while read line; do 
  input=\$line
  filename=\`basename \$input\`
  echo "\$uid@\$host> hadoop fs -get \$input /tmp/\$id/\$filename"
  hadoop fs -get \$input /tmp/\$id/\$filename
  echo "\$uid@\$host> velveth output-\$filename 17 -fasta -short /tmp/\$id/\$filename"
  velveth output-\$filename 17 -fasta -short /tmp/\$id/\$filename
  echo "\$uid@\$host> hadoop fs -put output-\$filename /user/\$id/."
  hadoop fs -put output-\$filename /user/\$id/.
  hadoop fs -chown \$id /user/\$id/output-\$filename
done
rm -rf /tmp/\$id
EOF
~$ chmod a+x mapper.sh
~$ cat sample-00.txt | ./mapper.sh 
~$ hadoop fs -rmr output-*
~$ rm -rf output-sample-*
  • 接著用 hadoop streaming 來執行 
    ~$ hadoop jar hadoop-streaming.jar -input lab10_input -output lab10_out2 -mapper mapper.sh -file mapper.sh