Hadoop is a software platform that lets one easily write and run applications that process vast amounts of data. This charm manages the HDFS master node (NameNode).

Overview

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.

Usage

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

Benchmarking

This charm provides several benchmarks to gauge the performance of your
environment.

The easiest way to run the benchmarks on this service is to relate it to the
Benchmark GUI. You will likely also want to relate it to the
Benchmark Collector to have machine-level information collected during the
benchmark, for a more complete picture of how the machine performed.

However, each benchmark is also an action that can be called manually:

    $ 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

Status and Smoke Test

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 namenode/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>

Monitoring

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 :master relation
  • Enable the ganglia_metrics config option

For example:

juju add-relation namenode ganglia:master
juju set namenode 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.

Deploying in Network-Restricted Environments

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.

Mirroring 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.

Mirroring Resources

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-namenode, apache-hadoop-resourcemanager,
apache-hadoop-slave, apache-hadoop-plugin, etc) into a single
directory and serve them all with a single juju-resources serve instance.

Contact Information

Hadoop

Configuration

ganglia_metrics
(boolean)
                            Enable metrics using Ganglia, if related to the Ganglia charm via the
ganglia:master relation.  The Hadoop services *will* be restarted if
this option is enabled and the Ganglia service is related.

                        
resources_mirror
(string)
                            URL from which to fetch resources (e.g., Hadoop binaries) instead of Launchpad.

                        
hadoop_version
(string)
                            Hadoop version

                        
2.7.2
journalnode_quorum_size
(int)
                            Minimum number of journalnodes that must be ready before the shared
edits dir will be initialized (aka formatted) on each journalnode.

                        
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