hadoop spark #1

Supports: xenial


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.

Hadoop is designed to scale from a few servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, Hadoop can detect and handle failures at the application layer. This provides a highly-available service on top of a cluster of machines, each of which may be prone to failure.

Spark is a fast and general engine for large-scale data processing.

This bundle provides a complete deployment of Hadoop and Spark components from Apache Bigtop that performs distributed data processing at scale. Ganglia and rsyslog applications are also provided to monitor cluster health and syslog activity.

Bundle Composition

The applications that comprise this bundle are spread across 5 machines as follows:

  • NameNode (HDFS)
  • ResourceManager (YARN)
    • Colocated on the NameNode unit
  • Slave (DataNode and NodeManager)
    • 3 separate units
  • Client (Hadoop endpoint)
  • Spark (Master in yarn-client mode)
    • Colocated on the Client unit
  • Plugin (Facilitates communication with the Hadoop cluster)
    • Colocated on the Client unit
  • Ganglia (Web interface for monitoring cluster metrics)
    • Colocated on the Client unit
  • Rsyslog (Aggregate cluster syslog events in a single location)
    • Colocated on the Client unit

Deploying this bundle results in a fully configured Apache Bigtop cluster on any supported cloud, which can be scaled to meet workload demands.


A working Juju installation is assumed to be present. If Juju is not yet set up, please follow the getting-started instructions prior to deploying this bundle.

Note: This bundle requires hardware resources that may exceed limits of Free-tier or Trial accounts on some clouds. To deploy to these environments, modify a local copy of bundle.yaml with slave: num_units: 1 and machines: 'X': constraints: mem=3G as needed to satisfy account limits.

Deploy this bundle from the Juju charm store with the juju deploy command:

juju deploy hadoop-spark

Alternatively, deploy a locally modified bundle.yaml with:

juju deploy /path/to/bundle.yaml

The charms in this bundle can also be built from their source layers in the Bigtop charm repository. See the Bigtop charm README for instructions on building and deploying these charms locally.

Network-Restricted Environments

Charms can be deployed in environments with limited network access. To deploy in this environment, configure a Juju model with appropriate proxy and/or mirror options. See Configuring Models for more information.



The applications that make up this bundle provide status messages to indicate when they are ready:

juju status

This is particularly useful when combined with watch to track the on-going progress of the deployment:

watch -n 2 juju status

The message for each unit will provide information about that unit's state. Once they all indicate that they are ready, perform application smoke tests to verify that the bundle is working as expected.

Smoke Test

The charms for each core component (namenode, resourcemanager, slave, and spark) provide a smoke-test action that can be used to verify the application is functioning as expected. Note that the 'slave' component runs extensive tests provided by Apache Bigtop and may take up to 30 minutes to complete. Run the smoke-test actions as follows:

juju run-action namenode/0 smoke-test
juju run-action resourcemanager/0 smoke-test
juju run-action slave/0 smoke-test
juju run-action spark/0 smoke-test

Watch the progress of the smoke test actions with:

watch -n 2 juju show-action-status

Eventually, all of the actions should settle to status: completed. If any report status: failed, that application is not working as expected. Get more information about a specific smoke test with:

juju show-action-output <action-id>


Applications in this bundle include Hadoop command line and web utilities that can be used to verify information about the cluster.

From the command line, show the HDFS dfsadmin report and view the current list of YARN NodeManager units with the following:

juju run --application namenode "su hdfs -c 'hdfs dfsadmin -report'"
juju run --application resourcemanager "su yarn -c 'yarn node -list'"

To access the HDFS web console, find the PUBLIC-ADDRESS of the namenode application and expose it:

juju status namenode
juju expose namenode

The web interface will be available at the following URL:


Similarly, to access the Resource Manager web consoles, find the PUBLIC-ADDRESS of the resourcemanager application and expose it:

juju status resourcemanager
juju expose resourcemanager

The YARN and Job History web interfaces will be available at the following URLs:


Finally, to access the Spark web console, find the PUBLIC-ADDRESS of the spark application and expose it:

juju status spark
juju expose spark

The web interface will be available at the following URL:



This bundle includes Ganglia for system-level monitoring of the namenode, resourcemanager, slave, and spark units. Metrics are sent to a centralized ganglia unit for easy viewing in a browser. To view the ganglia web interface, find the PUBLIC-ADDRESS of the Ganglia application and expose it:

juju status ganglia
juju expose ganglia

The web interface will be available at:



This bundle includes rsyslog to collect syslog data from the namenode, resourcemanager, slave, and spark units. These logs are sent to a centralized rsyslog unit for easy syslog analysis of the units that make up the Hadoop cluster. One method of viewing this log data is to simply cat syslog from the rsyslog unit:

juju run --unit rsyslog/0 'sudo cat /var/log/syslog'

Logs may also be forwarded to an external rsyslog processing service. See the Forwarding logs to a system outside of the Juju environment section of the rsyslog README for more information.


The resourcemanager charm in this bundle provide several benchmarks to gauge the performance of the Hadoop cluster. Each benchmark is an action that can be run with juju run-action:

$ juju actions resourcemanager
mrbench     Mapreduce benchmark for small jobs
nnbench     Load test the NameNode hardware and configuration
smoke-test  Run an Apache Bigtop smoke test.
teragen     Generate data with teragen
terasort    Runs teragen to generate sample data, and then runs terasort to sort that data
testdfsio   DFS IO Testing

$ juju run-action resourcemanager/0 nnbench
Action queued with id: 55887b40-116c-4020-8b35-1e28a54cc622

$ juju show-action-output 55887b40-116c-4020-8b35-1e28a54cc622
      direction: asc
      units: secs
      value: "128"
    start: 2016-02-04T14:55:39Z
    stop: 2016-02-04T14:57:47Z
    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
  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 spark charm in this bundle also provides benchmarks to gauge the performance of the Spark cluster. Each benchmark is an action that can be run with juju run-action:

$ juju actions spark
pagerank                          Calculate PageRank for a sample data set
sparkpi                           Calculate Pi

$ juju run-action spark/0 pagerank
Action queued with id: 339cec1f-e903-4ee7-85ca-876fb0c3d28e

$ juju show-action-output 339cec1f-e903-4ee7-85ca-876fb0c3d28e
      direction: asc
      units: secs
      value: "83"
    start: 2017-04-12T23:22:38Z
    stop: 2017-04-12T23:24:01Z
  output: '{''status'': ''completed''}'
status: completed
  completed: 2017-04-12 23:24:02 +0000 UTC
  enqueued: 2017-04-12 23:22:36 +0000 UTC
  started: 2017-04-12 23:22:37 +0000 UTC


By default, three slave units and one unit of each of the other components are deployed with this bundle. To scale the cluster compute and storage capabilities, simply add more slave units. To add one unit:

juju add-unit slave

Multiple units may be added at once. For example, add four more slave units:

juju add-unit -n4 slave

Contact Information


Bundle configuration