The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model.
This charm deploys the DataNode component of the Apache Bigtop platform to provide HDFS storage resources.
This charm is intended to be deployed via one of the apache bigtop bundles. For example:
juju quickstart bigtop-processing-mapreduce
This will deploy the Apache Bigtop platform with a workload node preconfigured to work with the cluster.
You can also manually load and run map-reduce jobs via the plugin charm included in the bundles linked above:
juju scp my-job.jar plugin/0:
juju ssh plugin/0
hadoop jar my-job.jar
Apache Bigtop charms provide extended status reporting to indicate when they are ready:
juju status --format=tabular
This is particularly useful when combined with watch to track the on-going
progress of the deployment:
watch -n 0.5 juju status --format=tabular
The message for each unit will provide information about that unit's state.
Once they all indicate that they are ready, you can perform a "smoke test"
to verify HDFS or YARN services are working as expected. Trigger the
smoke-test action by:
juju action do namenode/0 smoke-test
juju action do resourcemanager/0 smoke-test
After a few seconds or so, you can check the results of the smoke test:
juju action status
You will see status: completed if the smoke test was successful, or
status: failed if it was not. You can get more information on why it failed
via:
juju action fetch <action-id>
To scale your cluster storage capabilities, you can simply add more datanode units. For example, to add three more units:
juju add-unit datanode -n 3
Charms can be deployed in environments with limited network access. To deploy in this environment, you will need a local mirror to serve required packages.
You can setup a local mirror for apt packages using squid-deb-proxy. For instructions on configuring juju to use this, see the Juju Proxy Documentation.