Angular-Python-Salesforce Integrations

Our app purpose is salesforce metadata backup, so we need to implement retrieve metadata logic. In Salesforce we can work with metadata with Metadata API and one of possibility if to retrieve metadata with package.xml file that contains the description of the components we need. But we can simplify it a lot and don't need to use API directly. I plan to use SFDX CLI that offers us a lot of features that we can use with python subprocess lib. More details regarding package.xml you can find here https://developer.salesforce.com/docs/atlas.en-us.api_meta.meta/api_meta/manifest_samples.htm There are different options to build this file. You can do it manually, or use one of automation tools (ex.  https://packagebuilder.herokuapp.com/ ). If you have your package.xml it is the time to add it to the app. We can store package.xml in Task level, so let's add new field to the Task model (with name package_xml ). (You can reference this article to do DB migration -  https://angular-pytho...

Same like we did it for SLDS Input let's create SLDS Select UI component. .../slds-select.component.html <div class="slds-form-element"> <label class="slds-form-element__label" [for]="idPrefix+'_select-id'">{{label}}</label> <div class="slds-form-element__control"> <div class="slds-select_container"> <select [ngModel]="value" (ngModelChange)="onValueChange($event)" [id]="idPrefix+'_select-id'" [name]="name" class="slds-select" > <option [ngValue]="null">-select-</option> <option *ngFor="let op of options" [ngValue]="op.value">{{op.label}}</option> </select> </div> </div> </div> .../slds-sele...

In last article we added Salesforce connection setup logic to our application. Now we need to implement connection test logic and token refresh flow.  Connection test flow is a simple request to Salesforce API with access_token. If we receive valid response it means all works fine. In other case we should notify user about problems with Salesforce integration. I mentioned last time when we setup connection and receive response I save it to DB as is as it contains many useful information. And now we can use part of this response. Here is example of OAuth handshake response: { 'access_token': '00Dxxxxxxxxxx', 'refresh_token': '5Aezzzzzzzzzz', 'signature': '8KFGb34p3vMLhSRHHmJZN3Bux9tlcA1+cvgIACw2SG4=', 'scope': 'refresh_token api', 'instance_url': 'https://ap17.salesforce.com', 'id': 'https://login.salesforce.com/id/00D2x000003v71DEAQ/0052x000001S9ojAAC', 'toke...

Now it's time to join our app with Salesforce. To send calls to Salesforce API we need OAuth2.0 Access Token and I plan to implement Webflow to get it. Also Access Token has short life and to have access to the API we need to refresh it periodically with refresh token. All this should be setup properly in connected app and in oauth handshake process. First of all we need Connected App with its Key and Secret. Create new Developer Org (it's free) and create Connected App same in this screenshot Consumer Key and Consumer Secret should be stored as ENV Variables for later use in our project ... app.config['SFDC_CONNECTED_APP_KEY'] = os.environ['SFDC_CONNECTED_APP_KEY'] app.config['SFDC_CONNECTED_APP_SECRET'] = os.environ['SFDC_CONNECTED_APP_SECRET'] ... OAuth Handhake contains 2 steps: - first you need to redirect user from your app to salesforce login page (Frontend part). - next catch redirect from Salesforce with code and state information t...

In first post ( https://angular-python-salesforce.blogspot.com/2020/04/scheduler-jobs-in-backend.html ) I've described common approach to implement Scheduler Job. Now I'm going to make it works in local environment for development purposes. I've updated cli commands structure and now task group contains "scheduler" and "run" scripts: - scheduler is designed to call from cron and run tasks processing. - run script is designed to process one particular task ./backend/scripts/task.py import os import sys import click from flask import Blueprint from models import db, Task task_scripts = Blueprint('task_scripts', __name__, cli_group='task') def run_task_helper(task): if current_app.config['is_development_mode']:: print('RUN TASK LOCALY') import subprocess subprocess.Popen(['flask', 'task', 'run', str(task.id)]) else: print('RUN TASK IN PRODUCTION ......