I need to run a simple Java based deeplearning4j example in hadoop cluster and I found one here. My need to specify the input from command line (which should be a path on HDFS) and output should go to HDFS, for later view
However, in the example there is no mention, it is hard coding the input from local file system and output goes to local file system.
Can anyone help me here?
Maybe some combination of this recent pull request on our examples:
https://github.com/deeplearning4j/dl4j-examples/pull/384
and Spring-hadoop could help you?
http://projects.spring.io/spring-hadoop/
I mean conceptually all you'd do is change the file system type.
The FileSystem api in hadoop can point to either local or an hdfs url so there shouldn't be much change.
I want to consume a user selected file in Hadoop, through given user interface. What should be the approach, is it is a wise decision to use Hadoop in web
There are two issues - If you should use hadoop, and how to use hadoop.
the typical file you can expect a user uploading through a web interface is much smaller then the data size where hadoop begins to be relevant.
So it's very probable that hadoop isn't the right choice for this scenario
It's hard to know what the problem is without more detailed logs.
But the most common case, if the class that isn't found is your class (and not an infrastructure class) is that you need to distribute your jars to all the hadoop tasks.
a simple solution is to use the -libjars parameter when running your application
see a good explanation here
I want to run some executables outside of hadoop (but on the same cluster) using input files that are stored inside HDFS.
Do these files need to be copied locally to the node? or is there a way to access HDFS outside of hadoop?
Any other suggestions on how to do this are fine. Unfortunately my executables can not be run within hadoop though.
Thanks!
There are a couple typical ways:
You can access HDFS files through the HDFS Java API if you are writing your program in Java. You are probably looking for open. This will give you a stream that acts like a generic open file.
You can stream your data with hadoop cat if your program takes input through stdin: hadoop fs -cat /path/to/file/part-r-* | myprogram.pl. You could hypothetically create a bridge with this command line command with something like popen.
Also check WebHDFS which made into the 1.0.0 release and will be in the 23.1 release also. Since it's based on rest API, any language can access it and also Hadoop need not be installed on the node on which the HDFS files are required. Also. it's equally fast as the other options mentioned by orangeoctopus.
The best way is install "hadoop-0.20-native" package on the box where you are running your code.
hadoop-0.20-native package can access hdfs filesystem. It can act as a hdfs proxy.
I had similar issue and asked appropriate question. I needed to access HDFS / MapReduce services outside of cluster. After I found solution I posted answer here for HDFS. Most painfull issue there happened to be user authentication which in my case was solved in most simple case (complete code is in my question).
If you need to minimize dependencies and don't want to install hadoop on clients here is nice Cloudera article how to configure Maven to build JAR for this. 100% success for my case.
Main difference in Remote MapReduce job posting comparing to HDFS access is only one configuration setting (check for mapred.job.tracker variable).
Since the zohmg project seems to be dead (no new commits since nov 2009), I would like to know if any of you used/uses it (with successful results). Or if you know anything about future of this project.
And if not, is there any alternative for this project. I'm looking for tool that will help to extract data from (apache) logs (using Hadoop as a batch processing system), store it into HBase, help with querying this data.
Cascading is very often used for this. It also provides adapters for HBase.
Examples can be found here
http://github.com/cwensel/cascading.samples
HBase integration
http://www.cascading.org/modules.html
I need to write data in to Hadoop (HDFS) from external sources like a windows box. Right now I have been copying the data onto the namenode and using HDFS's put command to ingest it into the cluster. In my browsing of the code I didn't see an API for doing this. I am hoping someone can show me that I am wrong and there is an easy way to code external clients against HDFS.
There is an API in Java. You can use it by including the Hadoop code in your project.
The JavaDoc is quite helpful in general, but of course you have to know, what you are looking for *g *
http://hadoop.apache.org/common/docs/
For your particular problem, have a look at:
http://hadoop.apache.org/common/docs/current/api/org/apache/hadoop/fs/FileSystem.html
(this applies to the latest release, consult other JavaDocs for different versions!)
A typical call would be:
Filesystem.get(new JobConf()).create(new Path("however.file"));
Which returns you a stream you can handle with regular JavaIO.
For the problem of loading the data I needed to put into HDFS, I choose to turn the problem around.
Instead of uploading the files to HDFS from the server where they resided, I wrote a Java Map/Reduce job where the mapper read the file from the file server (in this case via https), then write it directly to HDFS (via the Java API).
The list of files is read from the input. I then have an external script that populates a file with the list of files to fetch, uploads the file into HDFS (using hadoop dfs -put), then start the map/reduce job with a decent number of mappers.
This gives me excellent transfer performance, since multiple files are read/written at the same time.
Maybe not the answer you were looking for, but hopefully helpful anyway :-).
About 2 years after my last answer, there are now two new alternatives - Hoop/HttpFS, and WebHDFS.
Regarding Hoop, it was first announced in Cloudera's blog and can be downloaded from a github repository. I have managed to get this version to talk successfully to at least Hadoop 0.20.1, it can probably talk to slightly older versions as well.
If you're running Hadoop 0.23.1 which at time of writing still is not released, Hoop is instead part of Hadoop as its own component, the HttpFS. This work was done as part of HDFS-2178. Hoop/HttpFS can be a proxy not only to HDFS, but also to other Hadoop-compatible filesystems such as Amazon S3.
Hoop/HttpFS runs as its own standalone service.
There's also WebHDFS which runs as part of the NameNode and DataNode services. It also provides a REST API which, if I understand correctly, is compatible with the HttpFS API. WebHDFS is part of Hadoop 1.0 and one of its major features is that it provides data locality - when you're making a read request, you will be redirected to the WebHDFS component on the datanode where the data resides.
Which component to choose depends a bit on your current setup and what needs you have. If you need a HTTP REST interface to HDFS now and you're running a version that does not include WebHDFS, starting with Hoop from the github repository seems like the easiest option. If you are running a version that includes WebHDFS, I would go for that unless you need some of the features Hoop has that WebHDFS lacks (access to other filesystems, bandwidth limitation, etc.)
Install Cygwin, install Hadoop locally (you just need the binary and configs that point at your NN -- no need to actually run the services), run hadoop fs -copyFromLocal /path/to/localfile /hdfs/path/
You can also use the new Cloudera desktop to upload a file via the web UI, though that might not be a good option for giant files.
There's also a WebDAV overlay for HDFS but I don't know how stable/reliable that is.
It seems there is a dedicated page now for this at http://wiki.apache.org/hadoop/MountableHDFS:
These projects (enumerated below) allow HDFS to be mounted (on most
flavors of Unix) as a standard file system using the mount command.
Once mounted, the user can operate on an instance of hdfs using
standard Unix utilities such as 'ls', 'cd', 'cp', 'mkdir', 'find',
'grep', or use standard Posix libraries like open, write, read, close
from C, C++, Python, Ruby, Perl, Java, bash, etc.
Later it describes these projects
contrib/fuse-dfs is built on fuse, some C glue, libhdfs and the hadoop-dev.jar
fuse-j-hdfs is built on fuse, fuse for java, and the hadoop-dev.jar
hdfs-fuse - a google code project is very similar to contrib/fuse-dfs
webdav - hdfs exposed as a webdav resource mapR - contains a closed source hdfs compatible file system that supports read/write
NFS access
HDFS NFS Proxy - exports HDFS as NFS without use of fuse. Supports Kerberos and re-orders writes so they are written to hdfs
sequentially.
I haven't tried any of these, but I will update the answer soon as I have the same need as the OP
You can now also try to use Talend, which includes components for Hadoop integration.
you can try mounting HDFS on your machine(call it machine_X) where you are executing your code and machine_X should have infiniband connectivity with the HDFS Check this out, https://wiki.apache.org/hadoop/MountableHDFS
You can also use HadoopDrive (http://hadoopdrive.effisoft.eu). It's a Windows shell extension.