The Apache Hadoop S3 connector "S3A" works with Content Gateway S3. Here is a small but complete example, using a single-node Hadoop system that can be easily run on any Docker server. It shows bucket listing, distcp, and a simple mapreduce job against a bucket in a Caringo domain.
Create a container using the docker image from https://github.com/sequenceiq/hadoop-docker.
$ docker run -it sequenceiq/hadoop-docker:2.7.0 /etc/bootstrap.sh -bash
At the container's shell prompt, add the path containing the "hadoop" binary:
PATH=/usr/local/hadoop/bin:$PATH
Copy the jar's needed for the S3A libraries:
cd /usr/local/hadoop-2.7.0/share/hadoop/tools/lib/ && cp -p hadoop-aws-2.7.0.jar aws-java-sdk-1.7.4.jar jackson-core-2.2.3.jar jackson-databind-2.2.3.jar jackson-annotations-2.2.3.jar /usr/local/hadoop-2.7.0/share/hadoop/hdfs/lib/
- Make sure your domain (mydomain.example.com) and bucket (hadoop-test) have been created and that your /etc/hosts or DNS are configured to resolve http://mydomain.example.com/hadoop-test to your cloudgateway server's S3 port.
Create an S3 token:
curl -i -u USERNAME -X POST --data-binary '' -H 'X-User-Secret-Key-Meta: secret' -H 'X-User-Token-Expires-Meta: +90' http://mydomain.example.com/.TOKEN/ HTTP/1.1 201 Created ... Token e181dcb1d01d5cf24f76dd276b95a638 issued for USERNAME in [root] with secret secret
List your bucket (it should be empty).
hadoop fs -Dfs.s3a.access.key=e181dcb1d01d5cf24f76dd276b95a638 -Dfs.s3a.secret.key=secret -Dfs.s3a.endpoint=mydomain.example.com -ls s3a://hadoop-test/
Note: You will get error "
No such file or directory is expected"if the bucket is empty or the trailing slash is missing.Copy the sample "input" files into your bucket:
hadoop distcp -Dfs.s3a.access.key=e181dcb1d01d5cf24f76dd276b95a638 -Dfs.s3a.secret.key=secret -Dfs.s3a.endpoint=jam.cloud.caringo.com input s3a://hadoop-test/input
Verify with "-ls" or in Content Gateway ui that the bucket now has ~31 objects.
hadoop fs -Dfs.s3a.access.key=e181dcb1d01d5cf24f76dd276b95a638 -Dfs.s3a.secret.key=secret -Dfs.s3a.endpoint=jam.cloud.caringo.com -ls s3a://hadoop-test/input/ Found 31 items ...
Run the sample mapreduce job that grep's the input files.
hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.0.jar grep -Dfs.s3a.access.key=e181dcb1d01d5cf24f76dd276b95a638 -Dfs.s3a.secret.key=secret -Dfs.s3a.endpoint=mydomain.example.com s3a://hadoop-test/input s3a://hadoop-test/output 'dfs[a-z.]+'
Display the results in the "output" file.
hadoop fs -Dfs.s3a.access.key=e181dcb1d01d5cf24f76dd276b95a638 -Dfs.s3a.secret.key=secret -Dfs.s3a.endpoint=mydomain.example.com -cat s3a://hadoop-test/output/part-r-00000 6 dfs.audit.logger 4 dfs.class 3 dfs.server.namenode. 2 dfs.period 2 dfs.audit.log.maxfilesize ...
PS: https://wiki.apache.org/hadoop/AmazonS3 makes a good point:
S3 is not a filesystem. The Hadoop S3 filesystem bindings make it pretend to be a filesystem, but it is not. It can act as a source of data, and as a destination -though in the latter case, you must remember that the output may not be immediately visible.
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