The Hadoop system has its unique shell language, which is called FS. Comparing with the common Bash shell within the Linux ecosystem, the FS shell has much fewer commands. To deal with the humongous size of data distributively stored at the Hadoop nodes, in my practice, I have 10 popular Linux command to facilitate my daily work.
1. sort
A good conduct of running Hadoop is to always test the map/reduce programs at the local machine before releasing the time-consuming map/reduce codes to the cluster environment. The sort command simulates the sort and shuffle step necessary for the map/redcue process. For example, I can run the piped commands below to verify whether the Python codes have any bugs.
./ | sort | ./
2. tail
Interestingly, the FS shell at Hadoop only supports the tail command instead of the head command. Then I can only grab the bottom lines of the data stored at Hadoop.
hadoop fs -tail 5 data/web.log.9
3. sed
Sine the FS shell doesn’t provide the head command, the alternative solution is to use the sed command that actually has more flexible options.
hadoop fs -cat data/web.log.9 | sed '1,+5!d'
4. stat
The stat command allows me to know the time when the file has been touched.
hadoop fs -stat data/web.log.9
5. awk
The commands that the FS shell supports usually have very few options. For example the du command under the FS shell does not support -sh option to aggregate the disk usage of the sub-directories. In this case, I have to look for help from the awk command to satisfy my need.
hadoop fs -du data | awk '{sum+=$1} END {print sum}'
6. wc
One of the most important things to understand a file located at the Hadoop is to find the number of its total lines.
hadoop fs -cat data/web.log.9 | wc -l
7. cut
The cut command is convenient to select the specified columns at the file. For example, I am able to count the lines for each of the unique groups from the column between the position of #5 and #14.
hadoop fs -cat data/web.log.9 | cut -c 5-14 | uniq -c
8. getmerge
The great thing for the getmerge command is that I am able to fetch all the result after map/reduce to the local file system as a single file.
hadoop fs -getmerge result result_merged.txt
9. grep
I can start a mapper-only job only with the grep command form the Bash shell to search the lines which contain the key words I am interested in. And this is a map-only task.
hadoop jar $STREAMING -D mapred.reduce.tasks=0 -input data -output result -mapper "bash -c 'grep -e Texas'"
10. at and crontab
The at and crontab commnands are my favorite to schedule a job at Hadoop. For example, I would like to use the order below to clean the map/reduce results at midnight.
at 0212
at > hadoop fs -rmr result