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 an HDFS master node running the NameNode component of Apache Hadoop 2.7.1, which manages the distribution and replication of data among the various DataNode components.
This charm is intended to be deployed via one of the apache bundles. For example:
juju quickstart apache-analytics-sql
This will deploy the Apache Hadoop platform with Apache Hive available to perform SQL-like queries against your data.
You can also manually load and run map-reduce jobs via the plugin charm included in the bigdata bundles linked above:
juju scp my-job.jar plugin/0:
juju ssh plugin/0
hadoop jar my-job.jar
You can perform a namenode benchmark (nnbench), in order to test your namenode system and configuration
$ juju action do namenode/0 nnbench
Action queued with id: 55887b40-116c-4020-8b35-1e28a54cc622
$ juju action fetch --wait 0 55887b40-116c-4020-8b35-1e28a54cc622
results:
meta:
composite:
direction: asc
units: secs
value: "128"
start: 2016-02-04T14:55:39Z
stop: 2016-02-04T14:57:47Z
results:
raw: '{"BAD_ID": "0", "FILE: Number of read operations": "0", "Reduce input groups":
"8", "Reduce input records": "95", "Map output bytes": "1823", "Map input records":
"12", "Combine input records": "0", "HDFS: Number of bytes read": "18635", "FILE:
Number of bytes written": "32999982", "HDFS: Number of write operations": "330",
"Combine output records": "0", "Total committed heap usage (bytes)": "3144749056",
"Bytes Written": "164", "WRONG_LENGTH": "0", "Failed Shuffles": "0", "FILE:
Number of bytes read": "27879457", "WRONG_MAP": "0", "Spilled Records": "190",
"Merged Map outputs": "72", "HDFS: Number of large read operations": "0", "Reduce
shuffle bytes": "2445", "FILE: Number of large read operations": "0", "Map output
materialized bytes": "2445", "IO_ERROR": "0", "CONNECTION": "0", "HDFS: Number
of read operations": "567", "Map output records": "95", "Reduce output records":
"8", "WRONG_REDUCE": "0", "HDFS: Number of bytes written": "27412", "GC time
elapsed (ms)": "603", "Input split bytes": "1610", "Shuffled Maps ": "72", "FILE:
Number of write operations": "0", "Bytes Read": "1490"}'
status: completed
timing:
completed: 2016-02-04 14:57:48 +0000 UTC
enqueued: 2016-02-04 14:55:14 +0000 UTC
started: 2016-02-04 14:55:27 +0000 UTC
The services 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 that HDFS is working as expected using the built-in smoke-test
action:
juju action do 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>
This charm supports monitoring via Ganglia. To enable monitoring, you must do both of the following (the order does not matter):
- Add a relation to the [Ganglia charm][] via the
:masterrelation - Enable the
ganglia_metricsconfig option
For example:
juju add-relation hdfs-master ganglia:master
juju set hdfs-master ganglia_metrics=true
Enabling monitoring will issue restart the NameNode and all DataNode components on all of the related compute-slaves. Take care to ensure that there are no running jobs when enabling monitoring.
The Apache Hadoop charms can be deployed in environments with limited network access. To deploy in this environment, you will need a local mirror to serve the packages and resources required by these charms.
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.
In addition to apt packages, the Apache Hadoop charms require a few binary
resources, which are normally hosted on Launchpad. If access to Launchpad
is not available, the jujuresources library makes it easy to create a mirror
of these resources:
sudo pip install jujuresources
juju-resources fetch --all /path/to/resources.yaml -d /tmp/resources
juju-resources serve -d /tmp/resources
This will fetch all of the resources needed by this charm and serve them via a
simple HTTP server. The output from juju-resources serve will give you a
URL that you can set as the resources_mirror config option for this charm.
Setting this option will cause all resources required by this charm to be
downloaded from the configured URL.
You can fetch the resources for all of the Apache Hadoop charms
(apache-hadoop-hdfs-master, apache-hadoop-yarn-master,
apache-hadoop-hdfs-secondary, apache-hadoop-plugin, etc) into a single
directory and serve them all with a single juju-resources serve instance.