--- title: "SDK" date: 2019-03-13T18:28:09-07:00 draft: false weight: 3 --- # Autocompletes Close to 100% of [Hive and Impala grammar](https://github.com/cloudera/hue/tree/master/desktop/core/src/desktop/js/parse/jison) are supported which makes the autocomplete extremly powerful. Other languages defaults to a generic SQL grammar. See the [SQL parser](/developer/parsers/) for more details. Integrating [Apache Calcite](https://calcite.apache.org/docs/reference.html), [ZetaSql](https://github.com/google/zetasql)... would make SQL users even happier with a lot more Databases! # Connectors They provide integration with any SQL database or Job execution engine. Here is a list of the [existing connectors](https://github.com/cloudera/hue/tree/master/desktop/libs/notebook/src/notebook/connectors). Connectors are pluggable and new engines can be added. Feel free to contact the [community](https://discourse.gethue.com/c/developer-sdk-api). ## Editor ### SQL #### SqlAlchemy [SqlAlchemy](https://www.sqlalchemy.org) is the prefered way if the HiveServer2 API is not supported by the database. The implementation is in [`sql_alchemy.py`](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/sql_alchemy.py) and is depends on the repective SqlAlchemy dialects. #### Jdbc With the JDBC proxy, query editor with any JDBC compatible database. View the [JDBC connector](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/jdbc.py). **Note** In the long term, SqlAlchemy is prefered as more "Python native". #### Solr SQL [Solr connector](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/solr.py). #### Custom 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](https://github.com/cloudera/hue/tree/master/desktop/libs/notebook/src/notebook/connectors). Each connector API subclasses the [Base API](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/base.py) and must implement the methods defined within; refer to the [JdbcApi](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/jdbc.py) or [RdbmsApi](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/rdbms.py) for representative examples. ### Jobs #### Spark / Livy Based on the [Livy REST API](https://livy.incubator.apache.org/docs/latest/rest-api.html) * [Notebook connector](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/spark_shell.py) * PySpark * Scala * Spark SQL * [Batch connector](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/spark_batch.py) #### Oozie MapReduce, Pig, Java, Shell, Sqoop, DistCp [Oozie connector](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/connectors/oozie_batch.py). ## Job Browser 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](https://github.com/cloudera/hue/tree/master/apps/jobbrowser/src/jobbrowser/apis). ## File Browser Various storage systems like Hadoop HDFS, AWS S3 and Azure [ADLS](https://issues.cloudera.org/browse/HUE-7248) can be interacted with. The [`fsmanager.py`](https://github.com/cloudera/hue/blob/master/desktop/core/src/desktop/lib/fsmanager.py) is the main router to each API. **Note** Ceph can be used via the S3 browser. ## Dashboard [Dashboards](/user/querying/#dashboards) are generic and support Apache Solr and SQL: The API was influenced by Solr but is now generic: [Dashboard API](https://github.com/cloudera/hue/blob/master/desktop/libs/dashboard/src/dashboard/dashboard_api.py) ### SQL [SQL API](https://github.com/cloudera/hue/blob/master/desktop/libs/notebook/src/notebook/dashboard_api.py) Implementations: * [Impala API](https://github.com/cloudera/hue/blob/master/apps/impala/src/impala/dashboard_api.py) * [Hive API](https://github.com/cloudera/hue/blob/master/apps/beeswax/src/beeswax/dashboard_api.py) ### Apache Solr [Solr Dashboard API](https://github.com/cloudera/hue/blob/master/apps/search/src/search/dashboard_api.py) ### Elastic Search A connector similar to Solr or SQL Alchemy binding would need to be developed [HUE-7828](https://issues.cloudera.org/browse/HUE-7828) # Public API Hue can be accessed directly via a Django Python Shell or by its REST API. ## REST REST APIs are not all public yet but this is work in progress in [HUE-1450](https://issues.cloudera.org/browse/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. ### Quickstart 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 ### SQL Querying ### SQL Risk Optimization ### Data Browsing ### Workflow scheduling ### Data Catalog The [metadata API](https://github.com/cloudera/hue/tree/master/desktop/libs/metadata) is powering [Search and Tagging here](http://gethue.com/improved-sql-exploration-in-hue-4-3/) and the [Query Assistant with Navigator Optimizer Integration](http://gethue.com/hue-4-sql-editor-improvements/). The backends is pluggable by providing alternative [client interfaces](https://github.com/cloudera/hue/tree/master/desktop/libs/metadata/src/metadata/catalog): * Cloudera Navigator (default) * Apache Atlas ([HUE-8749](https://issues.cloudera.org/browse/HUE-8749)) * Dummy (skeleton for integrating new catalogs) #### Searching for entities $.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)); }); #### Finding an entity in order to get its id $.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)); }); #### Adding a tag with the dummy backend $.post("/metadata/api/catalog/add_tags/", { id: "22", tags: ko.mapping.toJSON(["usage"]), interface: "dummy" }, function(data) { console.log(ko.mapping.toJSON(data)); }); #### Deleting a key/value property $.post("/metadata/api/catalog/delete_metadata_properties/", { "id": "32", "keys": ko.mapping.toJSON(["project", "steward"]) }, function(data) { console.log(ko.mapping.toJSON(data)); }); #### Deleting a key/value property $.post("/metadata/api/catalog/delete_metadata_properties/", { "id": "32", "keys": ko.mapping.toJSON(["project", "steward"]) }, function(data) { console.log(ko.mapping.toJSON(data)); }); #### Getting the model mapping $.get("/metadata/api/catalog/models/properties/mappings/", function(data) { console.log(ko.mapping.toJSON(data)); }); #### Getting a namespace $.post("/metadata/api/catalog/namespace/", { namespace: 'huecatalog' }, function(data) { console.log(ko.mapping.toJSON(data)); }); #### Creating a namespace $.post("/metadata/api/catalog/namespace/create/", { "namespace": "huecatalog", "description": "my desc" }, function(data) { console.log(ko.mapping.toJSON(data)); }); #### Creating a namespace property $.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)); }); #### Map a namespace property to a class $.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)); }); ## Python * [Hue API: Execute some builtin or shell commands](http://gethue.com/hue-api-execute-some-builtin-commands/). * [How to manage the Hue database with the shell](http://gethue.com/how-to-manage-the-hue-database-with-the-shell/). ### How to count documents of a user 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 --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}) # Applications 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. ## Overview 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: + Configuration Management + Hadoop interoperability + Supervision of subprocesses + A collaborative UI + Basic user administration and authorization This document will orient you with the general structure of Hue and will walk you through adding a new application using the SDK. ## Creating an application Now that we have a high-level overview of what's going on, let's go ahead and create a new installation. ### Download, Unpack, Build The Hue SDK is available from [Github](http://github.com/cloudera/hue). 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](/developer/development/). 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. ### Creating the app 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
Some apps are blacklisted on certain versions of CDH (such as the 'Spark' app) due to certain incompatibilities, which prevent them loading from in Hue. Check the hue.ini 'app_blacklist' parameter for details.
### Install SDK Application 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
If you'd like to customize the build process, you can modify (or even complete rewrite) your own `Makefile`, as long as it supports the set of required targets. Please see `Makefile.sdk` for the required targets and their semantics.
Congrats, you've added a new app!
What was that all about? virtualenv is a way to isolate python environments in your system, and isolate incompatible versions of dependencies. Hue uses the system python, and that's about all. It installs its own versions of dependencies. Entry Points are a way for packages to optionally hook up with other packages.
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. ### Customizing 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

The result is

%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.