Salesforce Custom Metadata vs. Custom Objects: Where Should I Put My Configuration and Validation Settings?

(Version française en dessous)

Yesterday, a colleague asked me the difference between "custom metadata" and "custom objects" for storing "configuration and validation" information in Salesforce.

My answer is:

• Custom metadata if you can. Salesforce says so.
  And it survives sandbox refreshes!
• Data tables (custom objects) if you truly need it to be part of your data
  (e.g. people will want to include it, hyperlinked to other data, in reports).

When I wrote an Apex trigger that determined which User should "own" each "Admissions Application" in our org, I ended up splitting the configuration data in two.

Here's why:

Custom Metadata

Used for a table that helps Apex code ask, "Who does this?"

• key: the code for a graduate degree we offer
• value: a username responsible for recruiting people to that graduate degree

Data (Custom Objects)

Used for a list of U.S. states, their full spellings, and fields with "lookup" links to the User table indicating who does what work for that state.

• There was strong demand to generate native Salesforce reports per User showing all the states they're responsible for and the high schools in those states. It made sense to ensure that the "high school" table could have a "lookup" field to this "states" table.
• Custom metadata can have "master-detail" and "lookup" relational links to other custom metadata, but it can't link to ordinary data.
  • This meant we needed to store the the "states" as data (custom objects), even though we would also be using it as configuration information for Apex triggers.

---

UI & Editability Considerations

I'll let you in on a dirty little secret about another reason I used data tables ("custom objects") for most of the Undergraduate Admissions counselor assignment configuration data.

Undergraduate Admissions tweaks their "recruiter assignment" business rules several times a year. The real-world configuration data for their business is a lot more complex than a simple "list of U.S. states."

I'll be honest: Salesforce's user interfaces for hand-editing, viewing, and bulk-editing data are a lot more end-user-friendly than their user interfaces for the same operations on custom metadata, and setting granular "edit" permissions is a lot more sysadmin-friendly for data. I wanted to make sure end users, not sysadmins, were the ones whose time was spent tweaking the configuration several times a year!

I was thoroughly scolded at Dreamforce. Actually, I stand by my decision to use "data" tables, because there truly is a business need to report on the configuration data alongside normal data. But ... building your own user interfaces (typically using Lightning Components) to help end users edit custom metadata was a big theme. You have been warned:

1. Dan Appleman's "Build Awesome Configuration Pages with Lightning Components & Custom Metadata"
2. Gustavo Melendez & Krystian Charubin's "Crafting Flexible APIs in Apex Using Custom Metadata"
3. Beth Breisness & Randi Wilson's "Create Guided User Experiences for Managing ISV Custom Metadata"

---

---

🇫🇷 - en français

Une collègue, peu familière avec Salesforce, m'a demandée quelle était la différence entre « métadonnées personnalisées » et « objets personnalisés » pour stocker des données de configuration et de validation dans une organisation Salesforce.

J'ai répondu:

• Selon Salesforce, s'il est possible, utilisez des métadonnées personnalisées.
  (Elles survivent l'actualisation d'un environnement sandbox.)

• Stockez vos informations aux tables de bases de données (objets personnalisés) s'il faut créer des relations aux autres objets de votre organisation.
  (Par exemple, s'il y a des utilisateurs qui vont générer des rapports sur ces données.)

Mon approche

Lorsque j'ai écrit un déclencheur Apex pour attribuer les enregistrements de notre objet « demande d'admission » aux utilisateurs corrects, j'ai fini par utiliser les deux approches:

Métadonnées personnalisées

J'ai choisi une type de métadonnées personnalisées pour stocker des données simples concernant « qui gère chaque spécialisation ? »

• clé: le code qui indique un diplôme d'études supérieures que l'université offre
• valeur: un nom d'utilisateur chargé de recruter des étudiants au diplôme

Objets personnalisés

J'ai choisi un objet personalisé pour stocker une liste d'états américains (abréviation et nom), avec quelques champs de référence indiquant les utilisateurs qui y gèrent divers aspects du recrutement d'étudiants en license.

• Nos utilisateurs ont besoin de générer des rapports, regroupés par utilisateur, avec les états qu'ils gèrent et les lycées qui y sont situés. Donc il fallait pouvoir créer une relation de l'objet « lycée » à l'objet « état ».
• On peut créer des relations entre métadonnées personnalisées, mais pas entre les types de métadonnées personnalisées et les objets personalisés.
  • Donc j'ai choisi un modèle « objet personnalisé » pour stocker les informations à propos des états, même si ces informations servent aussi comme données de configuration pour un déclencheur Apex.

Considérations au sujet de l'interface utilisateur et au contrôle d'accès

Je vais vous révéler un secret : ce n'est pas qu‘à propos des besoins relationnels que j'ai choisi le modèle « objet personnalisé » pour les informations à propos de la gestion du recrutement en licence.

Le service admission en license changent les règles de « qui gère quoi » plusieurs fois par an. Et les données de configuration sont, en réalité, beaucoup plus complexes qu'une seule liste d'états.

Actuellement, les interfaces utilisateur de Salesforce pour voir et enregistrer des données aux objets personnalisés sont supérieures à celles pour les métadonnées personnalisées – surtout si l'on est utilisateur ordinaire. Le contrôle d'accès, aussi, est plus facile à gérer pour les administrateurs. Je voulais m'assurer que ça soit des utilisateurs ordinaires qui font plusieurs fois par an cette saisie de données, et non pas les administrateurs !

On m'a fait honte de cette position à Dreamforce. Ben, je maintiens ma choix d'objet personnalisé pour raisons de relations entre objets. Mais … on a beaucoup répété qu'il est de bonne pratique de construire ses propres interfaces utilisateur pour autoriser des utilisateurs ordinaires à modifier les métadonnées personnalisées en toute sécurité. Je vous aurai prévenus !

Vidéos pertinentes de Dreamforce

1. « Build Awesome Configuration Pages with Lightning Components & Custom Metadata » de Dan Appleman
2. « Crafting Flexible APIs in Apex Using Custom Metadata » de Gustavo Melendez & Krystian Charubin
3. « Create Guided User Experiences for Managing ISV Custom Metadata » de Beth Breisness & Randi Wilson

Python for Salesforce Administrators - "In Both Tables?" Dummy-Check Script

I was asked how to use Python to "dummy check" that every transaction in a Salesforce log also appeared in a payment processor's log, and vice-versa.

The following are a few simple scripts that do just that. We're going to work with our "sample1" & "sample2" files, which have some people who overlap by name and email, and others who don't.
In your case, instead of "name & email" you might want to match on "transaction ID" or "product ID, customer ID, and timestamp." Whatever appears in both datasets.

Note that "Albert Howard" is going to show up as "not in the other file" for both files because he has a different email address in each file.

(Prep work: First, make sure you've created a ".CSV" file like the one described in "First Scripts". I called mine "sample1.csv". Second, make sure you've created a 2nd ".CSV" file like the one described in "Combining Multiple Tables". As in that post, in each example, we'll overwrite the contents of a piece of Python code saved to our hard drive as "script.py" and run the entire script.)

---

Code for one big "error log" output file

Code:

import pandas
pandas.set_option('expand_frame_repr', False)
df1 = pandas.read_csv('C:\\tempexamples\\sample1.csv', dtype=object)
df2 = pandas.read_csv('C:\\tempexamples\\sample2.csv', dtype=object)

df1matchfields=['First','Last','Email']
df2matchfields=['FirstName','LastName','Em']

mergedf = df1.merge(df2, left_on=df1matchfields, right_on=df2matchfields, how='outer', indicator=True)

uniquedf = mergedf[mergedf['_merge'].str.endswith('_only')]
uniquedf.to_csv('C:\\tempexamples\\output.csv', index=0, quoting=1)

Output:

"Id","First","Last","Email","Company","PersonId","FirstName","LastName","Em","FavoriteFood","_merge"
"5829","Jimmy","Buffet","jb@example.com","RCA","","","","","","left_only"
"2894","Shirley","Chisholm","sc@example.com","United States Congress","","","","","","left_only"
"30829","Cesar","Chavez","cc@example.com","United Farm Workers","","","","","","left_only"
"724","Albert","Howard","ah@example.com","Imperial College of Science","","","","","","left_only"
"","","","","","983mv","Shirley","Temple","st@example.com","Lollipops","right_only"
"","","","","","k28fo","Donald","Duck","dd@example.com","Pancakes","right_only"
"","","","","","8xi","Albert","Howard","ahotherem@example.com","Potatoes","right_only"

---

Code for two separate "error log" output files

Code:

import pandas
pandas.set_option('expand_frame_repr', False)
df1 = pandas.read_csv('C:\\tempexamples\\sample1.csv', dtype=object)
df2 = pandas.read_csv('C:\\tempexamples\\sample2.csv', dtype=object)

df1matchfields=['First','Last','Email']
df2matchfields=['FirstName','LastName','Em']

mergedf = df1.merge(df2, left_on=df1matchfields, right_on=df2matchfields, how='outer', indicator=True)

uniquedf1 = mergedf[mergedf['_merge']=='left_only'][df1.columns]
uniquedf2 = mergedf[mergedf['_merge']=='right_only'][df2.columns]
uniquedf1.to_csv('C:\\tempexamples\\output1.csv', index=0, quoting=1)
uniquedf2.to_csv('C:\\tempexamples\\output2.csv', index=0, quoting=1)

Output 1:

"Id","First","Last","Email","Company"
"5829","Jimmy","Buffet","jb@example.com","RCA"
"2894","Shirley","Chisholm","sc@example.com","United States Congress"
"30829","Cesar","Chavez","cc@example.com","United Farm Workers"
"724","Albert","Howard","ah@example.com","Imperial College of Science"

Output 2:

"PersonId","FirstName","LastName","Em","FavoriteFood"
"983mv","Shirley","Temple","st@example.com","Lollipops"
"k28fo","Donald","Duck","dd@example.com","Pancakes"
"8xi","Albert","Howard","ahotherem@example.com","Potatoes"

---

---

The code above only works if you have "Pandas version 0.17" or greater installed.

Pandas is up to version "0.23" as of this blog post, so that's a pretty safe bet.

Except that the "no admin rights, even with old installations of Windows" programming environment I walked you through installing is many years old, and it comes with "Pandas version 0.16." So here are two scripts that do the equivalent job in older versions, if you don't feel like upgrading everything.

"Old Pandas" code for one big "error log" output file

Code:

import pandas
pandas.set_option('expand_frame_repr', False)
df1 = pandas.read_csv('C:\\tempexamples\\sample1.csv', dtype=object)
df2 = pandas.read_csv('C:\\tempexamples\\sample2.csv', dtype=object)

df1matchfields=['First','Last','Email']
df2matchfields=['FirstName','LastName','Em']

mergedf = df1.assign(SourceDF='df1').merge(df2.assign(SourceDF='df2'), left_on=['First','Last','Email'], right_on=['FirstName','LastName','Em'], how='outer')
uniquedf = mergedf[mergedf['SourceDF_x'].isnull() | mergedf['SourceDF_y'].isnull()]

uniquedf.to_csv('C:\\tempexamples\\output.csv', index=0, quoting=1)

Output:

"Id","First","Last","Email","Company","SourceDF_x","PersonId","FirstName","LastName","Em","FavoriteFood","SourceDF_y"
"5829","Jimmy","Buffet","jb@example.com","RCA","df1","","","","","",""
"2894","Shirley","Chisholm","sc@example.com","United States Congress","df1","","","","","",""
"30829","Cesar","Chavez","cc@example.com","United Farm Workers","df1","","","","","",""
"724","Albert","Howard","ah@example.com","Imperial College of Science","df1","","","","","",""
"","","","","","","983mv","Shirley","Temple","st@example.com","Lollipops","df2"
"","","","","","","k28fo","Donald","Duck","dd@example.com","Pancakes","df2"
"","","","","","","8xi","Albert","Howard","ahotherem@example.com","Potatoes","df2"

"Old Pandas" code for two separate "error log" output files

Code:

import pandas
pandas.set_option('expand_frame_repr', False)
df1 = pandas.read_csv('C:\\tempexamples\\sample1.csv', dtype=object)
df2 = pandas.read_csv('C:\\tempexamples\\sample2.csv', dtype=object)
 
df1matchfields=['First','Last','Email']
df2matchfields=['FirstName','LastName','Em']
 
mergedf = df1.assign(SourceDF='df1').merge(df2.assign(SourceDF='df2'), left_on=['First','Last','Email'], right_on=['FirstName','LastName','Em'], how='outer')
 
uniquedf1 = mergedf[mergedf['SourceDF_y'].isnull()][df1.columns]
uniquedf2 = mergedf[mergedf['SourceDF_x'].isnull()][df2.columns]
uniquedf1.to_csv('C:\\tempexamples\\output1.csv', index=0, quoting=1)
uniquedf2.to_csv('C:\\tempexamples\\output2.csv', index=0, quoting=1)

Output 1:

"Id","First","Last","Email","Company"
"5829","Jimmy","Buffet","jb@example.com","RCA"
"2894","Shirley","Chisholm","sc@example.com","United States Congress"
"30829","Cesar","Chavez","cc@example.com","United Farm Workers"
"724","Albert","Howard","ah@example.com","Imperial College of Science"

Output 2:

"PersonId","FirstName","LastName","Em","FavoriteFood"
"983mv","Shirley","Temple","st@example.com","Lollipops"
"k28fo","Donald","Duck","dd@example.com","Pancakes"
"8xi","Albert","Howard","ahotherem@example.com","Potatoes"

---

Table of Contents

• Introduction & Table of Contents
• Before You Begin Programming
• Installing Python 3 on Windows
• First Scripts - Import/Export CSVs, Filter Out Rows
• Rename/Drop/Add Columns
• Recognizing Pandas DataFrames vs. Series (Important!)
• Fancier Row-Filtering and Data-Editing
• Combining Multiple Tables
• Filtering Rows By Maximum Date Per Group
• Introduction to XML and JSON

Python for Salesforce Administrators - A "Combining Multiple Tables" (VLOOKUP) Example with the "Simple Salesforce" plugin

Today I used Python to facilitate a quick UPDATE of 60 Contact records in Salesforce.

In Salesforce, there were 60-ish Contact records where I had instructed an end user to populate a field called "RegSys_External_Id_B__c" with the "ID" that they could see while logged into their registration system.

However, what I really wanted to do was make sure that "RegSys_External_Id_A__c" was populated with the "real external ID" (that my end users couldn't see, but that held everything together in the back-end of their registration system) of each person they had hand-matched.

I asked them to simply fill it in with dummy values, "XXXX01," "XXXX02," "XXXX03," etc. so that I could easily find the records they had already "hand-checked" later.

I then used Python as follows:

First, I used Python's "Simple Salesforce" plugin to log into our org and download the 60 Contact records into Python's "Pandas" plugin. I saved that data into a variable "cstofixdf" (as in "Contacts To Fix DataFrame"). Note that the data that comes back from Simple Salesforce has to have "['records']" appended to it to become something that "Pandas" can read and turn into a "DataFrame" (2-dimensional table). Also, I drop the "attributes" column that comes back with "Simple Salesforce" data because I think it's ugly and takes up unnecessary space on my screen when I preview what my "DataFrame" looks like. Anyway, I also printed out all the "RegSys_External_Id_B__c" values as a comma-separated, quote-surrounded list onto my screen so that I could easily copy them to my clipboard.

import pandas
pandas.set_option('expand_frame_repr', False)

from simple_salesforce import Salesforce
sf = Salesforce(username='myemail@example.com', password='mypassword', security_token='mysalesforcesecuritytoken')

cs = sf.query("SELECT Id, RegSys_External_Id_A__c, RegSys_External_Id_B__c FROM Contact WHERE RegSys_External_Id_B__c <> NULL AND RegSys_External_Id_A__c LIKE 'X%'")['records']
cstofixdf = pandas.DataFrame(cs)
cstofixdf.drop('attributes', axis='columns', inplace=True)

print(list(cstofixdf['Flatbridge_Student_ID__c']))

My output looked like this -- only with more like 60 items in the list instead of 4:

['8294', '29842', '8482', '2081']

Once I had copied the list between the square-brackets onto my clipboard, I pasted it into the tool by which I query the back-end of my end user's course registration system. It uses normal "SQL," so my code looked like this:

SELECT IdA, IdB
FROM ExternalIdMappingTable
WHERE IdB IN ('8294', '29842', '8482', '2081')

I exported the 60 rows of output from THAT database onto my hard drive at "c:\examples\otherdb.csv"

I then ran the following code -- note that I've "commented out" the "print(list(...))" line of my code with a "#" since I no longer need to print that information to my screen.

import pandas
pandas.set_option('expand_frame_repr', False)

from simple_salesforce import Salesforce
sf = Salesforce(username='myemail@example.com', password='mypassword', security_token='mysalesforcesecuritytoken')

cs = sf.query("SELECT Id, RegSys_External_Id_A__c, RegSys_External_Id_B__c FROM Contact WHERE RegSys_External_Id_B__c <> NULL AND RegSys_External_Id_A__c LIKE 'X%'")['records']
cstofixdf = pandas.DataFrame(cs)
cstofixdf.drop('attributes', axis='columns', inplace=True)

#print(list(cstofixdf['Flatbridge_Student_ID__c']))

regdf = pandas.read_csv('c:\\examples\\otherdb.csv', dtype=object)

mergedf = cstofixdf.merge(regdf, how='inner', left_on='RegSys_External_Id_B__c', right_on='IdB')
mergedf.drop(['IdB', 'RegSys_External_Id_A__c', 'RegSys_External_Id_B__c'], axis='columns', inplace=True)
mergedf.rename(columns={'IdA':'RegSys_External_Id_A__c'}, inplace=True)

mergedf.to_csv('c:\\examples\\uploadme.csv', index=0, quoting=1)

The last 5 lines of code are the new part.

First, I import "otherdb.csv"

Next, I "inner-merge" (like a VLOOKUP, with "inner" specifying that the "ID B" must show up in both datasets) the two "DataFrames" -- the one I downloaded from Salesforce and the one I just loaded in from CSV, and save the output into a new "DataFrame" called "mergedf."

Not shown here, I did a bit of quality-checking after I wrote the line of code that did the merge, before getting rid of "excess" columns. For example, I did:

print(mergedf)

and

print(len(cstofixdf))

and

print(len(regdf))

and

print(len(mergedf))

I double-checked that all the data-sets were the same length (there shouldn't have been any duplicates or missing values), and I hand-skimmed the results of "mergedf" to make sure I hadn't written the ".merge()" code wrong.

After that, I get rid of a few columns I won't want in my output CSV file: the old "A" & "B" values from Salesforce (I just needed the Salesforce ID), as well as the "B" value from the SQL download.

Then I renamed the SQL-downloaded "IdA" column to "RegSys_External_Id_A__c" to be explicit that this was the Salesforce Contact-table field I intended to update this data into.

Finally, I dumped the "mergedf" "DataFrame" to CSV on my hard drive.

From there, I was just a Data Load away from having proper data in Salesforce!

---

P.S. You may note that some of my code reads like this:

mergedf.drop(['IdB', 'RegSys_External_Id_A__c', 'RegSys_External_Id_B__c'], axis='columns', inplace=True)

Whereas older examples in my blog read like this:

mergedf = mergedf.drop(['IdB', 'RegSys_External_Id_A__c', 'RegSys_External_Id_B__c'], axis=1)

They do the same thing.

Many Pandas functions produce an altered copy of your "DataFrame" or "Series," rather than altering the actual data you have stored into a given "variable." Therefore, if you actually want to store the "altered" version into that "variable name," you have to explicitly do so. This is actually pretty common in programming -- you'll see things like "x = x + 1" all the time.

The "inplace=True" option on certain Pandas operations is a shorthand that keeps you from having to type all that. Sometimes, it gets buggy on me, and I didn't want your first lessons to fail, so I wrote my introductory examples the "long way."

You may also notice a difference between "axis='columns'" and "axis=1". They do the same thing. "1" is faster to type than "'columns'", but obviously "'columns'" is easier to read when you come back and need to figure out what you did later. Your choice!

---

Table of Contents

• Introduction & Table of Contents
• Before You Begin Programming
• Installing Python 3 on Windows
• First Scripts - Import/Export CSVs, Filter Out Rows
• Rename/Drop/Add Columns
• Recognizing Pandas DataFrames vs. Series (Important!)
• Fancier Row-Filtering and Data-Editing
• Combining Multiple Tables
• Filtering Rows By Maximum Date Per Group
• Introduction to XML and JSON

Trigger/Process/Workflow or Scheduled Script/Tool?

I often get asked to write triggers to detect the existence of certain types of data in Salesforce and, when such data exists, make a modification to some other data.

The only problem is, writing "a trigger" isn't always easy to do in a well-normalized database (that is, a database that leverages master-detail and lookup relationships to avoid redundant data entry).

Take, for example, an architecture where "App Document" records have a master-detail relationship to a parent "Admissions Application" and the "Admissions Application," in turn, has a master-detail relationship to a parent "Contact" record.

The other day, I was asked to automatically flip the "Status" to "Waived" on any "App Document" records that meet the following criteria:

• The record is of type "English Proficiency" and is in blank/"Required" status
• The record's parent "Application" has an "application citizenship category" of "International" and a "level" of "Undergraduate"
• The record's grandparent "Contact" has a "foreign citizenship country" of "United Kingdom," "Ireland," "Canada," "Australia," "New Zealand," (etc.)

This is ill-suited to a Trigger/Process/Workflow because I would actually need THREE "insert"/"update" automations, each with relatively redundant code:

1. one for all Contacts
   (in case the citizenship country changes ... then go looking for appropriate "child" apps and "grandchild" documents)
2. one for all Applications
   (in case the "application citizenship category" or "level" changes ... then double-check the parent Contact's citizenship and go looking for "child" documents)
3. and one for all App Documents
   (check the parent & grandparent details and change self if appropriate)

Yikes! That's a lot of redundant trigger code, and some of those operations aren't very efficient against Salesforce governor limits.

Especially since just one SOQL query can easily fetch the ID of all "App Document" records whose "Status" needs to be set to "Waived":

SELECT Id
FROM AdmDocument__c
WHERE Type__c='English Proficiency'
AND (Status__c = NULL OR Status__c = 'Required')
AND Application__r.Level__r.Name='Undergraduate'
AND Application__r.Citizenship_Category__c='International'
AND Application.Contact__r.Foreign_Citizenship_Country__c IN 
   ('Australia','Canada','Ireland','New Zealand','United Kingdom')

When someone asks you to "write a trigger" or "write a process builder" or "write a workflow" to automate data migration inside of Salesforce, be sure to ask yourself, "How many tables' values changing could cause a scenario to arise that would make this data need to be modified as requested?"

If the answer is "2 or more," and especially if it's "3 or more," think hard about whether "every 15 minutes" / "daily" is frequent enough for your end users to see "automatic fixes," and whether a SOQL query could extract the IDs of the records that need to be changed.

If you can come up with a SOQL query that represents the "problem records," you should be able to use a schedulable ETL tool or scheduled Apex to "extract and load back" with much lower overhead against governor limits than triggers/processes/workflows would incur. Your future self will also thank you when someone (inevitably) asks for a change to your script.

Re-Parented Child Objects Don't Fire Triggers In Parent Record Merges

Today I learned, via the Salesforce forums:

• In Salesforce, if you merge 2 "Contact" records, and if you have a bunch of "child"-table records pointing to the "about-to-be-deleted" Contact record as a foreign key, Salesforce will automatically change the foreign key cross-reference in the "child" records to the ID of the "surviving" Contact record, and it will update those child records' "last modified" timestamp, BUT it will not allow any "UPDATE" triggers from those child tables to fire.
• The only thing you can latch onto to detect that a "Contact merge" has just happened is the "AFTER DELETE" trigger-context against the "Contact" table (you can detect a "deleted but merged" Contact in that context from an ordinary "deleted" Contact because it has a "MasterRecordId" value).
  You can't "detect" that a "child" record has just been "re-parented" in the context of a "merge."
• Annoyingly, once you're that far into the merge ("after contact delete" trigger context), you can no longer tell which "child" records were pointing to the old "parent" because that's long since been "fixed" by Salesforce.
  All you can see is which "child" records are now cross-referencing the surviving "Contact" – which includes all "child" records that were already pointing to the surviving "Contact" in the first place.
• Finally, if you use that knowledge to kick off DML against surviving Contacts' "child" records, you must be careful, because "after Contact delete" is still in the context of the initial Contact merge, and you have to avoid loops (Salesforce will yell at you if you don't).
  If your DML against the "child" records kicks off triggers that result in more DML against one of the very Contacts involved in the merge, you need to make sure you kick off the DML against the "child" records in a "@future" context to force a brand new "execution context" (roughly like a transaction, but a little different in some ways – see Dan Appleman's Advanced Apex book).

Loving my "Just Push Play" Run-Specified-Tests Script

Power tools are fun.

THIS PYTHON SCRIPT HAS CHANGED MY LIFE.

Whipped it out twice this week trying to get code written under tight deadlines.

Having a nearly-instant, low-clicking-around answer to, "Would it probably work in production?" so I can easily watch how every single change to just 1 line of code behaves, before doing a full-on deploy of code that "seems to work" with a proper deployment tool and "Run All Tests," has been AMAZING.

Dreamforce 2017 Lessons Learned & Takeaways

Lucky me, I got to attend Dreamforce again. Here's a summary.

Misc

1. "Machine learning" tools are getting point-and-click enough that feeding hundreds of pieces of historical student data (e.g. "days before schoolyear applied," "GPA," "time between inquiry & admission," "time of year applied," etc.) into them and letting a computer look for trends (e.g. "likely to submit a confirming deposit?") isn't all that out of reach. Drag-and-drop tools are coming into prominence that make dimensionality reduction & linear regression problems like this intuitive & easy to play with.
    
2. There's something called "macros" I should look into more. At a glance, not sure it's anything I can't do a lot faster w/ DemandTools, but worth a glance.
    

Org configuration change management

1. One of the concepts behind "DX scratch orgs'" mere 7-day lifespan is to encourage you to put the result of all your coding time & effort into a source code version control system.
    
2. Two guiding philosophies behind development with "DX" are that: 1) you can make all the changes you like by hand in them -- just be sure to use the DX command-line tool to pull those changes down to your computer so you can promptly get them into your version control system, and 2) you should never again be making any changes to configuration in production / normal-sandbox orgs by hand (only via deploying from a copy of whatever you did in a "DX scratch org" into one of those orgs via something like the DX command-line tool).
    
3. On a related note, I still haven't heard a really admin-geared lecture about DX and "please don't mess around with things by hand in production/staging sandboxes, and please don't deploy anything via change set."
    
4. Gearset looks like an absolute miracle for shops where those kinds of "no change sets, please" disciplined version-control practices aren't yet in place (e.g. shops transitioning from Salesforce as an "interesting side project" with a no-code solo admin). It's an enterprise-class-(seeming?) tool that simply lets you log into Salesforce and a cloud Git repository and say, "Hey, synchronize my entire org's metadata once a day -- kthxbye." It also seems to be able to facilitate rollbacks. In a small shop where communication lines are open enough that once you know what changed since things were working, sending out a few emails to figure out who changed it and if they could kindly fix it is easy, this "daily snapshot" seems well worth $3600/year. Even after you have "version control" in place -- when it's this easy, what's the harm in a "side repository" of "CYA snapshots?" (Such Git-stored snapshots could also, potentially, be mined by local scripts that transform them into things like SQL commands for dropping & rebuilding tables & SOQL queries for a local daily cache of Salesforce data.) And that's without all the other things Gearset does. (Please stay just $3600/year, Gearset!) Getting a tour from a staffer makes their product look even cooler than their summary on their web site -- apparently they can give you one over the web. *drools* I want.
    

Lightning Experience

1. Although there's a lot that's still missing from "Lightning Experience" (e.g. "bucketing" & "formulas" in native reporting tools), by the Winter '19 edition, there might be enough LE-only functionality in Salesforce's native reporting tools to justify a switch--for example, something called "joined reports," field-to-field filtering, subfoldering, easier permissions management, and bookmarking of reports. (So far, we haven't found it worthwhile.) I'm hoping to get a lot of data integration projects done & stable over the next few months so I have time to play around with Lightning Experience in a sandbox.
    
2. Apparently, having a really nice "Lightning Experience" home page for a user doesn't automatically translate into a really nice mobile version of the same page -- that has to be set up separately.
    
3. There's a thing called the "Lightning Data Service" that might make Lightning Components require almost as little code as Visualforce for simple "put this SOQL query's results on a page" use cases, unlike last year when I took a hands-on training. It also looks like it has something to do w/ getting Lightning Components on the same page to auto-update when data from the database that one of them just edited changes.
    

SOQL query performance & data models

1. OpportunityContactRole isn't anywhere close to becoming a first-class object (a table against which you can write triggers). *sigh*
    
2. The "affiliations" package might be replaceable with the "multiple accounts for contacts" feature & a few triggers similar to ones from the "affiliations" package. That said, the "affiliations" package doesn't really seem to be hurting anything. But Pardot and other vendors are working to get on board with the native version of the feature, so it's worth keeping an eye on.
    
3. Having a single "Generic Account" record for people whose account you don't know the value of slows down most SOQL queries. It improves performance to give everyone their own accounts named after them, as in the HEDA model.
    
4. Salesforce objects/tables aren't really tables. They're sets of tables (e.g. indexes are a table, fields are a table, etc.). That's why Salesforce doesn't call them "tables" and impacts SOQL query performance. We don't seem to have hit issues yet, but there's also something Salesforce can do with their back-end data model and materialized views for storing your org's data called skinny tables if you ask -- it can speed up SOQL queries.
    
5. Reminder: "=" is fast in a query that can leverage a highly selective index, "!=" is not -- "!=" always requires a table scan. Query optimization 101, but so easy to overlook when you get busy and don't write queries daily where you have to worry about it.
    
6. There's a technology called "big objects," paired with "asynchronous SOQL," coming along, but it doesn't yet look useful for us. You've got to be really sure about data not changing, because right now, you can't even drop such a table once it's created.
    

Other programming-related notes

1. Stay on top of the "metadata in Apex" project, but overall, it's on purpose that you can't change your schema & change your data via the same programming language / execution environment, so don't get too excited.
    
2. Hopefully coming within a year: the ability to easily make Apex respect CRUD permissions & field-level security permissions (when you don't want your triggers to "play God").
    
3. If you're a web developer, you can write your Visualforce/Apex to include a Visualforce Boolean variable as a toggle that switches whether Visualforce pages rely on Salesforce-hosted "static resources" (e.g. CSS, JavaScript) or "localhost"-hosted ones. This can make testing a lot easier & less fragile & faster than actually trying to update the contents of Salesforce-hosted "static resources" when you're not yet even sure if they're correct. Talk to Jon Schleicher for more info about how.
    
4. Via the reporting API (warning: the JSON to parse "is a doozy"), you can wrap Reports from Salesforce's native reporting tool in Lightning Components and therefore make them easy to embed in other pages. (Natively, only dashboards can be drag-and-dropped into Lightning Components.)
    
5. Google Docs are a form of "cloud-hosted info storage with an API" -- which means that you can include info from them in custom-programmed user interface elements like Lightning Components. Possibly overlooked form of integration of results from a cheap-and-easy aggregate-reporting tool (spreadsheets) with business users' main daily work environment (Salesforce).
    
6. Personal goal: blast through data integration projects so I can free up time to work on Trailhead modules and learn to be better able to leverage a lot of the technologies I learned about "tips & tricks" for. There's a lot out there that I'd like to do, but at 8 hours apiece, one really has to work to free up the time. But they're so well-written.