Hello I am trying to copy a file in my S3 bucket into HDFS using the cp command.
I do something like
Hadoop --config config fs -cp s3a://path hadooppath
This works well when my config is in my local.
However now I am trying to set it up as an oozie job. So when I am now unable to pass the configuration files present in config directory in my local system. Even if its in HDFS, then still it doesn't seem to work. Any suggestions ?
I tried -D command in Hadoop and passed name and value pairs, still it throws some error. It works only from my local system.
Did you Try DISTCP in oozie? Hadoop 2.7.2 will supports S3 data source. You can able to schedule it by coordinators. Just parse the credentials to coordinators either RESTAPI or in Properties files. Its easy way to copy a data periodically(Scheduled manner).
${HADOOP_HOME}/bin/hadoop distcp s3://<source>/ hdfs://<destination>/
Related
May I know how to execute HDFS copy commands on DataProc cluster using airflow.
After the cluster is created using airflow, I have to copy few jar files from Google storage to the HDFS master node folder.
You can execute hdfs commands on dataproc cluster using something like this
gcloud dataproc jobs submit hdfs 'ls /hdfs/path/' --cluster=my-cluster --
region=europe-west1
The easiest way is [1] via
gcloud dataproc jobs submit pig --execute 'fs -ls /'
or otherwise [2] as a catch-all for other shell commands.
For a single small file
You can copy a single file from Google Cloud Storage (GCS) to HDFS using the hdfs copy command. Note that you need to run this from a node within the cluster:
hdfs dfs -cp gs://<bucket>/<object> <hdfs path>
This works because
hdfs://<master node>
is the default filesystem. You can explicitly specify the scheme and NameNode if desired:
hdfs dfs -cp gs://<bucket>/<object> hdfs://<master node>/<hdfs path>
For a large file or large directory of files
When you use hdfs dfs, data is piped through your local machine. If you have a large dataset to copy, you will likely want to do this in parallel on the cluster using DistCp:
hadoop distcp gs://<bucket>/<directory> <HDFS target directory>
Consider [3] for details.
[1] https://pig.apache.org/docs/latest/cmds.html#fs
[2] https://pig.apache.org/docs/latest/cmds.html#sh
[3] https://hadoop.apache.org/docs/current/hadoop-distcp/DistCp.html
I am not sure about your use case to do this via airflow because if its onetime setup then i think we can run commands directly on dataproc cluster. But found some links which might be of some help. As i understand we can use BashOperator and can run commands.
https://big-data-demystified.ninja/2019/11/04/how-to-ssh-to-a-remote-gcp-machine-and-run-a-command-via-airflow/
Airflow Dataproc operator to run shell scripts
Using Apache Hadoop version 2.7.2 and trying to copy files from HDFS to Amazon S3 using below command.
hadoop distcp hdfs://<<namenode_host>>:9000/user/ubuntu/input/flightdata s3a://<<bucketid>>
Getting below exception using above command.
java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: s3a://<<bucketid>>.distcp.tmp.attempt_1462460298670_0004_m_000001_0
Thanks much for the help.
It should be possible to go from HDFS to S3 - I have done it before using syntax like the following, running it from a HDFS cluster:
distcp -Dfs.s3a.access.key=... -Dfs.s3a.secret.key=... /user/vagrant/bigdata s3a://mytestbucket/bigdata
It you run your command like this, does it work:
hadoop distcp hdfs://namenode_host:9000/user/ubuntu/input/flightdata s3a://bucketid/flightdata
From the exception, it looks like it is expecting a 'folder' to put the data in, as opposed to the root of the bucket.
You need to provide AWS credentials in order to successfully transfer files TO/FROM HDFS and S3.
You can pass the access_key_id and secret parameters as shown by #stephen above but you should use a credential provider api for production use where you can manage your credentials without passing them around in individual commands.
Ref: https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/CredentialProviderAPI.html
Secondly you do not need to specify "hdfs" protocol. An absolute hdfs path is sufficient.
I'm trying to execute a spark program in cluster mode in Amazon Ec2 using
spark-submit --master spark://<master-ip>:7077 --deploy-mode cluster --class com.mycompany.SimpleApp ./spark.jar
And the class has a line that tries to read a file:
JavaRDD<String> logData = sc.textFile("/user/input/CHANGES.txt").cache();
I'm unable to read this txt file in cluster mode even if I'm able to read in standalone mode. In cluster mode, it's looking to read from hdfs. So I put the file in hdfs at /root/persistent-hdfs using
hadoop fs -mkdir -p /wordcount/input
hadoop fs -put /app/hadoop/tmp/input.txt /wordcount/input/input.txt
And I can see the file using hadoop fs -ls /workcount/input. But Spark is still unable to read the file. Any idea what I'm doing wrong. Thanks.
You might want to check the following points:
Is the file really in the persistent HDFS?
It seems that you just copy the input file from /app/hadoop/tmp/input.txt to /wordcount/input/input.txt, all in the node disk. I believe you misunderstand the functionality of the hadoop commands.
Instead, you should try putting the file explicitly in the persistent HDFS (root/persistent-hdfs/), and then loading it using the hdfs://... prefix.
Is the persistent HDFS server up?
Please take a look here, it seems Spark only starts the ephemeral HDFS server by default. In order to switch to the persistent HDFS server, you must do the following:
1) Stop the ephemeral HDFS server: /root/ephemeral-hdfs/bin/stop-dfs.sh
2) Start the persistent HDFS server: /root/persistent-hdfs/bin/start-dfs.sh
Please try these things, I hope they can serve you well.
I gave an input to the mapper from a local filesystem.It is running successfully from eclipse,But not running from the cluster as it is unable to find the local input path saying:input path does not exist.Please can anybody help me how to give a local file path to a mapper so that it can run in the cluster and i can get the output in hdfs
This is a very old question. Recently faced the same issue.
I am not aware of how correct this solution is it worked for me though. Please bring to notice if there are any drawbacks of this.Here's what I did.
Reading a solution from the mail-archives, I realised if i modify fs.default.name from hdfs://localhost:8020/ to file:/// it can access the local file system. However, I didnt want this for all my mapreduce jobs. So I made a copy of core-site.xml in a local system folder (same as the one from where I would submit my MR jar to hadoop jar).
and in my Driver class for MR I added,
Configuration conf = new Configuration();
conf.addResource(new Path("/my/local/system/path/to/core-site.xml"));
conf.addResource(new Path("/usr/lib/hadoop-0.20-mapreduce/conf/hdfs-site.xml"));
The MR takes input from local system and writes the output to hdfs:
Running in a cluster requires the data to be loaded into distributed storage (HDFS). Copy the data to HDFS first using hadoop fs -copyFromLocal and then try to trun your job again, giving it the path of the data in HDFS
The question is an interesting one. One can have data on S3 and access this data without an explicit copy to HDFS prior to running the job. In the wordcount example, one would specify this as follows:
hadoop jar example.jar wordcount s3n://bucket/input s3n://bucket/output
What occurs in this is that the mappers read records directly from S3.
If this can be done with S3, why wouldn't hadoop similarly, using this syntax instead of s3n
file:///input file:///output
?
But empirically, this seems to fail in an interesting way -- I see that Hadoop gives a file not found exception for a file that is indeed in the input directory. That is, it seems to be able to list the files in the put directory on my local disk but when it comes time to open them to read the records, the file is not found (or accessible).
The data must be on HDFS for any MapReduce job to process it. So even if you have a source such as local File System or a network path or a web based store (such as Azure Blob Storage or Amazon Block stoage), you would need to copy the data at HDFS first and then run the Job.
The bottom line is that you would need to push the data first to to HDFS and there are several ways depend on data source, you would perform the data transfer from your source to HDFS such as from local file system you would use the following command:
$hadoop -f CopyFromLocal SourceFileOrStoragePath _HDFS__Or_directPathatHDFS_
Try setting the input path like this
FileInputFormat.addInputPath(conf, new Path(file:///the directory on your local filesystem));
if you give the file extension, it can access files from the localsystem
I have tried the following code and got the solution...
Please try it and let me know..
You need to get FileSystem object for local file system and then use makequalified method to return path.. As we need to pass path of local filesystem(no other way to pass this to inputformat), i ve used make qualified, which in deed returns only local file system path..
The code is shown below..
Configuration conf = new Configuration();
FileSystem fs = FileSystem.getLocal(conf);
Path inputPath = fs.makeQualified(new Path("/usr/local/srini/")); // local path
FileInputFormat.setInputPaths(job, inputPath);
I hope this works for your requirement, though it's posted very late.. It worked fine for me.. It does not need any configuration changes i believe..
U might wanna try this by setting the configuration as
Configuration conf=new Configuration();
conf.set("job.mapreduce.tracker","local");
conf.set("fs.default.name","file:///");
After this u can set the fileinputformat with the local path and u r good to go
Suppose I write a java program and i want to run it in Hadoop, then
where should the file be saved?
how to access it from hadoop?
should i be calling it by the following command? hadoop classname
what is the command in hadoop to execute the java file?
The simplest answers I can think of to your questions are:
1) Anywhere
2,3,4)$HADOOP_HOME/bin/hadoop jar [path_to_your_jar_file]
A similar question was asked here Executing helloworld.java in apache hadoop
It may seem complicated, but it's simpler than you might think!
Compile your map/reduce classes, and your main class into a jar. Let's call this jar myjob.jar.
This jar does not need to include the Hadoop libraries, but it should include any other dependencies you have.
Your main method should set up and run your map/reduce job, here is an example.
Put this jar on any machine with the hadoop command line utility installed.
Run your main method using the hadoop command line utility:
hadoop jar myjob.jar
Hope that helps.
where should the file be saved?
The data should be saved in "hdfs". You will want to probably load it into the cluster from your data source using something like Apache Flume. The file can be placed anywhere but most home is /user/hadoop/
how to access it from hadoop?
SSH into the hadoop cluster headnode like a standard linux server.
To list your hadoop root hdfs
hadoop fs -ls /
should i be calling it by the following command? hadoop classname
You should be using the hadoop command to access your data and run your programs, try hadoop help
what is the command in hadoop to execute the java file?
hadoop -jar MyJar.jar com.mycompany.MainDriver arg[0] arg[1] ...