Fix HDFS url description, and other various edits
HDFS url description is wrong as a result of code changes. This was the major motivation for this CR. Additional changes * formatted for 80 characters * consistent use of '.' at the end of bullets * added mention of Spark * adding '.sahara' suffix is no longer necessary * some other minor changes Closes-Bug: 1376457 Change-Id: I72134bcdf6c42911d07e65952a9a56331d896699
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@ -1,101 +1,124 @@
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Sahara (Data Processing) UI User Guide
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======================================
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This guide assumes that you already have Sahara service and the Horizon dashboard up and running.
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Don't forget to make sure that Sahara is registered in Keystone.
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If you require assistance with that, please see the `installation guide <../installation.guide.html>`_.
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This guide assumes that you already have the Sahara service and Horizon
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dashboard up and running. Don't forget to make sure that Sahara is registered in
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Keystone. If you require assistance with that, please see the
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`installation guide <../installation.guide.html>`_.
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Launching a cluster via the Sahara UI
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-------------------------------------
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Registering an Image
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--------------------
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1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then click on the "Image Registry" panel.
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1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then
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click on the "Image Registry" panel
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2) From that page, click on the "Register Image" button at the top right.
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2) From that page, click on the "Register Image" button at the top right
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3) Choose the image that you'd like to register as a Hadoop Image
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3) Choose the image that you'd like to register with Sahara
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4) Enter the username of the cloud-init user on the image.
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4) Enter the username of the cloud-init user on the image
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5) Click on the tags that you want to add to the image. (A version ie: 1.2.1 and a type ie: vanilla are required for cluster functionality)
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5) Click on the tags that you want to add to the image. (A version ie: 1.2.1 and
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a type ie: vanilla are required for cluster functionality)
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6) Click the "Done" button to finish the registration.
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6) Click the "Done" button to finish the registration
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Create Node Group Templates
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---------------------------
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1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then click on the "Node Group Templates" panel.
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1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then
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click on the "Node Group Templates" panel
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2) From that page, click on the "Create Template" button at the top right.
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2) From that page, click on the "Create Template" button at the top right
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3) Choose your desired Plugin name and Version from the dropdowns and click "Create".
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3) Choose your desired Plugin name and Version from the dropdowns and click
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"Create"
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4) Give your Node Group Template a name (description is optional)
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5) Choose a flavor for this template (based on your CPU/memory/disk needs)
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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.
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6) Choose the storage location for your instance, this can be either "Ephemeral
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Drive" or "Cinder Volume". If you choose "Cinder Volume", you will need to add
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additional configuration
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7) Choose which processes should be run for any instances that are spawned from this Node Group Template.
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7) Choose which processes should be run for any instances that are spawned from
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this Node Group Template
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8) Click on the "Create" button to finish creating your Node Group Template.
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8) Click on the "Create" button to finish creating your Node Group Template
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Create a Cluster Template
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-------------------------
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1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then click on the "Cluster Templates" panel.
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1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then
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click on the "Cluster Templates" panel
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2) From that page, click on the "Create Template" button at the top right.
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2) From that page, click on the "Create Template" button at the top right
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3) Choose your desired Plugin name and Version from the dropdowns and click "Create".
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3) Choose your desired Plugin name and Version from the dropdowns and click
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"Create"
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4) Under the "Details" tab, you must give your template a name.
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4) Under the "Details" tab, you must give your template a name
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5) Under the "Node Groups" tab, you should add one or more nodes that can be based on one or more templates.
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5) Under the "Node Groups" tab, you should add one or more nodes that can be
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based on one or more templates
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- To do this, start by choosing a Node Group Template from the dropdown and click the "+" button.
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- You can adjust the number of nodes to be spawned for this node group via the text box or the "-" and "+" buttons.
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- Repeat these steps if you need nodes from additional node group templates.
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- To do this, start by choosing a Node Group Template from the dropdown and
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click the "+" button
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- You can adjust the number of nodes to be spawned for this node group via
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the text box or the "-" and "+" buttons
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- Repeat these steps if you need nodes from additional node group templates
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6) Optionally, you can adjust your configuration further by using the "General Parameters", "HDFS Parameters" and "MapReduce Parameters" tabs.
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6) Optionally, you can adjust your configuration further by using the "General
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Parameters", "HDFS Parameters" and "MapReduce Parameters" tabs
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7) Click on the "Create" button to finish creating your Cluster Template.
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7) Click on the "Create" button to finish creating your Cluster Template
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Launching a Cluster
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-------------------
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1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then click on the "Clusters" panel.
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1) Navigate to the "Project" dashboard, then the "Data Processing" tab, then
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click on the "Clusters" panel
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2) Click on the "Launch Cluster" button at the top right.
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2) Click on the "Launch Cluster" button at the top right
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3) Choose your desired Plugin name and Version from the dropdowns and click "Create".
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3) Choose your desired Plugin name and Version from the dropdowns and click
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"Create"
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4) Give your cluster a name. (required)
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4) Give your cluster a name (required)
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5) Choose which cluster template should be used for your cluster.
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5) Choose which cluster template should be used for your cluster
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6) Choose the image that should be used for your cluster (if you do not see any options here, see `Registering an Image`_ above).
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6) Choose the image that should be used for your cluster (if you do not see any
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options here, see `Registering an Image`_ above)
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7) Optionally choose a keypair that can be used to authenticate to your cluster instances.
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7) Optionally choose a keypair that can be used to authenticate to your cluster
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instances
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8) Click on the "Create" button to start your cluster.
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8) Click on the "Create" button to start your cluster
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- Your cluster's status will display on the Clusters table.
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- It will likely take several minutes to reach the "Active" state.
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- Your cluster's status will display on the Clusters table
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- It will likely take several minutes to reach the "Active" state
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Scaling a Cluster
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-----------------
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1) From the Data Processing/Clusters page, click on the "Scale Cluster" button of the row that contains the cluster that you want to scale.
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1) From the Data Processing/Clusters page, click on the "Scale Cluster" button
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of the row that contains the cluster that you want to scale
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2) You can adjust the numbers of instances for existing Node Group Templates.
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2) You can adjust the numbers of instances for existing Node Group Templates
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3) You can also add a new Node Group Template and choose a number of instances to launch.
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3) You can also add a new Node Group Template and choose a number of instances
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to launch
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- This can be done by selecting your desired Node Group Template from the dropdown and clicking the "+" button.
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- Your new Node Group will appear below and you can adjust the number of instances via the text box or the +/- buttons.
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- This can be done by selecting your desired Node Group Template from the
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dropdown and clicking the "+" button
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- Your new Node Group will appear below and you can adjust the number of
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instances via the text box or the "+" and "-" buttons
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4) To confirm the scaling settings and trigger the spawning/deletion of instances, click on "Scale".
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4) To confirm the scaling settings and trigger the spawning/deletion of
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instances, click on "Scale"
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Elastic Data Processing (EDP)
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-----------------------------
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@ -103,113 +126,155 @@ Data Sources
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------------
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Data Sources are where the input and output from your jobs are housed.
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1) From the Data Processing/Data Sources page, click on the "Create Data Source" button at the top right.
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1) From the Data Processing/Data Sources page, click on the "Create Data Source"
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button at the top right
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2) Give your Data Source a name.
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2) Give your Data Source a name
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3) Enter the URL to the Data Source.
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3) Enter the URL of the the Data Source
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- For a Swift object, the url will look like <container>.sahara/<path> (ie: mycontainer.sahara/inputfile). The "swift://" is automatically added for you.
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- For an HDFS object, the url will look like <host>/<path> (ie: myhost/user/hadoop/inputfile). The "hdfs://" is automatically added for you.
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- For a Swift object, enter <container>/<path> (ie: *mycontainer/inputfile*).
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Sahara will prepend *swift://* for you
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- For an HDFS object, enter an absolute path, a relative path or a full URL:
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4) Enter the username and password for the Data Source.
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+ */my/absolute/path* indicates an absolute path in the cluster HDFS
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+ *my/path* indicates the path */user/hadoop/my/path* in the cluster HDFS
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assuming the defined HDFS user is *hadoop*
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+ *hdfs://host:port/path* can be used to indicate any HDFS location
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5) Enter an optional description.
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4) Enter the username and password for the Data Source (also see
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`Additional Notes`_)
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6) Click on "Create".
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5) Enter an optional description
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7) Repeat for additional Data Sources.
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6) Click on "Create"
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7) Repeat for additional Data Sources
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Job Binaries
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------------
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Job Binaries are where you define/upload the source code (mains and libraries) for your job.
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Job Binaries are where you define/upload the source code (mains and libraries)
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for your job.
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1) From the Data Processing/Job Binaries page, click on the "Create Job Binary" button at the top right.
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1) From the Data Processing/Job Binaries page, click on the "Create Job Binary"
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button at the top right
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2) Give your Job Binary a name (this can be different than the actual filename).
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2) Give your Job Binary a name (this can be different than the actual filename)
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3) Choose the type of storage for your Job Binary.
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3) Choose the type of storage for your Job Binary
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- For "Swift", you will need to enter the URL of your binary (<container>.sahara/<path>) as well as the username and password.
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- For "Internal database", you can choose from "Create a script" or "Upload a new file".
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- For "Swift", enter the URL of your binary (<container>/<path>) as well as
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the username and password (also see `Additional Notes`_)
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- For "Internal database", you can choose from "Create a script" or "Upload
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a new file"
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4) Enter an optional description.
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4) Enter an optional description
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5) Click on "Create".
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5) Click on "Create"
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6) Repeat for additional Job Binaries
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Jobs
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----
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Jobs are where you define the type of job you'd like to run as well as which "Job Binaries" are required.
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Jobs are where you define the type of job you'd like to run as well as which
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"Job Binaries" are required
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1) From the Data Processing/Jobs page, click on the "Create Job" button at the top right.
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1) From the Data Processing/Jobs page, click on the "Create Job" button at the
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top right
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2) Give your Job a name.
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2) Give your Job a name
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3) Choose the type of job you'd like to run (Pig, Hive, MapReduce, Streaming MapReduce, Java Action)
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3) Choose the type of job you'd like to run
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4) Choose the main binary from the dropdown (not applicable for MapReduce or Java Action).
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4) Choose the main binary from the dropdown
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5) Enter an optional description for your Job.
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- This is required for Hive, Pig, and Spark jobs
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- Other job types do not use a main binary
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6) Optionally, click on the "Libs" tab and add one or more libraries that are required for your job. Each library must be defined as a Job Binary.
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5) Enter an optional description for your Job
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7) Click on "Create".
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6) Click on the "Libs" tab and choose any libraries needed by your job
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- MapReduce and Java jobs require at least one library
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- Other job types may optionally use libraries
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7) Click on "Create"
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Job Executions
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--------------
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Job Executions are what you get by "Launching" a job. You can monitor the status of your job to see when it has completed its run.
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Job Executions are what you get by "Launching" a job. You can monitor the
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status of your job to see when it has completed its run
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1) From the Data Processing/Jobs page, find the row that contains the job you want to launch and click on the "Launch Job" button at the right side of that row.
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1) From the Data Processing/Jobs page, find the row that contains the job you
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want to launch and click on the "Launch Job" button at the right side of that
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row
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2) Choose the cluster (already running--see `Launching a Cluster`_ above) on which you would like the job to run.
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2) Choose the cluster (already running--see `Launching a Cluster`_ above) on
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which you would like the job to run
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3) Choose the Input and Output Data Sources (Data Sources defined above).
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3) Choose the Input and Output Data Sources (Data Sources defined above)
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4) If additional configuration is required, click on the "Configure" tab.
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4) If additional configuration is required, click on the "Configure" tab
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- Additional configuration properties can be defined by clicking on the "Add" button.
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- An example configuration entry might be mapred.mapper.class for the Name and org.apache.oozie.example.SampleMapper for the Value.
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- Additional configuration properties can be defined by clicking on the "Add"
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button
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- An example configuration entry might be mapred.mapper.class for the Name and
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org.apache.oozie.example.SampleMapper for the Value
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5) Click on "Launch". To monitor the status of your job, you can navigate to the Sahara/Job Executions panel.
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5) Click on "Launch". To monitor the status of your job, you can navigate to
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the Sahara/Job Executions panel
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6) You can relaunch a Job Execution from the Job Executions page by using the "Relaunch on New Cluster" or "Relaunch on Existing Cluster" links.
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6) You can relaunch a Job Execution from the Job Executions page by using the
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"Relaunch on New Cluster" or "Relaunch on Existing Cluster" links
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- 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.
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- 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.
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- Relaunch on New Cluster will take you through the forms to start a new
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cluster before letting you specify input/output Data Sources and job
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configuration
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- Relaunch on Existing Cluster will prompt you for input/output Data Sources
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as well as allow you to change job configuration before launching the job
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Example Jobs
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------------
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There are sample jobs located in the sahara repository. The instructions there guide you through running the jobs via the command line.
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In this section, we will give a walkthrough on how to run those jobs via the Horizon UI.
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These steps assume that you already have a cluster up and running (in the "Active" state).
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There are sample jobs located in the sahara repository. In this section, we
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will give a walkthrough on how to run those jobs via the Horizon UI. These steps
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assume that you already have a cluster up and running (in the "Active" state).
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1) Sample Pig job - https://github.com/openstack/sahara/tree/master/etc/edp-examples/pig-job
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1) Sample Pig job -
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https://github.com/openstack/sahara/tree/master/etc/edp-examples/pig-job
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- Load the input data file from https://github.com/openstack/sahara/tree/master/etc/edp-examples/pig-job/data/input into swift
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- Load the input data file from
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https://github.com/openstack/sahara/tree/master/etc/edp-examples/pig-job/data/input
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into swift
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- Click on Projet/Object Store/Containers and create a container with any name ("samplecontainer" for our purposes here).
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- Click on Projet/Object Store/Containers and create a container with any
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name ("samplecontainer" for our purposes here)
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- Click on Upload Object and give the object a name ("piginput" in this case)
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- Click on Upload Object and give the object a name
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("piginput" in this case)
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- Navigate to Data Processing/Data Sources, Click on Create Data Source.
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- Navigate to Data Processing/Data Sources, Click on Create Data Source
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- Name your Data Source ("pig-input-ds" in this sample)
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- Type = Swift, URL samplecontainer.sahara/piginput, fill-in the Source username/password fields with your username/password and click "Create"
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- Type = Swift, URL samplecontainer/piginput, fill-in the Source
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username/password fields with your username/password and click "Create"
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- Create another Data Source to use as output for the job
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- Create another Data Source to use as output for our job. Name = pig-output-ds, Type = Swift, URL = samplecontainer.sahara/pigoutput, Source username/password, "Create"
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- Name = pig-output-ds, Type = Swift, URL = samplecontainer/pigoutput,
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Source username/password, "Create"
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- Store your Job Binaries in the Sahara database
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- Navigate to Data Processing/Job Binaries, Click on Create Job Binary
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- Name = example.pig, Storage type = Internal database, click Browse and find example.pig wherever you checked out the sahara project <sahara root>/etc/edp-examples/pig-job
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- Name = example.pig, Storage type = Internal database, click Browse and
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find example.pig wherever you checked out the sahara project
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<sahara root>/etc/edp-examples/pig-job
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- 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 root>/etc/edp-examples/pig-job
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- Create another Job Binary: Name = udf.jar, Storage type = Internal
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database, click Browse and find udf.jar wherever you checked out the
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sahara project <sahara root>/etc/edp-examples/pig-job
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- Create a Job
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@ -217,59 +282,83 @@ These steps assume that you already have a cluster up and running (in the "Activ
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- Name = pigsample, Job Type = Pig, Choose "example.pig" as the main binary
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- Click on the "Libs" tab and choose "udf.jar", then hit the "Choose" button beneath the dropdown, then click on "Create"
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- Click on the "Libs" tab and choose "udf.jar", then hit the "Choose" button
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beneath the dropdown, then click on "Create"
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- Launch your job
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- To launch your job from the Jobs page, click on the down arrow at the far right of the screen and choose "Launch on Existing Cluster"
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- To launch your job from the Jobs page, click on the down arrow at the far
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right of the screen and choose "Launch on Existing Cluster"
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- 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.
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- For the input, choose "pig-input-ds", for output choose "pig-output-ds".
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Also choose whichever cluster you'd like to run the job on
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- For this job, no additional configuration is necessary, so you can just click on "Launch"
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- For this job, no additional configuration is necessary, so you can just
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click on "Launch"
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- You will be taken to the "Job Executions" page where you can see your job progress through "PENDING, RUNNING, SUCCEEDED" phases
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- You will be taken to the "Job Executions" page where you can see your job
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progress through "PENDING, RUNNING, SUCCEEDED" phases
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||||
- 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.
|
||||
- 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/tree/master/etc/edp-examples/edp-spark
|
||||
2) Sample Spark job -
|
||||
https://github.com/openstack/sahara/tree/master/etc/edp-examples/edp-spark
|
||||
|
||||
- Store the Job Binary in the Sahara database
|
||||
|
||||
- Navigate to Data Processing/Job Binaries, Click on Create Job Binary
|
||||
|
||||
- Name = sparkexample.jar, Storage type = Internal database, Browse to the location <sahara root>/etc/edp-examples/edp-spark and choose spark-example.jar, Click "Create"
|
||||
- Name = sparkexample.jar, Storage type = Internal database, Browse to the
|
||||
location <sahara root>/etc/edp-examples/edp-spark and choose
|
||||
spark-example.jar, Click "Create"
|
||||
|
||||
- Create a Job
|
||||
|
||||
- Name = sparkexamplejob, Job Type = Spark, Main binary = Choose sparkexample.jar, Click "Create"
|
||||
- Name = sparkexamplejob, Job Type = Spark,
|
||||
Main binary = Choose sparkexample.jar, Click "Create"
|
||||
|
||||
- Launch your job
|
||||
|
||||
- To launch your job from the Jobs page, click on the down arrow at the far right of the screen and choose "Launch on Existing Cluster"
|
||||
- To launch your job from the Jobs 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.
|
||||
- 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
|
||||
- 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 "Job Executions" page where you can see your job progress through "PENDING, RUNNING, SUCCEEDED" phases
|
||||
- You will be taken to the "Job Executions" 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.
|
||||
- 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>/<job execution id>. You can do ``cat stdout`` which should display something like "Pi is roughly 3.14156132"
|
||||
- The output is located at /tmp/spark-edp/<name of job>/<job execution 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.
|
||||
- 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 that has an existing Job Execution. In order to be able to delete that job, you would first need to delete any Job Executions that relate to that job.
|
||||
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 that has an existing Job
|
||||
Execution. In order to be able to delete that job, you would first need to
|
||||
delete any Job Executions 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>`
|
||||
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>`
|
||||
|
Loading…
Reference in New Issue
Block a user