I have a requirement, where i have to merge the output of mappers of a directory in to a single file. Lets say i have a directory A which contains 3 files.
../A/1.txt
../A/2.txt
../A/3.txt
I need to run a mapper to process these files which shud generate one output file. I KNOW REDUCER WILL DO THAT, BUT I DONT WANT TO USE REDUCER LOGIC.
OR
Can i have only one mapper to process all the files under a directory.
If you set up fuse to mount your HDFS to a local directory, then your output can be the mounted filesystem.
For example, I have our HDFS mounted to /mnt/hdfs locally. I run the following command and it works great:
hadoop fs -getmerge /reports/some_output /mnt/hdfs/reports/some_output.txt
Of course, there are other reasons to use fuse to mount HDFS to a local directory, but this was a nice side effect for us
Can i have only one mapper to process all the files under a directory.
Have you looked into CombinedFileInputFormat? Felix Ren-Chyan Chern writes about setting it up in some detail.
Related
I am learning hadoop. I came across a problem now. I ran the mapreduce job and output was stored in multiple files but not as single file. I want to append all of them into a single file in hdfs. I know about appendToFile and getmerge commands. But they work only for either local file system to hdfsor hdfs to local system but not from HDFS to HDFS. Is there any way to append the output files in HDFS to a single file in HDFS without touching local file system?
The only way to do this would be to force your mapreduce code to use one reducer, for example, by sorting all the results by a single key.
However, this defeats the purpose of having a distributed filesystem and multiple processors. All Hadoop jobs should be able to read a directory of files, not isolated to process a single file
If you need a single file to download from HDFS, then you should use getmerge
There is no easy way to do this directly in HDFS. But the below trick works. Although not a feasible solution, but should work if output is not huge.
hadoop fs -cat source_folder_path/* | hadoop fs -put target_filename
Is there any way to modify the txt file inside HDFS directly via terminal?
Assume, I have "my_text_file.txt", and I would like to modify it inside HDFS using below command.
$ hdfs dfs -XXXX user/my_text_file.txt
I am interested to know "xxxx" if there exists any.
Please note that I don't want to make modification in local and then copy it to HDFS.
You cannot edit files, which all are already in HDFS. It will not support. HDFS works on "Write once, read many". So if you want to edit a file, make changes in your local copy then move it to HDFS.
Currently as explained by #BruceWayne, its not possible. It would be very difficult to edit the files stored in hdfs because all the files are distributed in hdfs and it would be very difficult to edit in the terminal using hdfs commands. Currently these are supported as terminal commands.
You can edit them by locating the data location of each datanode in the cluster.But that would be troublesome.
Moreover you can install HUE. With HUE you can edit the files in hdfs using web UI.
You can not edit files in HDFS, as it works on the principle of Write once, Read Many.But now a day, we can edit file using Hue file browser in cloudera.
I would like to know, how does the getMerge command work in OS/HDFS level. Will it copy each and every byte/blocks from one file to another file,or just a simple file descriptor change? How costliest operation is it?
getmerge
Usage: hadoop fs -getmerge <src> <localdst> [addnl]
Takes a source directory and a destination file as input and concatenates files in src into the destination local file. Optionally addnl can be set to enable adding a newline character at the end of each file.
So, to answer your question,
Will it copy each and every byte/blocks from one file to another file
Yes, and no. It will find every HDFS block containing the files in the given source directory and concatenate them together into a single file on your local filesystem.
a simple file descriptor change
Not sure what you mean by that. getmerge doesn't change any file descriptors; it is just reading data from HDFS to your local filesystem.
How costliest operation is it?
Expect it to be as costly as manually cat-ing all the files in an HDFS directory. The same operation for
hadoop fs -getmerge /tmp/ /home/user/myfile
Could be achieved by doing
hadoop fs -cat /tmp/* > /home/user/myfile
The costly operation being the fetching of many file pointers and transferring those records over the network to your local disk.
I tried using flume directory spooler source and HDFS sink. But this does not serve my purpose because, the files are read by Flume and then get written to HDFS as part files which can be rolled by size/time (Please correct me if I've got this wrong). Is there a tool that continously does something like an HDFS put on all files that are dumped in the spool directory ?
If i got your question correctly then you have a and you are getting files into it and that file you want to move to HDFS without reading it and HDFS copyFromLocal will solve your issue then you just need to have an logic which can return you the recent files in the directory and run CopyFromLocal command to copy it in HDFS.
I'm trying to run the following example in hadoop: http://hadoop.apache.org/common/docs/current/mapred_tutorial.html
However I don't understand the commands that are being used, specifically how to create an input file, upload it to the HDFS and then run the word count example.
I'm trying the following command:
bin/hadoop fs -put inputFolder/inputFile inputHDFS/
however it says
put: File inputFolder/inputFile does not exist
I have this folder inside the hadoop folder which is the folder before "bin" so why is this happening?
thanks :)
Hopefully this isn't overkill:
Assuming you've installed hadoop (in either local, distributed or pseudo-distributed), you have to make sure hadoop's bin and other misc parameters are in your path. In linux/mac this is a simple matter of adding the following to one of your shell files (~/.bashrc, ~/.zshrc, ~/.bash_profile, etc. - depending on your setup and preferences):
export HADOOP_INSTALL_DIR=/path/to/hadoop # /opt/hadoop or /usr/local/hadoop, for example
export JAVA_HOME=/path/to/jvm
export PATH=$PATH:$HADOOP_INSTALL_DIR/bin
export PATH=$PATH:$HADOOP_INSTALL_DIR/sbin
Then run exec $SHELL or reload your terminal. To verify hadoop is running, type hadoop version and see that no errors are raised. Assuming you followed the instructions on how to set up a single node cluster and started hadoop services with the start-all.sh command, you should be good to go:
In pseudo-dist mode, your file system pretends to be HDFS. So just reference any path like you would with any other linux command, like cat or grep. This is useful for testing, and you don't have to copy anything around.
With an actual HDFS running, I use the copyFromLocal command (I find it to just work):
$ hadoop fs -copyFromLocal ~/data/testfile.txt /user/hadoopuser/data/
Here I've assumed your performing the copying on a machine that is part of the cluster. Note that if your hadoopuser is the same as your unix username, you can drop the /user/hadoopuser/ part - it is implicitly assumed to do everything inside your HDFS user dir. Also, if you're using a client machine to run commands on a cluster (you can do that too!), know that you'll need to pass the cluster's configuration using -conf flag right after hadoop fs, like:
# assumes your username is the same as the one on HDFS, as explained earlier
$ hadoop fs -conf ~/conf/hadoop-cluster.xml -copyFromLocal ~/data/testfile.txt data/
For the input file, you can use any file/s that contain text. I used some random files from the gutenberg site.
Last, to run the wordcount example (comes as jar in hadoop distro), just run the command:
$ hadoop jar /path/to/hadoop-*-examples.jar wordcount /user/hadoopuser/data/ /user/hadoopuser/output/wc
This will read everything in data/ folder (can have one or many files) and write everything to output/wc folder - all on HDFS. If you run this in pseudo-dist, no need to copy anything - just point it to proper input and output dirs. Make sure the wc dir doesn't exist or your job will crash (cannot write over existing dir). See this for a better wordcount breakdown.
Again, all this assumes you've made it through the setup stages successfully (no small feat).
Hope this wasn't too confusing - good luck!