title: "Browser" date: 2019-03-13T18:28:09-07:00 draft: false
The goal of the importer is to allow ad hoc queries on data not yet in the clusters thereby expedite self-service analytics.
If you want to import your own data instead of installing the sample
tables, open the importer from the left menu or from the little + in the left assist.
If you've ever struggled with creating new SQL tables from files, you'll be happy to learn that this is now much easier. The wizard has been revamped to two simple steps and also offers more formats. Now users just need to:
And that's it!
To learn more, watch the video on Data Import Wizard.
Although you can create tables by executing the appropriate Hive HQL DDL query commands, it is easier to create a table using the create table wizard.
From a File
If you've ever struggled with creating new SQL tables from files, you'll be happy to learn that this is now much easier. With the latest Hue release, you can now create these in an ad hoc way and thereby expedite self-service analytics. The wizard has been revamped to two simple steps and also offers more formats. Now users just need to:
Files can be dragged & dropped, selected from HDFS or S3 (if configured), and their formats are automatically detected. The wizard also assists when performing advanced functionalities like table partitioning, Kudu tables, and nested types.
Manually
In the past, indexing data into Solr to then explore it with a Dynamic Dashboard has been quite difficult. The task involved writing a Solr schema and a Morphlines file then submitting a job to YARN to do the indexing. Often times getting this correct for non trivial imports could take a few days of work. Now with Hue's new feature you can start your YARN indexing job in minutes. This tutorial offers a step by step guide on how to do it.
Read more about ingesting data from traditional databases.
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.
Read more about Dashboards.
Simply drag & drop widgets that are interconnected together. This is great for exploring new datasets or monitoring without having to type.
Any CSV file can be dragged & dropped and ingested into an index in a few clicks via the Data Import Wizard [link]. The indexed data is immediately queryable and its facets/dimensions will be very fast to explore.
The Collection browser got polished in the last releases and provide more information on the columns. The left metadata assist of Hue 4 makes it handy to list them and peak at their content via the sample popup.
The search box support live prefix filtering of field data and comes with a Solr syntax autocomplete in order to make the querying intuitive and quick. Any field can be inspected for its top values of statistic. This analysis happens very fast as the data is indexed.
The top search bar offers a full autocomplete on all the values of the index.
The “More like This” feature lets you selected fields you would like to use to find similar records. This is a great way to find similar issues, customers, people... with regard to a list of attributes.
This is work in progress but dashboards will soon offer a classic reporting option.
Read more about extending connectors.
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.
The Table Browser enables you to manage the databases, tables, and partitions of the metastore shared by the Hive and Impala. You can use Metastore Manager to perform the following operations:
Databases
Tables
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:
Hue can be setup to read and write to a configured S3 account, and users get autocomplete capabilities and can directly query from and save data to S3 without any intermediate moving/copying to HDFS.
** Create Hive Tables Directly From S3** Hue's Metastore Import Data Wizard can create external Hive tables directly from data directories in S3. This allows S3 data to be queried via SQL from Hive or Impala, without moving or copying the data into HDFS or the Hive Warehouse.
To create an external Hive table from S3, navigate to the Metastore app, select the desired database and then click the “Create a new table from a file” icon in the upper right.
Enter the table name and optional description, and in the “Input File or Directory” filepicker, select the S3A filesystem and navigate to the parent directory containing the desired data files and click the “Select this folder” button. The “Load Data” dropdown should automatically select the “Create External Table” option which indicates that this table will directly reference an external data directory.
Choose your input files' delimiter and column definition options and finally click “Create Table” when you're ready to create the Hive table. Once created, you should see the newly created table details in the Metastore.
Save Query Results to S3
Now that we have created external Hive tables created from our S3 data, we can jump into either the Hive or Impala editor and start querying the data directly from S3 seamlessly. These queries can join tables and objects that are backed either by S3, HDFS, or both. Query results can then easily be saved back to S3.
** S3 Configuration **
Learn more about it on the ADLS integration post.
Users gets autocomplete capabilities and more:
** Exploring ADLS in Hue's file browser ** Once Hue is successfully configured to connect to ADLS, we can view all accessible folders within the account by clicking on the ADLS root. From here, we can view the existing keys (both directories and files) and create, rename, move, copy, or delete existing directories and files. Additionally, we can directly upload files to ADLS.
** Create Hive Tables Directly From ADLS ** Hue's table browser import wizard can create external Hive tables directly from files in ADLS. This allows ADLS data to be queried via SQL from Hive or Impala, without moving or copying the data into HDFS or the Hive Warehouse. To create an external Hive table from ADLS, navigate to the table browser, select the desired database and then click the plus icon in the upper right. Select a file using the file picker and browse to a file on ADLS.
** Save Query Results to ADLS ** Now that we have created external Hive tables created from our ADLS data, we can jump into either the Hive or Impala editor and start querying the data directly from ADLS seamlessly. These queries can join tables and objects that are backed either by ADLS, HDFS, or both. Query results can then easily be saved back to ADLS.
** ADLS Configuration **
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.
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.
You can upload text and binary files to the HDFS.
You can download text and binary files to the HDFS.
You can extract zip archives to the HDFS. The archive is extracted to a directory named archivename.
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.
In the File Browser window, click the Trash icon.
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
In the Change Owner/Group dialog box:
Click Submit to make the changes.
Permissions
You can view and edit files as text or binary.
View
Edit
Sentry roles and privileges can directly be edited in the Security interface.
Solr privileges can be edited directly via the interface.
For listing collections, query and creating collection:
Admin=*->action=*
Collection=*->action=*
Schema=*->action=*
Config=*->action=*
The Job Browser application lets you to examine multiple types of jobs jobs running in the 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.
If there are jobs running, then the Job Browser list appears.
Note: At any level you can view the log for an object by clicking the Log icon in the Logs column.
To view job information for an individual job:
There are three ways to access the new browser:
Best: Click on the query ID after executing a SQL query in the editor. This will open the mini job browser overlay at the current query. Having the query execution information side by side the SQL editor is especially helpful to understand the performance characteristics of your queries. Open the mini job browser overlay and navigate to the queries tab. Open the job browser and navigate to the queries tab.
Query capabilities
Read more about it on Browsing Impala Query Execution within the SQL Editor .
List submitted workflows, schedules and bundles.
List Livy sessions and submitted statements.