| SETTINGS | Override the Hive and Hadoop default settings. To configure a new
setting:
- Click Add.
- For Key, enter a Hive or Hadoop configuration variable name.
- For Value, enter the value you want to use for the variable.
For example, to override the directory where structured Hive query logs
are created, you would enter hive.querylog.location for Key, and a
path for Value.
To view the default settings, click the Settings tab at the top of
the page. For information about Hive configuration variables, see:
[http://wiki.apache.org/hadoop/Hive/AdminManual/Configuration](http://wiki.apache.org/hadoop/Hive/AdminManual/Configuration).
For information about Hadoop configuration variables, see:
[http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml](http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml).
| FILE RESOURCES | Make files locally accessible at query execution time available on the
Hadoop cluster. Hive uses the Hadoop Distributed Cache to distribute the
added files to all machines in the cluster at query execution time.
- Click Add to configure a new setting.
- From the Type drop-down menu, choose one of the following:
- jar - Adds the specified resources to the Java classpath.
- archive - Unarchives the specified resources when
distributing them.
- file - Adds the specified resources to the distributed
cache. Typically, this might be a transform script (or similar)
to be executed.
- For Path, enter the path to the file or click
 to browse and select the file.
 Note: It is not necessary to specify files
used in a transform script if the files are available in the same path
on all machines in the Hadoop cluster.
| USER-DEFINED FUNCTIONS | Specify user-defined functions. Click Add to configure a new
setting. Specify the function name in the Name field, and specify
the class name for Classname.
You *must* specify a JAR file for the user-defined functions in FILE RESOURCES.
To include a user-defined function in a query, add a $ (dollar sign)
before the function name in the query. For example, if MyTable is a
user-defined function name in the query, you would type: SELECT $MyTable
|
| PARAMETERIZATION | Indicate that a dialog box should display to enter parameter values when
a query containing the string $parametername is executed. Enabled by
default. |
## Autocomplete
## Syntax checker
## Query Assist
## Risk Alerts
## Presentation
### Jobs
The Editor application enables you to create and submit jobs to
the cluster. You can include variables with your jobs to enable
you and other users to enter values for the variables when they run your
job.
Most job design types support all the settings listed in the following
table. For job type specific settings, see:
[MapReduce](#mapreduce),
[Streaming](#streaming),
[Java](#java),
[Pig](#pig),
[Hive](#hive),
[Sqoop](#sqoop),
[Shell](#shell),
[Ssh](#ssh),
[DistCp](#distcp),
[Fs](#fs), and
[Email](#email).
All job design settings except Name and Description support the use of
variables of the form $variable\_name. When you run the job, a dialog
box will appear to enable you to specify the values of the variables.
| Name | Identifies the job and its collection of properties and parameters. |
| Description | A description of the job. The description is displayed in the dialog box
that appears if you specify variables for the job. |
| Advanced | Advanced settings:- Is shared- Indicate whether to share the action with all users.
- Oozie parameters - parameters to pass to Oozie
| Prepare | Specifies paths to create or delete before starting the workflow job. |
| Params | Parameters to pass to a script or command. The parameters are expressed
using the [JSP 2.0 Specification (JSP.2.3) Expression
Language](http://jcp.org/aboutJava/communityprocess/final/jsr152/),
allowing variables, functions, and complex expressions as parameters. |
| Job Properties | Job properties. To set a property value, click Add Property.- Property name - a configuration property name. This field provides autocompletion, so you can type the first few characters of a property name and then select the one you want from the drop-down
list.
- Valuethe property value.
| Files | Files to pass to the job. Equivalent to the Hadoop -files option. |
| Archives | Files to pass to the job. Archives to pass to the job. Equivalent to the Hadoop -archives option. |
#### MapReduce Job Design
A MapReduce job design consists of MapReduce functions written in Java.
You can create a MapReduce job design from existing mapper and reducer
classes without having to write a main Java class. You must specify the
mapper and reducer classes as well as other MapReduce properties in the
Job Properties setting.
| Jar path | The fully-qualified path to a JAR file containing the classes that
implement the Mapper and Reducer functions. |
#### Streaming Job Design
Hadoop streaming jobs enable you to create MapReduce functions in any
non-Java language that reads standard Unix input and writes standard
Unix output. For more information about Hadoop streaming jobs, see
[Hadoop
Streaming](http://archive.cloudera.com/cdh/3/hadoop-0.20.2+320/streaming.html).
| Mapper | The path to the mapper script or class. If the mapper file is not on the
machines on the cluster, use the Files option to pass it as a part
of job submission. Equivalent to the Hadoop -mapper option. |
| Reducer | The path to the reducer script or class. If the reducer file is not on
the machines on the cluster, use the Files option to pass it as a
part of job submission. Equivalent to the Hadoop -reducer option. |
#### Java Job Design
A Java job design consists of a main class written in Java.
| Jar path | The fully-qualified path to a JAR file containing the main class. |
| Main class | The main class to invoke the program. |
| Args | The arguments to pass to the main class. |
| Java opts | The options to pass to the JVM. |
#### Pig Job Design
A Pig job design consists of a Pig script.
| Script name | Script name or path to the Pig script. |
#### Hive Job Design
A Hive job design consists of a Hive script.
| Script name | Script name or path to the Hive script. |
#### Sqoop Job Design
A Sqoop job design consists of a Sqoop command.
| Command | The Sqoop command. |
#### Shell Job Design
A Shell job design consists of a shell command.
| Command | The shell command. |
| Capture outputIndicate whether to capture the output of the command. |
#### Ssh Job Design
A Ssh job design consists of an ssh command.
| User | The name of the user to run the command as. |
| Host | The name of the host to run the command on. |
| Command | The ssh command. |
| Capture outputIndicate whether to capture the output of the command. |
#### DistCp Job Design
A DistCp job design consists of a DistCp command.
#### Fs Job Design
A Fs job design consists of a command that operates on HDFS.
| Delete path | The path to delete. If it is a directory, it deletes recursively all its
content and then deletes the directory. |
| Create directoryThe path of a directory to create. |
| Move file | The source and destination paths to the file to be moved. |
| Change permissions | The path whose permissions are to be changed, the permissions, and an
indicator of whether to change permission recursively. |
#### Email Job Design
A Email job design consists of an email message.
| To addresses | The recipient of the email message. |
| CC addresses (optional) | The cc recipients of the email message. |
| Subject | The subject of the email message. |
| Body | The body of the email message. |
### Spark
#### Batch
This is a quick way to submit any Jar or Python jar/script to a cluster via the Scheduler or Editor.
#### Interactive
Hue relies on Livy (http://livy.io/) for the interactive Scala, Python and R snippets.
Livy got initially developed in the Hue project but got a lot of traction and was moved to its own project on livy.io. Here is a tutorial on how to use a notebook to perform some Bike Data analysis.
Make sure that the Notebook and interpreters are set in the hue.ini, and Livy is up and running:
[spark]
# Host address of the Livy Server.
livy_server_host=localhost
[notebook]
## Show the notebook menu or not
show_notebooks=true
[[interpreters]]
# Define the name and how to connect and execute the language.
[[[hive]]]
# The name of the snippet.
name=Hive
# The backend connection to use to communicate with the server.
interface=hiveserver2
[[[spark]]]
name=Scala
interface=livy
[[[pyspark]]]
name=PySpark
interface=livy
## SDK
# Dashboards
Dashboards are an interactive way to explore your data quickly and easily. No programming is required and the analysis is done by drag & drops and clicks.
### SDK
# Browsers
Hue’s Browsers powers your Data Catalog. They let you easily search, glance and perform actions on data or jobs in Cloud or on premise clusters.
## Tables
The Table Browser enables you to manage the databases,
tables, and partitions of the metastore shared by
the ([Beeswax](../beeswax.html) and [Cloudera Impala Query
UI](../impala.html)) applications. You can use Metastore
Manager to perform the following operations:
- Databases
- [Select a database](#selectDatabase)
- [Create a database](#createDatabase)
- [Drop databases](#dropDatabase)
- Tables
- [Create tables](#createTables)
- [Browse tables](#browseTables)
- [Import data into a table](#importDataIntoTables)
- [Drop tables](#dropTables)
- [View the location of a table](#viewTableLocation)
Metastore Manager Installation and Configuration
------------------------------------------------
Metastore Manager is one of the applications installed as part of Hue.
Browsing Tables
---------------
**To browse table data:**
In the Table List window, check the checkbox next to a table name and
click **Browse Data**. The table's data displays in the Query Results
window.
**To browse table metadata:**
Do one of the following:
- In the Table List window, click a table name.
- Check the checkbox next to a table name and click **View**.
- The table's metadata displays in the **Columns** tab. You can view
the table data by selecting the **Sample** tab.
- If the table is partitioned, you can view the partition columns by
clicking the **Partition Columns** tab and display the partitions by
clicking **Show Partitions(n)**, where n is the number of partitions
in the ACTIONS pane on the left.
Importing Data into a Table
---------------------------
When importing data, you can choose to append or overwrite the table's
data with data from a file.
1. In the Table List window, click the table name. The Table Metadata
window displays.
2. In the ACTIONS pane, click **Import Data**.
3. For **Path**, enter the path to the file that contains the data you
want to import.
4. Check **Overwrite existing data** to replace the data in the
selected table with the imported data. Leave unchecked to append to
the table.
5. Click **Submit**.
Dropping Tables
---------------
1. In the Table List window, click the table name. The Table Metadata
window displays.
2. In the ACTIONS pane, click **Drop Table**.
3. Click **Yes** to confirm the deletion.
Viewing a Table's Location
--------------------------
1. In the Table List window, click the table name. The Table Metadata
window displays.
2. Click **View File Location**. The file location of the selected
table displays in its directory in the File Browser window.
## File Browser
The File Browser application lets you browse and manipulate files and
directories in the Hadoop Distributed File System (HDFS), S3 or ADLS.
With File Browser, you can:
- Create files and directories, upload and download files, upload zip
archives, and rename, move, and delete files and directories. You
can also change a file's or directory's owner, group, and
permissions. See [Files and Directories](#filesAndDirectories).
- Search for files, directories, owners, and groups. See [Searching
for Files and Directories](#searching).
- View and edit files as text or binary. See [Viewing and Editing
Files](#viewAndEdit).
### File systems
#### HDFS
#### S3
#### ADLS
File Browser Installation and Configuration
-------------------------------------------
File Browser is one of the applications installed as part of Hue. For
information about installing and configuring Hue, see the Hue Installation
manual.
Starting File Browser
---------------------
Files and Directories
---------------------
You can use File Browser to view the input and output files of your
MapReduce jobs. Typically, you can save your output files in /tmp or in
your home directory if your system administrator set one up for you. You
must have the proper permissions to manipulate other user's files.
### Creating Directories
1. In the File Browser window, select **New > Directory**.
2. In the **Create Directory** dialog box, enter a directory name and
then click **Submit**.
### Changing Directories
- Click the directory name or parent directory dots in the **File
Browser** window.
- Click the  icon, type a directory name, and
press **Enter**.
To change to your home directory, click **Home** in the path field at
the top of the **File Browser** window.
 **Note**:
The **Home** button is disabled if you do not have a home directory. Ask
a Hue administrator to create a home directory for you.
### Creating Files
1. In the File Browser window, select **New > File**.
2. In the **Create File** dialog box, enter a file name and then click
**Submit**.
### Uploading Files
You can upload text and binary files to the HDFS.
1. In the **File Browser** window, browse to the directory where you
want to upload the file.
2. Select **Upload \> Files**.
3. In the box that opens, click **Upload a File** to browse to and
select the file(s) you want to upload, and then click **Open**.
### Copying a File
1. In the **File Browser** window, check the checkbox next to the file
you want to copy.
2. Click the  Copy button.
### Downloading Files
You can download text and binary files to the HDFS.
1. In the **File Browser** window, check the checkbox next to the file
you want to download.
2. Click the **Download** button.
### Uploading Zip Archives
You can upload zip archives to the HDFS. The archive is uploaded and
extracted to a directory named archivename.
1. In the **File Browser** window, browse to the directory where you
want to upload the archive.
2. Select **Upload > Zip file**.
3. In the box that opens, click **Upload a zip file** to browse to and
select the archive you want to upload, and then click **Open**.
### Trash Folder
File Browser supports the HDFS trash folder (*home directory*/.Trash) to
contain files and directories before they are permanently deleted. Files
in the folder have the full path of the deleted files (in order to be
able to restore them if needed) and checkpoints. The length of time a
file or directory stays in the trash depends on HDFS properties.
**Open**
1. In the **File Browser** window, click .
**Move Files and Directories To**
1. In the **File Browser** window, check the checkbox next to one or
more files and directories.
2. Select **Delete > Move to trash**.
**Empty**
1. In the **File Browser** window, click .
2. Click **Empty**.
### Renaming, Moving, Deleting, and Restoring Files and Directories
**Rename**
1. In the **File Browser** window, check the checkbox next to the file
or directory you want to rename.
2. Click the **Rename** button.
3. Enter the new name and then click **Submit**.
**Move**
1. In the **File Browser** window, check the checkbox next to the file
or directory you want to move.
2. Click the **Move** button.
3. In the **Move** dialog box, browse to or type the new directory, and
then click **Submit**.
**Delete**
1. In the **File Browser** window, check the checkbox next to the file
or directory you want to delete. If you select a directory, all of
the files and subdirectories contained within that directory are
also deleted.
2. Choose one of the following:
- **Delete > Move to trash**
- **Delete > Delete forever**
3. Click **Yes** to confirm. When you move a file to trash it is stored
in the .Trash folder in your home directory.
**Restore**
1. In the **File Browser** window, open the .Trash folder.
2. Navigate to the folder containing the file you want to restore.
3. Check the checkbox next to the file.
4. Click **Restore**.
### Changing a File's or Directory's Owner, Group, or Permissions
 **Note**:
Only the Hadoop superuser can change a file's or directory's owner,
group, or permissions. The user who starts Hadoop is the Hadoop
superuser. The Hadoop superuser account is not necessarily the same as a
Hue superuser account. If you create a Hue user (in User Admin) with the
same user name and password as the Hadoop superuser, then that Hue user
can change a file's or directory's owner, group, or permissions.
**Owner or Group**
1. In the **File Browser** window, check the checkbox next to the
select the file or directory whose owner or group you want to
change.
2. Choose **Change Owner/Group** from the Options menu.
3. In the **Change Owner/Group** dialog box:
- Choose the new user from the **User** drop-down menu.
- Choose the new group from the **Group** drop-down menu.
- Check the **Recursive** checkbox to propagate the change.
4. Click **Submit** to make the changes.
**Permissions**
1. In the **File Browser** window, check the checkbox next to the file
or directory whose permissions you want to change.
2. Click the **Change Permissions** button.
3. In the **Change Permissions** dialog box, select the permissions you
want to assign and then click **Submit**.
Searching for Files and Directories
-----------------------------------
To search for files or directories by name using the query search box,
enter the name of the file or directory in the query search box. File
Browser lists the files or directories matching the search criteria.
Viewing and Editing Files
-------------------------
You can view and edit files as text or binary.
**View**
1. In the **File Browser** window, click the file you want to view.
File Browser displays the first 4,096 bytes of the file in the
**File Viewer** window.
- If the file is larger than 4,096 bytes, use the Block navigation
buttons (First Block, Previous Block, Next Block, Last Block) to
scroll through the file block by block. The **Viewing Bytes**
fields show the range of bytes you are currently viewing.
- To switch the view from text to binary, click **View as Binary**
to view a hex dump.
- To switch the view from binary to text, click **View as Text**.
**Edit**
1. If you are viewing a text file, click **Edit File**. File Browser
displays the contents of the file in the **File Editor** window.
2. Edit the file and then click **Save** or **Save As** to save the
file.
**View Location in HDFS**
Click **View File Location**. File Browser displays the file's location
in the **File Browser** window.
Job Browser
===========
The Job Browser application lets you to examine the Hadoop MapReduce
jobs running on your Hadoop cluster. Job Browser presents the job and
tasks in layers. The top layer is a list of jobs, and you can link to a
list of that job's tasks. You can then view a task's attempts and the
properties of each attempt, such as state, start and end time, and
output size. To troubleshoot failed jobs, you can also view the logs of
each attempt.
Job Browser Installation and Configuration
------------------------------------------
Job Browser is one of the applications installed as part of Hue. For
information about installing and configuring Hue, see the Hue Installation
manual.
Job Browser can display both MRv1 and MRv2 jobs, but must be configured
to display one type at a time.
Starting Job Browser
--------------------
Click the **Job Browser** icon ()
in the navigation bar at the top of the Hue web page.
If there are no jobs that have been run, the **Welcome to the Job
Browser** page opens, with links to the Job Designer and Beeswax.
If there are jobs running, then the Job Browser list appears.
Filtering the Job Browser List
------------------------------
- To filter the jobs by their state (such as **Running** or
**Completed**), choose a state from the **Job status** drop-down
menu.
- To filter by a user who ran the jobs, enter the user's name in the
**User Name** query box.
- To filter by job name, enter the name in the **Text** query box.
- To clear the filters, choose **All States** from the **Job status**
drop-down menu and delete any text in the **User Name** and **Text**
query boxes.
- To display retired jobs, check the **Show retired jobs** checkbox.
Retired jobs show somewhat limited information – for example,
information on maps and reduces and job duration is not available.
Jobs are designated as Retired by the JobTracker based on the value
of mapred.jobtracker.retirejob.interval. The retired jobs no longer
display after the JobTracker is restarted.
Viewing Job Information and Logs
--------------------------------
 **Note**: At any level you can view the log
for an object by clicking the  icon in the Logs
column.
**To view job information for an individual job:**
1. In the **Job Browser** window, click **View** at the right of the
job you want to view. This shows the **Job** page for the job, with
the recent tasks associated with the job are displayed in the
**Tasks** tab.
2. Click the **Metadata** tab to view the metadata for this job.
3. Click the **Counters** tab to view the counter metrics for the job.
**To view details about the tasks associated with the job:**
1. In the **Job** window, click the **View All Tasks** link at the
right just above the **Recent Tasks** list. This lists all the tasks
associated with the job.
2. Click **Attempts** to the right of a task to view the attempts for
that task.
**To view information about an individual task:**
1. In the **Job** window, click the **View** link to the right of the
task. The attempts associated with the task are displayed.
2. Click the **Metadata** tab to view metadata for this task. The
metadata associated with the task is displayed.
3. To view the Hadoop counters for a task, click the **Counters** tab.
The counters associated with the task are displayed.
4. To return to the **Job** window for this job, click the job number
in the status panel at the left of the window.
**To view details about a task attempt:**
1. In the **Job Task** window, click the **View** link to the right of
the task attempt. The metadata associated with the attempt is
displayed under the **Metadata** tab.
2. To view the Hadoop counters for the task attempt, click the
**Counters** tab. The counters associated with the attempt are
displayed.
3. To view the logs associated with the task attempt, click the
**Logs** tab. The logs associated with the task attempt are
displayed.
4. To return to the list of tasks for the current job, click the task
number in the status panel at the left of the window.
### Scheduler
The application lets you build workflows and then schedule them to run regularly automatically. A monitoring interface shows the progress, logs and allow actions like pausing or stopping jobs.
The Oozie Editor/Dashboard application allows you to define Oozie
workflow, coordinator, and bundle applications, run workflow,
coordinator, and bundle jobs, and view the status of jobs. For
information about Oozie, see [Oozie
Documentation](http://archive.cloudera.com/cdh4/cdh/4/oozie/).
A workflow application is a collection of actions arranged in a directed
acyclic graph (DAG). It includes two types of nodes:
- Control flow - start, end, fork, join, decision, and kill
- Action - [MapReduce](../jobdesigner.html#mapreduce),
[Streaming](../jobdesigner.html#streaming),
[Java](../jobdesigner.html#java),
[Pig](../jobdesigner.html#pig),
[Hive](../jobdesigner.html#hive),
[Sqoop](../jobdesigner.html#sqoop),
[Shell](../jobdesigner.html#shell),
[Ssh](../jobdesigner.html#ssh),
[DistCp](../jobdesigner.html#distcp),
[Fs](../jobdesigner.html#fs), and
[Email](../jobdesigner.html#email).
In order to run DistCp, Streaming, Pig, Sqoop, and Hive jobs, Oozie
must be configured to use the Oozie ShareLib. See the Oozie Installation
manual.
A coordinator application allows you to define and execute recurrent and
interdependent workflow jobs. The coordinator application defines the
conditions under which the execution of workflows can occur.
A bundle application allows you to batch a set of coordinator
applications.
Oozie Editor/Dashboard Installation and Configuration
-----------------------------------------------------
Oozie Editor/Dashboard is one of the applications installed as part of
Hue. For information about installing and configuring Hue, see the Hue Installation
manual.
Starting Oozie Editor/Dashboard
-------------------------------
Click the **Oozie Editor/Dashboard** icon
() in the navigation bar at the top of
the Hue browser page. **Oozie Editor/Dashboard** opens with the
following screens:
- [Dashboard](#dashboard) - shows the running and completed workflow,
coordinator, and bundle jobs and information about Oozie
instrumentation and configuration. The screen is selected and opened
to the Workflows page.
- [Workflow Manager](#workflowManager) - shows available workflows and
allows you to create and import workflows.
- [Coordinator Manager](#coordinatorManager) - shows available coordinators and
allows you to create coordinators.
- [Bundle Manager](#bundleManager) - shows available bundles and
allows you to create bundles.
Filtering Lists in Oozie Editor/Dashboard
-----------------------------------------
Many screens contain lists. When you type in the Filter field on
screens, the lists are dynamically filtered to display only those rows
containing text that matches the specified substring.
Permissions in Oozie Editor/Dashboard
-------------------------------------
In the Dashboard workflows, coordinators, and bundles can only be
viewed, submitted, and modified by their owner or a superuser.
Editor permissions for performing actions on workflows, coordinators,
and bundles are summarized in the following table:
| Action | Superuser or Owner | All |
| View | Y. | Only if "Is shared" is set |
| Submit | Y. | Only if "Is shared" is set |
| Modify | Y. | N |
Dashboard
---------
The Dashboard shows a summary of the running and completed workflow,
coordinator, and bundle jobs.
You can view jobs for a period up to the last 30 days.
You can filter the list by date (1, 7, 15, or 30 days) or status
(Succeeded, Running, or Killed). The date and status buttons are
toggles.
### Workflows
Click the **Workflows** tab to view the running and completed workflow
jobs for the filters you have specified.
Click a workflow row in the Running or Completed table to view detailed
information about that workflow job.
In the left pane contains a link to the workflow and the variable values
specified.
For the selected job, the following information is available in the
right area.
- **Graph** tab shows the workflow DAG.
- **Actions** tab shows you details about the actions that make up the
workflow.
- Click the **Id** link to see additional details about the
action.
- Click the **External Id** link to view the job in the Job
Browser.
- **Details** tab shows job statistics including start and end times.
- **Configuration** tab shows selected job configuration settings.
- **Logs** tab shows log output generated by the workflow job.
- **Definition** tab shows the Oozie workflow definition, as it
appears in the workflow.xml file (also linked under the application
path properties in the **Details** tab and the **Configuration**
tab).
For each action in the workflow you can:
- Click the  icon to view the action screen,
which contains:
- **Details** tab shows job statistics including start and end
times.
- **Configuration** tab shows the action configuration settings.
- **Child Jobs** tab lists jobs generated by the action.
- Click the  icon to view the log in the Job
Browser.
### Coordinators
Click the **Coordinators** tab to view the running and completed
coordinator jobs for the filters you have specified.
For the selected job, the following information is available.
- The **Calendar** tab shows the timestamp of the job. Click the
timestamp to open the workflow DAG.
- The **Actions** tab shows you details about the actions that make up
the coordinator.
- Click the **Id** link to see additional details about the
action.
- Click the **External Id** link to view the job in the Job
Browser.
- The **Configuration** tab shows selected job configuration settings.
- The **Logs** tab shows log output generated by the coordinator.
- The **Definition** tab shows the Oozie coordinator definition, as it
appears in the coordinator.xml file (also linked under the
oozie.coord.application.path property in the **Configuration** tab).
### Editing a Workflow
In the Workflow Editor you can easily perform operations on Oozie action
and control nodes.
#### Action Nodes
The Workflow Editor supports dragging and dropping action nodes. As you
move the action over other actions and forks, highlights indicate active
areas. If there are actions in the workflow, the active areas are the
actions themselves and the areas above and below the actions. If you
drop an action on an existing action, a fork and join is added to the
workflow.
- Add actions to the workflow by clicking an action
 button and drop the action on the
workflow. The Edit Node screen displays.
1. Set the action properties and click **Done**. Each action in a
workflow must have a unique name.
- Copy an action by clicking the  **Copy**
button.
1. The action is opened in the Edit Node screen.
2. Edit the action properties and click **Done**. The action is added
to the end of the workflow.
- Delete an action by clicking the  button.
- Edit an action by clicking the  button.
- Change the position of an action by left-clicking and dragging an
action to a new location.
#### Control Nodes
- Create a fork and join by dropping an action on top of another
action.
- Remove a fork and join by dragging a forked action and dropping it
above the fork.
- Convert a fork to a decision by clicking the
 button.
- To edit a decision:
1. Click the  button.
2. Fill in the predicates that determine which action to perform
and select the default action from the drop-down list.
3. Click **Done**.
### Uploading Workflow Files
In the Workflow Editor, click the **Upload** button.
The workspace of the workflow is opened in the File Browser application.
Follow the procedure in [Uploading
Files](../filebrowser.html#uploadingFiles) to upload the files. You must
put JAR files in a lib directory in the workspace.
### Editing Workflow Properties
1. In the Workflow Editor, click the link under the Name or Description
fields in the left pane.
2. To share the workflow with all users, check the **Is shared**
checkbox.
3. To set advanced execution options, click **advanced** and edit the
deployment directory, add parameters and job properties, or specify
a job.xml file.
4. Click **Save**.
Coordinator Manager
-------------------
In Coordinator Manager you create Oozie coordinator applications and
submit them for execution.
Click the **Coordinators** tab to open the Coordinator Manager.
Each row shows a coordinator: its name, description, timestamp of its
last modification. It also shows:
- **Workflow** - the workflow that will be run by the coordinator.
- **Frequency** - how often the workflow referenced by the coordinator
will be run.
- **Status** - who can run the coordinator. **shared** means users
other than the owner can access the workflow. **personal** means
only the owner can modify or submit the workflow. The default is
personal.
- **Owner** - the user that created the coordinator.
In Coordinator Editor, you edit coordinators and the datasets required
by the coordinators. For information about defining coordinators and
datasets, see the [Coordinator
Specification](http://archive.cloudera.com/cdh4/cdh/4/oozie/CoordinatorFunctionalSpec.html).
### Editing a Coordinator
In the Coordinator Editor you specify coordinator properties and the
datasets on which the workflow scheduled by the coordinator will operate
by stepping through screens in a wizard. You can also advance to
particular steps and revisit steps by clicking the Step "tabs" above the
screens. The following instructions walk you through the wizard.
1. Type a name, select the workflow, check the **Is shared checkbox**
to share the job, and click **Next**. If the Coordinator Editor was
opened after scheduling a workflow, the workflow will be set.
2. Select how many times the coordinator will run for each specified
unit, the start and end times of the coordinator, the timezone of
the start and end times, and click **Next**. Times must be expressed
as UTC times. For example, to run at 10 pm PST, specify a start time
of 6 am UTC of the following day (+8 hours) and set the Timezone
field to America/Los\_Angeles.
3. Click **Add** to select an input dataset and click **Next**. If no
datasets exist, follow the procedure in [Creating a
Dataset](#creatingDataset).
4. Click **Add** to select an output dataset. Click **Save
coordinator** or click **Next** to specify advanced settings.
5. To share the coordinator with all users, check the **Is****shared**
checkbox.
6. Fill in parameters to pass to Oozie, properties that determine how
long a coordinator will wait before timing out, how many
coordinators can run and wait concurrently, and the coordinator
execution policy.
7. Click **Save coordinator**.
### Creating a Dataset
1. In the Coordinator Editor, do one of the following:
- Click **here** in the Inputs or Outputs pane at the top of the
editor.
- In the pane at the left, click the **Create new** link. Proceed
with [Editing a Dataset](#editingDataset).
### Displaying Datasets
1. In the Coordinator Editor, click **Show existing** in pane at the
left.
2. To edit a dataset, click the dataset name in the Existing datasets
table. Proceed with [Editing a Dataset](#editingDataset).
### Editing a Dataset
1. Type a name for the dataset.
2. In the Start and Frequency fields, specify when and how often the
dataset will be available.
3. In the URI field, specify a URI template for the location of the
dataset. To construct URIs and URI paths containing dates and
timestamps, you can specify the variables
${YEAR},${MONTH},${DAY},${HOUR},${MINUTE}. For example:
hdfs://foo:9000/usr/app/stats/${YEAR}/${MONTH}/data.
4. In the Instance field, click a button to choose a default, single,
or range of data instances. For example, if frequency==DAY, a window
of the last rolling 5 days (not including today) would be expressed
as start: -5 and end: -1. Check the advanced checkbox to display a
field where you can specify a coordinator [EL
function](http://archive.cloudera.com/cdh4/cdh/4/oozie/CoordinatorFunctionalSpec.html).
5. Specify the timezone of the start date.
6. In the Done flag field, specify the flag that identifies when input
datasets are no longer ready.
### Displaying the History of a Coordinator
1. Click the **Dashboard** tab.
2. Click the **Coordinators** tab.
3. Click a coordinator.
4. Click the **Actions** tab.
Bundle Manager
--------------
In Bundle Manager you create Oozie bundle applications and submit them
for execution.
Click the **Bundle** tab to open the Bundle Manager.
Each row shows a bundle: its name, description, timestamp of its last
modification. It also shows:
- **Coordinators** - the coordinators that will be run by the bundle.
- **Kick off** - the UTC time when the coordinators referenced by the
bundle will be started.
- **Status** - who can run the bundle. **shared** means users other
than the owner can access the workflow. **personal** means only the
owner can modify or submit the workflow. The default is personal.
- **Owner** - the user that created the bundle.
For information about defining bundles, see the [Bundle
Specification](http://archive.cloudera.com/cdh4/cdh/4/oozie/BundleFunctionalSpec.html).
### Opening a Bundle
To open a bundle, in Bundle Manager, click the bundle. Proceed with
[Editing a Bundle](#editingBundle).
### Creating a Bundle
1. Click the **Create** button at the top right.
2. In the Name field, type a name.
3. In the Kick off time field, choose a kick off time.
4. Check the Is shared checkbox to allow all users to access the
workflow.
5. Click **Save**. The Bundle Editor opens. Proceed with [Editing a
Bundle](#editingBundle).
### Submitting a Bundle
To submit a bundle for execution, check the checkbox next to the bundle
and click the **Submit** button.
### Editing a Bundle
In the Bundle Editor, you specify properties by stepping through screens
in a wizard. You can also advance to particular steps and revisit steps
by clicking the Step "tabs" above the screens. The following
instructions walk you through the wizard.
1. Click **Add** to select a coordinator that the bundle will kick off.
2. Choose the kick off time. The time must be expressed as a UTC time.
For example, to run at 10 pm PST, specify a start time of 6 am UTC
of the following day (+8 hours).
3. To share the bundle with all users, check the **Is shared**
checkbox.
4. Click **Next** to specify advanced settings or click **Save
bundle**.
5. Fill in parameters to pass to Oozie.
6. Click **Save bundle**.
# Contrib
Those modules are not active enough to be officially maintained in the core Hue but can still
fit your needs. Any [contribution](https://github.com/cloudera/hue/wiki) is still welcomed!
## SDK
Check the SDK guide or contact the community about how to build your own custom app.
## HBase Browser
We'll take a look at the new [HBase Browser App](http://gethue.com/the-web-ui-for-hbase-hbase-browser).
Prerequisites before using the app:
\1. Have HBase and Thrift Service 1 initiated (Thrift can be configured)
\2. Configure your list of HBase Clusters in
[hue.ini](https://github.com/cloudera/hue/blob/master/desktop/conf.dist/hue.ini#L467)
to point to your Thrift IP/Port
## SmartView
The smartview is the view that you land on when you first enter a table.
On the left hand side are the row keys and hovering over a row reveals a
list of controls on the right. Click a row to select it, and once
selected you can perform batch operations, sort columns, or do any
amount of standard database operations. To explore a row, simple scroll
to the right. By scrolling, the row should continue to lazily-load cells
until the end.
### Adding Data
To initially populate the table, you can insert a new row or bulk upload
CSV/TSV/etc. type data into your table.
On the right hand side of a row is a '+' sign that lets you insert
columns into your
row
### Mutating Data
To edit a cell, simply click to edit inline.
If you need more control or data about your cell, click “Full Editor” to
edit.
In the full editor, you can view cell history or upload binary data to
the cell. Binary data of certain MIME Types are detected, meaning you
can view and edit images, PDFs, JSON, XML, and other types directly in
your browser!
Hovering over a cell also reveals some more controls (such as the delete
button or the timestamp). Click the title to select a few and do batch
operations:
If you need some sample data to get started and explore, check out this
howto create [HBase table
tutorial](http://gethue.com/hadoop-tutorial-how-to-create-example-tables-in-hbase).
### Smart Searchbar
The "Smart Searchbar" is a sophisticated tool that helps you zero-in on
your data. The smart search supports a number of operations. The most
basic ones include finding and scanning row keys. Here I am selecting
two row keys with:
domain.100, domain.200
Submitting this query gives me the two rows I was looking for. If I want
to fetch rows after one of these, I have to do a scan. This is as easy
as writing a '+' followed by the number of rows you want to fetch.
domain.100, domain.200 +5
Fetches domain.100 and domain.200 followed by the next 5 rows. If you're
ever confused about your results, you can look down below and the query
bar and also click in to edit your query.
The Smart Search also supports column filtering. On any row, I can
specify the specific columns or families I want to retrieve. With:
domain.100[column_family:]
I can select a bare family, or mix columns from different families like
so:
domain.100[family1:, family2:, family3:column_a]
Doing this will restrict my results from one row key to the columns I
specified. If you want to restrict column families only, the same effect
can be achieved with the filters on the right. Just click to toggle a
filter.
Finally, let's try some more complex column filters. I can query for
bare columns:
domain.100[column_a]
This will multiply my query over all column families. I can also do
prefixes and scans:
domain.100[family: prefix* +3]
This will fetch me all columns that start with prefix\* limited to 3
results. Finally, I can filter on range:
domain.100[family: column1 to column100]
This will fetch me all columns in 'family:' that are lexicographically
\>= column1 but <= column100. The first column ('column1') must be a
valid column, but the second can just be any string for comparison.
The Smart Search also supports prefix filtering on rows. To select a
prefixed row, simply type the row key followed by a star \*. The prefix
should be highlighted like any other searchbar keyword. A prefix scan is
performed exactly like a regular scan, but with a prefixed row.
domain.10* +10
Finally, as a new feature, you can also take full advantage of the
[HBase filtering](denied:about:blank)language, by typing your filter
string between curly braces. HBase Browser autocompletes your filters
for you so you don't have to look them up every time. You can apply
filters to rows or scans.
domain.1000 {ColumnPrefixFilter('100-') AND ColumnCountGetFilter(3)}
This doc only covers a few basic features of the Smart Search. You can
take advantage of the full querying language by referring to the help
menu when using the app. These include column prefix, bare columns,
column range, etc. Remember that if you ever need help with the
searchbar, you can use the help menu that pops up while typing, which
will suggest next steps to complete your query.
## Sqoop 2 Editor
The Sqoop UI enables transfering data from a relational database
to Hadoop and vice versa. The UI lives uses Apache Sqoop to do this.
See the [Sqoop Documentation](http://sqoop.apache.org/docs/1.99.2/index.html) for more details on Sqoop.
Installation and Configuration
------------------------------
The Sqoop UI is one of the applications installed as part of
Hue. For information about installing and configuring Hue, see the Hue Installation
manual.
Starting
--------
Click the **Sqoop** icon
() in the navigation bar at the top of
the Hue browser page.
Sqoop Jobs
----------
Sqoop UI is oriented around jobs in Apache Sqoop.
### Creating a New Job
1. Click the **New job** button at the top right.
2. In the Name field, enter a name.
3. Choose the type of job: import or export.
The proceeding form fields will change depending on which type is chosen.
4. Select a connection, or create one if it does not exist.
5. Fill in the rest of the fields for the job.
For importing, the "Table name", "Storage type", "Output format", and "Output directory" are necessary at a minimum.
For exporting, the "Table name" and "Input directory" are necessary at a minimum.
6. Click **save** to finish.
### Editing a Job
1. In the list of jobs, click on the name of the job.
2. Edit the desired form fields in the job.
### Copying a Job
1. In the list of jobs, click on the name of the job.
2. On the left hand side of the job editor, there should be a panel containing actions.
Click **Copy**.
### Removing a Job
1. In the list of jobs, click on the name of the job.
2. On the left hand side of the job editor, there should be a panel containing actions.
Click **Delete**.
### Running a Job
There's a status on each of the items in the job list indicating
the last time a job was ran. The progress of the job should dynamically
update. There's a progress bar at the bottom of each item on the job list
as well.
1. In the list of jobs, click on the name of the job.
2. On the left hand side of the job editor, there should be a panel containing actions.
Click **Run**.
### Creating a New Connection
1. Click the **New job** button at the top right.
2. At the connection field, click the link titled **Add a new connection**.
3. Fill in the displayed fields.
4. Click **save** to finish.
### Editing a Connection
1. Click the **New job** button at the top right.
2. At the connection field, select the connection by name that should be edited.
3. Click **Edit**.
4. Edit the any of the fields.
5. Click **save** to finish.
### Removing a Connection
1. Click the **New job** button at the top right.
2. At the connection field, select the connection by name that should be deleted.
3. Click **Delete**.
NOTE: If this does not work, it's like because a job is using that connection.
Make sure not jobs are using the connection that will be deleted.
### Filtering Sqoop Jobs
The text field in the top, left corner of the Sqoop Jobs page enables fast filtering
of sqoop jobs by name.
## ZooKeeper Browser
The main two features are:
- Listing of the ZooKeeper cluster stats and clients
- Browsing and edition of the ZNode hierarchy
ZooKeeper Browser requires the [ZooKeeper
REST](https://github.com/apache/zookeeper/tree/trunk/src/contrib/rest)
service to be running. Here is how to setup this one:
First get and build ZooKeeper:
git clone https://github.com/apache/zookeeper
cd zookeeper
ant
Buildfile: /home/hue/Development/zookeeper/build.xml
init:
[mkdir] Created dir: /home/hue/Development/zookeeper/build/classes
[mkdir] Created dir: /home/hue/Development/zookeeper/build/lib
[mkdir] Created dir: /home/hue/Development/zookeeper/build/package/lib
[mkdir] Created dir: /home/hue/Development/zookeeper/build/test/lib
...
And start the REST service:
cd src/contrib/rest
nohup ant run&
If ZooKeeper and the REST service are not on the same machine as Hue, go
update the [Hue
settings](https://github.com/cloudera/hue/blob/master/desktop/conf.dist/hue.ini#L581)
and specify the correct hostnames and ports:
[zookeeper]
[[clusters]]
[[[default]]]
# Zookeeper ensemble. Comma separated list of Host/Port.
# e.g. localhost:2181,localhost:2182,localhost:2183
## host_ports=localhost:2181
# The URL of the REST contrib service
## rest_url=http://localhost:9998
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