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 a node running the ResourceManager component of Apache Hadoop 2.4.1, which manages the computation resources and job execution for the platform.
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:
juju scp my-job.jar resourcemanager/0:
juju ssh resourcemanager/0
hadoop jar my-job.jar
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 Yarn is working as expected using the built-in smoke-test
action:
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>
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 resourcemanager/0 terasort
Action queued with id: cbd981e8-3400-4c8f-8df1-c39c55a7eae6
$ juju action fetch --wait 0 cbd981e8-3400-4c8f-8df1-c39c55a7eae6
results:
meta:
composite:
direction: asc
units: ms
value: "206676"
results:
raw: '{"Total vcore-seconds taken by all map tasks": "439783", "Spilled Records":
"30000000", "WRONG_LENGTH": "0", "Reduce output records": "10000000", "HDFS:
Number of bytes read": "1000001024", "Total vcore-seconds taken by all reduce
tasks": "50275", "Reduce input groups": "10000000", "Shuffled Maps ": "8", "FILE:
Number of bytes written": "3128977482", "Input split bytes": "1024", "Total
time spent by all reduce tasks (ms)": "50275", "FILE: Number of large read operations":
"0", "Bytes Read": "1000000000", "Virtual memory (bytes) snapshot": "7688794112",
"Launched map tasks": "8", "GC time elapsed (ms)": "11656", "Bytes Written":
"1000000000", "FILE: Number of read operations": "0", "HDFS: Number of write
operations": "2", "Total megabyte-seconds taken by all reduce tasks": "51481600",
"Combine output records": "0", "HDFS: Number of bytes written": "1000000000",
"Total time spent by all map tasks (ms)": "439783", "Map output records": "10000000",
"Physical memory (bytes) snapshot": "2329722880", "FILE: Number of write operations":
"0", "Launched reduce tasks": "1", "Reduce input records": "10000000", "Total
megabyte-seconds taken by all map tasks": "450337792", "WRONG_REDUCE": "0",
"HDFS: Number of read operations": "27", "Reduce shuffle bytes": "1040000048",
"Map input records": "10000000", "Map output materialized bytes": "1040000048",
"CPU time spent (ms)": "195020", "Merged Map outputs": "8", "FILE: Number of
bytes read": "2080000144", "Failed Shuffles": "0", "Total time spent by all
maps in occupied slots (ms)": "439783", "WRONG_MAP": "0", "BAD_ID": "0", "Rack-local
map tasks": "2", "IO_ERROR": "0", "Combine input records": "0", "Map output
bytes": "1020000000", "CONNECTION": "0", "HDFS: Number of large read operations":
"0", "Total committed heap usage (bytes)": "1755840512", "Data-local map tasks":
"6", "Total time spent by all reduces in occupied slots (ms)": "50275"}'
status: completed
timing:
completed: 2015-05-28 20:55:50 +0000 UTC
enqueued: 2015-05-28 20:53:41 +0000 UTC
started: 2015-05-28 20:53:44 +0000 UTC
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 yarn-master ganglia:master
juju set yarn-master ganglia_metrics=true
Enabling monitoring will issue restart the ResourceManager and all NodeManager 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-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.