I was running hadoop distcp to copy a whole directory (500GB+) from /path/to/source to /path/to/destination. However, instead of running
$ hadoop distcp /path/to/source /path/to/destination
I did the following in mistake
$ hadoop distcp /path/to/source path/to/destination
The operation completed like a normal distcp copy, with mapreduce taking some time to run, and of course I did not get my data in /path/to/destination. It was also not in /path/to/source/path/to/destination, or other relative paths I could think of.
Where did the data go? Thanks.
It doesn't go anywhere if the destination path is not correct it stays in the source location
Related
I'm sorry if this is a rather simple question, but I haven't found anything exactly online and just needed a quick answer.
I am trying to copy files from one HDFS directory to a new directory to make a backup. I was given something like this:
hadoop fs -mkdir one/two/three/dir1_bkp
hadoop fs -cp one/two/three/dir1/* one/two/three/dir1_bkp
This should only copy all of the files in dir1 to dir1_bkp and not affect anything in dir1, correct?
Copying doesn't affect the source location, no.
Depending on the size of the data, distcp might be a better option
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
I am reading "Hadoop: The Defnitive Guide" and to explain my question let me quote from the book
distcp is implemented as a MapReduce job where the work of copying is done by the
maps that run in parallel across the cluster. There are no reducers. Each file is copied
by a single map, and distcp tries to give each map approximately the same amount of
data by bucketing files into roughly equal allocations. By default, up to 20 maps are used, but this can be changed by specifying the -m argument to distcp.
and in a footnote
Even for a single file copy, the distcp variant is preferred for large files since hadoop fs -cp copies the file
via the client running the command.
I understand why distcp works better for collection of files as different mappers are performing parallelly each on a single file. But when only a single file is to be copied why distcp performs better when the file size is large (according to the footnote). I am only getting started so it would be helpful if how cp command in hadoop works is explained and what is meant by "hadoop fs -cp copies the file via the client running the command.". I understand the write process of Hadoop which is explained in the book where a pipeline of datanodes are formed and each datanode is responsible to write data to the following datanode in the pipeline.
When a file is copied "via the client", the byte content is streamed from HDFS, to the local node running the command, then uploaded back to the destination HDFS location. The file metadata is not simply copied over to a new spot between datanodes directly as you'd expect.
Compare that to distcp, which creates smaller, parallel cp commands spread out over multiple hosts
I am a beginner in hadoop. I have two doubts
1) how to access files stored in the hdfs? Is it same as using a FileReader in java.io and giving the local path or is it something else?
2) i have created a folder where i have copied the file to be stored in hdfs and the jar file of the mapreduce program. When I run the command in any directory
${HADOOP_HOME}/bin/hadoop dfs -ls
it just shows me all the files in the current dir. So does that mean all the files got added without me explicitly adding it?
Yes, it's pretty much the same. Read this post to read files from HDFS.
You should keep in mind that HDFS is different than your local file system. With hadoop dfs you access the HDFS, not the local file system. So, hadoop dfs -ls /path/in/HDFS shows you the contents of the /path/in/HDFS directory, not the local one. That's why it's the same, no matter where you run it from.
If you want to "upload" / "download" files to/from HDFS you should use the commads:
hadoop dfs -copyFromLocal /local/path /path/in/HDFS and
hadoop dfs -copyToLocal /path/in/HDFS /local/path, respectively.
I'm trying to run the following example in hadoop: http://hadoop.apache.org/common/docs/current/mapred_tutorial.html
However I don't understand the commands that are being used, specifically how to create an input file, upload it to the HDFS and then run the word count example.
I'm trying the following command:
bin/hadoop fs -put inputFolder/inputFile inputHDFS/
however it says
put: File inputFolder/inputFile does not exist
I have this folder inside the hadoop folder which is the folder before "bin" so why is this happening?
thanks :)
Hopefully this isn't overkill:
Assuming you've installed hadoop (in either local, distributed or pseudo-distributed), you have to make sure hadoop's bin and other misc parameters are in your path. In linux/mac this is a simple matter of adding the following to one of your shell files (~/.bashrc, ~/.zshrc, ~/.bash_profile, etc. - depending on your setup and preferences):
export HADOOP_INSTALL_DIR=/path/to/hadoop # /opt/hadoop or /usr/local/hadoop, for example
export JAVA_HOME=/path/to/jvm
export PATH=$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!