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.
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 terasort benchmark, in order to gauge performance of your environment:
$ 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
You can also perform a mapreduce benchmark (mrbench) which relies less on HDFS:
$ juju action do apache-hadoop-resourcemanager/0 mrbench
Action queued with id: b64bc0ea-60af-4f28-8712-a1927926658a
$ juju action fetch --wait 0 b64bc0ea-60af-4f28-8712-a1927926658a
results:
meta:
composite:
direction: asc
units: secs
value: "77"
start: 2016-02-04T14:33:48Z
stop: 2016-02-04T14:35:06Z
results:
raw: '{"Map input records": "4", "Combine input records": "0", "Total time spent
by all maps in occupied slots (ms)": "147048", "Launched reduce tasks": "1",
"Combine output records": "0", "Map output materialized bytes": "84", "FILE:
Number of bytes written": "587281", "Spilled Records": "8", "FILE: Number of
read operations": "0", "Map output records": "4", "Total megabyte-seconds taken
by all map tasks": "150577152", "FILE: Number of bytes read": "32", "HDFS: Number
of write operations": "2", "CPU time spent (ms)": "4790", "HDFS: Number of read
operations": "15", "HDFS: Number of bytes read": "501", "WRONG_MAP": "0", "Input
split bytes": "493", "HDFS: Number of large read operations": "0", "IO_ERROR":
"0", "Reduce input groups": "1", "Bytes Written": "12", "GC time elapsed (ms)":
"2717", "Physical memory (bytes) snapshot": "965840896", "CONNECTION": "0",
"WRONG_LENGTH": "0", "HDFS: Number of bytes written": "12", "Virtual memory
(bytes) snapshot": "9412210688", "Total vcore-seconds taken by all map tasks":
"147048", "Total time spent by all map tasks (ms)": "147048", "Data-local map
tasks": "4", "Bytes Read": "8", "Reduce input records": "4", "FILE: Number of
large read operations": "0", "Total committed heap usage (bytes)": "696057856",
"Shuffled Maps ": "4", "Total megabyte-seconds taken by all reduce tasks": "10531840",
"WRONG_REDUCE": "0", "Failed Shuffles": "0", "Reduce output records": "4", "FILE:
Number of write operations": "0", "Launched map tasks": "4", "Reduce shuffle
bytes": "84", "BAD_ID": "0", "Merged Map outputs": "4", "Total vcore-seconds
taken by all reduce tasks": "10285", "Map output bytes": "20", "Total time spent
by all reduce tasks (ms)": "10285", "Total time spent by all reduces in occupied
slots (ms)": "10285"}'
status: completed
timing:
completed: 2016-02-04 14:35:07 +0000 UTC
enqueued: 2016-02-04 14:33:39 +0000 UTC
started: 2016-02-04 14:33:41 +0000 UTC
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 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.
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.