Editor

The goal of the Editor is to open-up data to more users by making self service querying easy and productive.

It is available in Editor or Notebook mode and will be integrated with the Dashboard soon. The Editor focuses on Apache Hive and Apache Impala but is also compatible with:

Editor’s Layout

 

SQL

Find or import your data

Use the left metadata assists to browse your existing data without losing your editor. The top search will look through your saved queries and matching tables, columns and databases. Objects can be tagged for a quick retrieval or assigning a more “humane” name. If the data does not exist yet, just drag & drop it to trigger the Create Table wizard and to import it in just two steps.

Query your data

When you found your data, the Editor’s autocomplete is extremely powerful as they support 90-100% of the language syntax and will highlight any syntax or logical error. The right assistant provides quick previews of the datasets, which columns or JOINs are popular and recommendations on how to type optimized queries. After the querying, refine your results before exporting to S3/HDFS/ADLS or downloaded as CSV/Excel.

Look at more functionalities here.

Configuration

Point Hue to any SQL engines by simplify putting the hostnames and ports in the hue.ini, e.g. for HiveServer2, Impala:

[beeswax]
  # Host where HiveServer2 is running.
  hive_server_host=localhost

[impala]
  # Host of the Impala Server (one of the Impalad)
  server_host=localhost

[notebook]
  [interpreters]]
    [[[mysql]]]
       name=MySql JDBC
       interface=jdbc
       ## Specific options for connecting to the server.
       ## The JDBC connectors, e.g. mysql.jar, need to be in the CLASSPATH environment variable.
       ## If 'user' and 'password' are omitted, they will be prompted in the UI.
       options='{"url": "jdbc:mysql://localhost:3306/hue", "driver": "com.mysql.jdbc.Driver", "user": "root", "password": "root"}'

Look at more configurations here.

 

Pig

Pig is supported via the Pig Editor.

 

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 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

 

Other engines

Other modes like MapReduce, Java, Shell, Sqoop are also available. Here is a list of the existing connectors. Connectors are pluggable and can new engines can be supported. Feel free to comment on the Hue list of github about it.