title: "SDK" date: 2019-03-13T18:28:09-07:00 draft: false
They provide SQL integration with any database. Here is a list of the existing connectors.
Connectors are pluggable and can new engines can be supported. Feel free to contact the community.
Close to 100% of Hive and Impala grammar is supported which makes the autocomplete extremly powerful. Other languages defaults to a generic SQL grammar.
See How to write your own SQL parser. Integrating Apache Calcite, ZetaSql... would make SQL users even happier with a lot more Databases!
SqlAlchemy is the prefered way if the HiveServer2 API is not supported by the database. The implementation is in sql_alchemy.py and is depends on the repective SqlAlchemy dialects.
With the JDBC proxy, query editor with any JDBC compatible database. View the JDBC connector.
Note In the long term, SqlAlchemy is prefered as more "Python native".
MapReduce, Pig, Java, Shell, Sqoop, DistCp Oozie connector.
Based on the Livy REST API
The Job Browser is generic and can list any type of jobs, queries and provide bulk operations like kill, pause, delete... and access to logs and recommendations.
Here is its API.
Here is an example on how the File Browser can list HDFS, S3 files and ADLS.
Note Ceph can be used via the S3 browser.
Dashboards are generic and support Solr and any SQL:
The API was influenced by Solr but is now generic:
Implementations:
When HS2, RDBMS, and JDBC Are Not Enough
If the built-in HiveServer2 (Hive, Impala, Spark SQL), RDBMS (MySQL, PostgreSQL, Oracle, SQLite), and JDBC interfaces don’t meet your needs, you can implement your own connector to the notebook app: Notebook Connectors. Each connector API subclasses the Base API and must implement the methods defined within; refer to the JdbcApi or RdbmsApi for representative examples.
A connector similar to Solr or SQL Alchemy binding would need to be developed HUE-7828
Hue can be accessed directly via a Django Python Shell or by its REST API.
REST APIs are not all public yet but this is work in progress in HUE-1450.
Hue is Ajax based and has a REST API used by the browser to communicate (e.g. submit a query or workflow, list some S3 files, export a document...). Currently this API is private and subject to change but can be easily reused. You would need to GET /accounts/login to get the CSRF token and POST it back along username and password and reuse the sessionid cookie in next communication calls.
Hue is based on the Django Web Framework. Django comes with user authentication system. Django uses sessions and middleware to hook the authentication system into request object. Hue uses stock auth form which uses username and password and csrftoken form variables to authenticate.
In this code snippet, we will use well-known python requests library. We will first acquire csrftoken by GET login_url and then create a dictionary of form data which contains username, password and csrftoken and the next_url and another dictionary for header which contains the Referer url and an empty dictionary for the cookies. After the POST request to login_url we will check the reponse code, which should be r.status_code == 200.
Once the request is successful then capture headers and cookies for subsequent requests. Subsequent request.session calls can be made by providing cookies=session.cookies and headers=session.headers.
import requests
def login_djangosite():
next_url = "/"
login_url = "http://localhost:8888/accounts/login?next=/"
session = requests.Session()
r = session.get(login_url)
form_data = dict(username="[your hue username]",password="[your hue password]",
csrfmiddlewaretoken=session.cookies['csrftoken'],next=next_url)
r = session.post(login_url, data=form_data, cookies=dict(), headers=dict(Referer=login_url))
# check if request executed successfully?
print r.status_code
cookies = session.cookies
headers = session.headers
r=session.get('http://localhost:8888/metastore/databases/default/metadata',
cookies=session.cookies, headers=session.headers)
print r.status_code
# check metadata output
print r.text
The metadata API is powering Search and Tagging here and the Query Assistant with Navigator Optimizer Integration.
The backends is pluggable by providing alternative client interfaces:
$.post("/metadata/api/catalog/search_entities_interactive/", {
query_s: ko.mapping.toJSON("*sample"),
sources: ko.mapping.toJSON(["sql", "hdfs", "s3"]),
field_facets: ko.mapping.toJSON([]),
limit: 10
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
Searching for entities with the dummy backend:
$.post("/metadata/api/catalog/search_entities_interactive/", {
query_s: ko.mapping.toJSON("*sample"),
interface: "dummy"
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
$.get("/metadata/api/navigator/find_entity", {
type: "table",
database: "default",
name: "sample_07",
interface: "dummy"
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
Adding/updating a comment with the dummy backend:
$.post("/metadata/api/catalog/update_properties/", {
id: "22",
properties: ko.mapping.toJSON({"description":"Adding a description"}),
interface: "dummy"
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
$.post("/metadata/api/catalog/add_tags/", {
id: "22",
tags: ko.mapping.toJSON(["usage"]),
interface: "dummy"
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
$.post("/metadata/api/catalog/delete_metadata_properties/", {
"id": "32",
"keys": ko.mapping.toJSON(["project", "steward"])
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
$.post("/metadata/api/catalog/delete_metadata_properties/", {
"id": "32",
"keys": ko.mapping.toJSON(["project", "steward"])
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
$.get("/metadata/api/catalog/models/properties/mappings/", function(data) {
console.log(ko.mapping.toJSON(data));
});
$.post("/metadata/api/catalog/namespace/", {
namespace: 'huecatalog'
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
$.post("/metadata/api/catalog/namespace/create/", {
"namespace": "huecatalog",
"description": "my desc"
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
$.post("/metadata/api/catalog/namespace/property/create/", {
"namespace": "huecatalog",
"properties": ko.mapping.toJSON({
"name" : "relatedEntities2",
"displayName" : "Related objects",
"description" : "My desc",
"multiValued" : true,
"maxLength" : 50,
"pattern" : ".*",
"enumValues" : null,
"type" : "TEXT"
})
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
$.post("/metadata/api/catalog/namespace/property/map/", {
"class": "hv_view",
"properties": ko.mapping.toJSON([{
namespace: "huecatalog",
name: "relatedQueries"
}])
}, function(data) {
console.log(ko.mapping.toJSON(data));
});
On the command line:
./build/env/bin/hue shell
If using Cloudera Manager, as a root user launch the shell.
Export the configuration directory:
export HUE_CONF_DIR="/var/run/cloudera-scm-agent/process/`ls -alrt /var/run/cloudera-scm-agent/process | grep HUE_SERVER | tail -1 | awk '{print $9}'`"
echo $HUE_CONF_DIR
> /var/run/cloudera-scm-agent/process/2061-hue-HUE_SERVER
Get the process id:
lsof -i :8888|grep -m1 hue|awk '{ print $2 }'
> 14850
In order to export all Hue's env variables:
for line in `strings /proc/$(lsof -i :8888|grep -m1 hue|awk '{ print $2 }')/environ|egrep -v "^HOME=|^TERM=|^PWD="`;do export $line;done
And finally launch the shell by:
HUE_IGNORE_PASSWORD_SCRIPT_ERRORS=1 /opt/cloudera/parcels/CDH/lib/hue/build/env/bin/hue shell
> ALERT: This appears to be a CM Managed environment
> ALERT: HUE_CONF_DIR must be set when running hue commands in CM Managed environment
> ALERT: Please run 'hue <command> --cm-managed'
Then use the Python code to access a certain user information:
Python 2.7.6 (default, Oct 26 2016, 20:30:19)
Type "copyright", "credits" or "license" for more information.
IPython 5.2.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
from django.contrib.auth.models import User
from desktop.models import Document2
user = User.objects.get(username='demo')
Document2.objects.documents(user=user).count()
In [8]: Document2.objects.documents(user=user).count()
Out[8]: 1167
In [10]: Document2.objects.documents(user=user, perms='own').count()
Out[10]: 1166
In [11]: Document2.objects.documents(user=user, perms='own', include_history=True).count()
Out[11]: 7125
In [12]: Document2.objects.documents(user=user, perms='own', include_history=True, include_trashed=True).count()
Out[12]: 7638
In [13]: Document2.objects.documents(user=user, perms='own', include_history=True, include_trashed=True, include_managed=True).count()
Out[13]: 31408
Out[14]:
(85667L,
{u'desktop.Document': 18524L,
u'desktop.Document2': 31409L,
u'desktop.Document2Permission': 556L,
u'desktop.Document2Permission_groups': 277L,
u'desktop.Document2Permission_users': 0L,
u'desktop.Document2_dependencies': 15087L,
u'desktop.DocumentPermission': 1290L,
u'desktop.DocumentPermission_groups': 0L,
u'desktop.DocumentPermission_users': 0L,
u'desktop.Document_tags': 18524L})
Building a brand new application is more work but is ideal for creating a custom solution.
Note It is now more recommended to integrate external services (e.g. integrate a new SQL Datatase with the Editor, add a new visualization...) to the core Hue APIs instead of building brand new application. This page gives good content in both cases. Feel free to contact the community for advice.
Hue leverages the browser to provide users with an environment for exploring and analyzing data.
Build on top of the Hue SDK to enable your application to interact efficiently with Hadoop and the other Hue services.
By building on top of Hue SDK, you get, out of the box:
This document will orient you with the general structure of Hue and will walk you through adding a new application using the SDK.
Now that we have a high-level overview of what's going on, let's go ahead and create a new installation.
The Hue SDK is available from Github. Releases are missing a few dependencies that could not be included because of licencing issues. Getting the dev environment in detailed in depth in the Developer section.
cd hue
## Build
make apps
## Run
build/env/bin/hue runserver
## Alternative run
build/env/bin/hue supervisor
## Visit http://localhost:8000/ with your web browser.
Run "create_desktop_app" to Set up a New Source Tree
./build/env/bin/hue create_desktop_app calculator
find calculator -type f
calculator/setup.py # distutils setup file
calculator/src/calculator/__init__.py # main src module
calculator/src/calculator/forms.py
calculator/src/calculator/models.py
calculator/src/calculator/settings.py # app metadata setting
calculator/src/calculator/urls.py # url mapping
calculator/src/calculator/views.py # app business logic
calculator/src/calculator/templates/index.mako
calculator/src/calculator/templates/shared_components.mako
# Static resources
calculator/src/static/calculator/art/calculator.png # logo
calculator/src/static/calculator/css/calculator.css
calculator/src/static/calculator/js/calculator.js
As you'll discover if you look at calculator's setup.py, Hue uses a distutils entrypoint to register applications. By installing the calculator package into Hue's python virtual environment, you'll install a new app. The "app_reg.py" tool manages the applications that are installed. Note that in the following example, the value after the "--install" option is the path to the root directory of the application you want to install. In this example, it is a relative path to "/Users/philip/src/hue/calculator".
./build/env/bin/python tools/app_reg/app_reg.py --install calculator --relative-paths
=== Installing app at calculator
Updating registry with calculator (version 0.1)
--- Making egg-info for calculator
Congrats, you've added a new app!
You can now browse the new application.
# If you haven't killed the old process, do so now.
build/env/bin/hue runserver
And then visit http://localhost:8000/ to check it out! You should see the app in the left menu.
Now that your app has been installed, you'll want to customize it.
As you may have guessed, we're going to build a small calculator
application. Edit calculator/src/calculator/templates/index.mako
to include a simple form and a Knockout viewmodel:
<%!from desktop.views import commonheader, commonfooter %>
<%namespace name="shared" file="shared_components.mako" />
%if not is_embeddable:
${commonheader("Calculator", "calculator", user, "100px") | n,unicode}
%endif
## Main body
<div class="container-fluid calculator-components">
<div class="row">
<div class="span6 offset3 margin-top-30 text-center">
<form class="form-inline">
<input type="text" class="input-mini margin-right-10" placeholder="A" data-bind="value: a">
<!-- ko foreach: operations -->
<label class="radio margin-left-5">
<input type="radio" name="op" data-bind="checkedValue: $data, checked: $parent.chosenOperation" /><span data-bind="text: $data"></span>
</label>
<!-- /ko -->
<input type="text" class="input-mini margin-left-10" placeholder="B" data-bind="value: b">
<button class="btn" data-bind="click: calculate">Calculate</button>
</form>
<h2 data-bind="visible: result() !== null">The result is <strong data-bind="text: result"></strong></h2>
</div>
</div>
</div>
<script>
(function() {
var CalculatorViewModel = function () {
var self = this;
self.operations = ko.observableArray(['+', '-', '*', '/']);
self.a = ko.observable();
self.b = ko.observable();
self.chosenOperation = ko.observable('+');
self.result = ko.observable(null);
self.calculate = function () {
var a = parseFloat(self.a());
var b = parseFloat(self.b());
var result = null;
switch (self.chosenOperation()) {
case '+':
result = a + b;
break;
case '-':
result = a - b;
break;
case '*':
result = a * b;
break;
case '/':
result = a / b;
}
self.result(result);
}
};
$(document).ready(function () {
ko.applyBindings(new CalculatorViewModel(), $('.calculator-components')[0]);
});
})();
</script>
%if not is_embeddable:
${commonfooter(messages) | n,unicode}
%endif
The template language here is Mako,
which is flexible and powerful. If you use the ".html" extension, Hue
will render your page using
Django templates
instead.
Note that we use Knockout.js to do the heavy lifting of this app.
Let's edit calculator/src/calculator/views.py to simply render the page:
#!/usr/bin/env python
from desktop.lib.django_util import render
def index(request):
return render('index.mako', request, {
'is_embeddable': request.GET.get('is_embeddable', False),
})
You can now go and try the calculator.