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deb-sahara/doc/source/horizon/dashboard.user.guide.rst
Nikita Konovalov e6ff7d17ea Updated UI docs 
The UI docs now describe sahara-dashboard being a
horizon plugin.

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2016-03-16 16:40:49 +03:00

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Sahara (Data Processing) UI User Guide
======================================
This guide assumes that you already have the sahara service and Horizon
dashboard up and running. Don't forget to make sure that sahara is
registered in Keystone. If you require assistance with that, please see the
`installation guide <../userdoc/installation.guide.html>`_.
The sections below give a panel by panel overview of setting up clusters
and running jobs. For a description of using the guided cluster and job tools,
look at `Launching a cluster via the Cluster Creation Guide`_ and
`Running a job via the Job Execution Guide`_.
Launching a cluster via the sahara UI
-------------------------------------
Registering an Image
--------------------
1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then
click on the "Clusters" panel and finally the "Image Registry" tab.
2) From that page, click on the "Register Image" button at the top right
3) Choose the image that you'd like to register with sahara
4) Enter the username of the cloud-init user on the image
5) Choose plugin and version to make the image available only for the intended
clusters
6) Click the "Done" button to finish the registration
Create Node Group Templates
---------------------------
1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then
click on the "Clusters" panel and then the "Node Group Templates" tab.
2) From that page, click on the "Create Template" button at the top right
3) Choose your desired Plugin name and Version from the dropdowns and click
"Create"
4) Give your Node Group Template a name (description is optional)
5) Choose a flavor for this template (based on your CPU/memory/disk needs)
6) Choose the storage location for your instance, this can be either "Ephemeral
Drive" or "Cinder Volume". If you choose "Cinder Volume", you will need to
add additional configuration
7) Switch to the Node processes tab and choose which processes should be run
for all instances that are spawned from this Node Group Template
8) Click on the "Create" button to finish creating your Node Group Template
Create a Cluster Template
-------------------------
1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then
click on the "Clusters" panel and finally the "Cluster Templates" tab.
2) From that page, click on the "Create Template" button at the top right
3) Choose your desired Plugin name and Version from the dropdowns and click
"Create"
4) Under the "Details" tab, you must give your template a name
5) Under the "Node Groups" tab, you should add one or more nodes that can be
based on one or more templates
- To do this, start by choosing a Node Group Template from the dropdown and
click the "+" button
- You can adjust the number of nodes to be spawned for this node group via
the text box or the "-" and "+" buttons
- Repeat these steps if you need nodes from additional node group templates
6) Optionally, you can adjust your configuration further by using the "General
Parameters", "HDFS Parameters" and "MapReduce Parameters" tabs
7) Click on the "Create" button to finish creating your Cluster Template
Launching a Cluster
-------------------
1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then
click on the "Clusters" panel and lastly, click on the "Clusters" tab.
2) Click on the "Launch Cluster" button at the top right
3) Choose your desired Plugin name and Version from the dropdowns and click
"Create"
4) Give your cluster a name (required)
5) Choose which cluster template should be used for your cluster
6) Choose the image that should be used for your cluster (if you do not see any
options here, see `Registering an Image`_ above)
7) Optionally choose a keypair that can be used to authenticate to your cluster
instances
8) Click on the "Create" button to start your cluster
- Your cluster's status will display on the Clusters table
- It will likely take several minutes to reach the "Active" state
Scaling a Cluster
-----------------
1) From the Data Processing/Clusters page (Clusters tab), click on the
"Scale Cluster" button of the row that contains the cluster that you want to
scale
2) You can adjust the numbers of instances for existing Node Group Templates
3) You can also add a new Node Group Template and choose a number of instances
to launch
- This can be done by selecting your desired Node Group Template from the
dropdown and clicking the "+" button
- Your new Node Group will appear below and you can adjust the number of
instances via the text box or the "+" and "-" buttons
4) To confirm the scaling settings and trigger the spawning/deletion of
instances, click on "Scale"
Elastic Data Processing (EDP)
-----------------------------
Data Sources
------------
Data Sources are where the input and output from your jobs are housed.
1) From the Data Processing/Jobs page (Data Sources tab), click on the
"Create Data Source" button at the top right
2) Give your Data Source a name
3) Enter the URL of the Data Source
- For a swift object, enter <container>/<path> (ie: *mycontainer/inputfile*).
sahara will prepend *swift://* for you
- For an HDFS object, enter an absolute path, a relative path or a full URL:
+ */my/absolute/path* indicates an absolute path in the cluster HDFS
+ *my/path* indicates the path */user/hadoop/my/path* in the cluster HDFS
assuming the defined HDFS user is *hadoop*
+ *hdfs://host:port/path* can be used to indicate any HDFS location
4) Enter the username and password for the Data Source (also see
`Additional Notes`_)
5) Enter an optional description
6) Click on "Create"
7) Repeat for additional Data Sources
Job Binaries
------------
Job Binaries are where you define/upload the source code (mains and libraries)
for your job.
1) From the Data Processing/Jobs (Job Binaries tab), click on the
"Create Job Binary" button at the top right
2) Give your Job Binary a name (this can be different than the actual filename)
3) Choose the type of storage for your Job Binary
- For "swift", enter the URL of your binary (<container>/<path>) as well as
the username and password (also see `Additional Notes`_)
- For "Internal database", you can choose from "Create a script" or "Upload
a new file"
4) Enter an optional description
5) Click on "Create"
6) Repeat for additional Job Binaries
Job Templates (Known as "Jobs" in the API)
------------------------------------------
Job templates are where you define the type of job you'd like to run as well
as which "Job Binaries" are required.
1) From the Data Processing/Jobs page (Job Templates tab),
click on the "Create Job Template" button at the top right
2) Give your Job Template a name
3) Choose the type of job you'd like to run
4) Choose the main binary from the dropdown
- This is required for Hive, Pig, and Spark jobs
- Other job types do not use a main binary
5) Enter an optional description for your Job Template
6) Click on the "Libs" tab and choose any libraries needed by your job template
- MapReduce and Java jobs require at least one library
- Other job types may optionally use libraries
7) Click on "Create"
Jobs (Known as "Job Executions" in the API)
-------------------------------------------
Jobs are what you get by "Launching" a job template. You can monitor the
status of your job to see when it has completed its run
1) From the Data Processing/Jobs page (Job Templates tab), find the row
that contains the job template you want to launch and click either
"Launch on New Cluster" or "Launch on Existing Cluster" the right side
of that row
2) Choose the cluster (already running--see `Launching a Cluster`_ above) on
which you would like the job to run
3) Choose the Input and Output Data Sources (Data Sources defined above)
4) If additional configuration is required, click on the "Configure" tab
- Additional configuration properties can be defined by clicking on the "Add"
button
- An example configuration entry might be mapred.mapper.class for the Name
and org.apache.oozie.example.SampleMapper for the Value
5) Click on "Launch". To monitor the status of your job, you can navigate to
the Data Processing/Jobs panel and click on the Jobs tab.
6) You can relaunch a Job from the Jobs page by using the
"Relaunch on New Cluster" or "Relaunch on Existing Cluster" links
- Relaunch on New Cluster will take you through the forms to start a new
cluster before letting you specify input/output Data Sources and job
configuration
- Relaunch on Existing Cluster will prompt you for input/output Data Sources
as well as allow you to change job configuration before launching the job
Example Jobs
------------
There are sample jobs located in the sahara repository. In this section, we
will give a walkthrough on how to run those jobs via the Horizon UI. These
steps assume that you already have a cluster up and running (in the "Active"
state). You may want to clone into https://github.com/openstack/sahara-tests
so that you will have all of the source code and inputs stored locally.
1) Sample Pig job -
https://github.com/openstack/sahara-tests/tree/master/etc/edp-examples/edp-pig/trim-spaces
- Load the input data file from
https://github.com/openstack/sahara-tests/blob/master/etc/edp-examples/edp-pig/trim-spaces/data/input
into swift
- Click on Project/Object Store/Containers and create a container with any
name ("samplecontainer" for our purposes here)
- Click on Upload Object and give the object a name
("piginput" in this case)
- Navigate to Data Processing/Jobs/Data Sources, Click on Create Data Source
- Name your Data Source ("pig-input-ds" in this sample)
- Type = Swift, URL samplecontainer/piginput, fill-in the Source
username/password fields with your username/password and click "Create"
- Create another Data Source to use as output for the job
- Name = pig-output-ds, Type = Swift, URL = samplecontainer/pigoutput,
Source username/password, "Create"
- Store your Job Binaries in the sahara database
- Navigate to Data Processing/Jobs/Job Binaries, Click on Create Job Binary
- Name = example.pig, Storage type = Internal database, click Browse and
find example.pig wherever you checked out the sahara project
<sahara-tests root>/etc/edp-examples/edp-pig/trim-spaces
- Create another Job Binary: Name = udf.jar, Storage type = Internal
database, click Browse and find udf.jar wherever you checked out the
sahara project <sahara-tests root>/etc/edp-examples/edp-pig/trim-spaces
- Create a Job Template
- Navigate to Data Processing/Jobs/Job Templates, Click on
Create Job Template
- Name = pigsample, Job Type = Pig, Choose "example.pig" as the main binary
- Click on the "Libs" tab and choose "udf.jar", then hit the "Choose"
button beneath the dropdown, then click on "Create"
- Launch your job
- To launch your job from the Job Templates page, click on the down
arrow at the far right of the screen and choose
"Launch on Existing Cluster"
- For the input, choose "pig-input-ds", for output choose "pig-output-ds".
Also choose whichever cluster you'd like to run the job on
- For this job, no additional configuration is necessary, so you can just
click on "Launch"
- You will be taken to the "Jobs" page where you can see your job
progress through "PENDING, RUNNING, SUCCEEDED" phases
- When your job finishes with "SUCCEEDED", you can navigate back to Object
Store/Containers and browse to the samplecontainer to see your output.
It should be in the "pigoutput" folder
2) Sample Spark job -
https://github.com/openstack/sahara-tests/tree/master/etc/edp-examples/edp-spark
You can clone into https://github.com/openstack/sahara-tests for quicker
access to the files for this sample job.
- Store the Job Binary in the sahara database
- Navigate to Data Processing/Jobs/Job Binaries, Click on Create Job Binary
- Name = sparkexample.jar, Storage type = Internal database, Browse to the
location <sahara-tests root>/etc/edp-examples/edp-spark and choose
spark-example.jar, Click "Create"
- Create a Job Template
- Name = sparkexamplejob, Job Type = Spark,
Main binary = Choose sparkexample.jar, Click "Create"
- Launch your job
- To launch your job from the Job Templates page, click on the
down arrow at the far right of the screen and choose
"Launch on Existing Cluster"
- Choose whichever cluster you'd like to run the job on
- Click on the "Configure" tab
- Set the main class to be: org.apache.spark.examples.SparkPi
- Under Arguments, click Add and fill in the number of "Slices" you want to
use for the job. For this example, let's use 100 as the value
- Click on Launch
- You will be taken to the "Jobs" page where you can see your job
progress through "PENDING, RUNNING, SUCCEEDED" phases
- When your job finishes with "SUCCEEDED", you can see your results by
sshing to the Spark "master" node
- The output is located at /tmp/spark-edp/<name of job template>/<job id>.
You can do ``cat stdout`` which should display something like
"Pi is roughly 3.14156132"
- It should be noted that for more complex jobs, the input/output may be
elsewhere. This particular job just writes to stdout, which is logged in
the folder under /tmp
Additional Notes
----------------
1) Throughout the sahara UI, you will find that if you try to delete an object
that you will not be able to delete it if another object depends on it.
An example of this would be trying to delete a Job Template that has an
existing Job. In order to be able to delete that job, you would
first need to delete any Job Templates that relate to that job.
2) In the examples above, we mention adding your username/password for the
swift Data Sources. It should be noted that it is possible to configure
sahara such that the username/password credentials are *not* required. For
more information on that, please refer to: :doc:`Sahara Advanced
Configuration Guide <../userdoc/advanced.configuration.guide>`
Launching a cluster via the Cluster Creation Guide
--------------------------------------------------
1) Under the Data Processing group, choose "Clusters" and then click on the
"Clusters" tab. The "Cluster Creation Guide" button is above that table.
Click on it.
2) Click on the "Choose Plugin" button then select the cluster type from the
Plugin Name dropdown and choose your target version. When done, click
on "Select" to proceed.
3) Click on "Create a Master Node Group Template". Give your template a name,
choose a flavor and choose which processes should run on nodes launched
for this node group. The processes chosen here should be things that are
more server-like in nature (namenode, oozieserver, spark master, etc).
Optionally, you can set other options here such as availability zone,
storage, security and process specific parameters. Click on "Create"
to proceed.
4) Click on "Create a Worker Node Group Template". Give your template a name,
choose a flavor and choose which processes should run on nodes launched
for this node group. Processes chosen here should be more worker-like in
nature (datanode, spark slave, task tracker, etc). Optionally, you can set
other options here such as availability zone, storage, security and process
specific parameters. Click on "Create" to proceed.
5) Click on "Create a Cluster Template". Give your template a name. Next,
click on the "Node Groups" tab and enter the count for each of the node
groups (these are pre-populated from steps 3 and 4). It would be common
to have 1 for the "master" node group type and some larger number of
"worker" instances depending on you desired cluster size. Optionally,
you can also set additional parameters for cluster-wide settings via
the other tabs on this page. Click on "Create" to proceed.
6) Click on "Launch a Cluster". Give your cluster a name and choose the image
that you want to use for all instances in your cluster. The cluster
template that you created in step 5 is already pre-populated. If you want
ssh access to the instances of your cluster, select a keypair from the
dropdown. Click on "Launch" to proceed. You will be taken to the Clusters
panel where you can see your cluster progress toward the Active state.
Running a job via the Job Execution Guide
-----------------------------------------
1) Under the Data Processing group, choose "Jobs" and then click on the
"Jobs" tab. The "Job Execution Guide" button is above that table. Click
on it.
2) Click on "Select type" and choose the type of job that you want to run.
3) If your job requires input/output data sources, you will have the option
to create them via the "Create a Data Source" button (Note: This button will
not be shown for job types that do not require data sources). Give your
data source a name and choose the type. If you have chosen swift, you
may also enter the username and password. Enter the URL for your data
source. For more details on what the URL should look like, see
`Data Sources`_.
4) Click on "Create a job template". Give your job template a name.
Depending on the type of job that you've chosen, you may need to select
your main binary and/or additional libraries (available from the "Libs"
tab). If you have not yet uploaded the files to run your program, you
can add them via the "+" icon next to the "Choose a main binary" select box.
5) Click on "Launch job". Choose the active cluster where you want to run you
job. Optionally, you can click on the "Configure" tab and provide any
required configuration, arguments or parameters for your job. Click on
"Launch" to execute your job. You will be taken to the Jobs tab where
you can monitor the state of your job as it progresses.
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