Wednesday, November 27, 2019

Dreamforce 2019 Roundup / Summary

The recap for this Dreamforce is going to come across as a bit of a photo journal as I go back through my phones timeline to piece back together what I got up to. Somehow, even on my 6th time attending this conference things still went past at a frantic pace.

Table of Contents

  1. Preconference
  2. Day One
    • Main Keynote
    • Meet the Developers
  3. Day Two
    • Developer Keynote
    • My Presentation
    • True to the Core
    • Dreamfest
  4. Day Three
  5. Day Four

Preconference

A visit to the Salesforce Tower for the Tooling Partner meeting and the Dreamforce speaker party.

Day One - 20th Nov

Main keynote

Meet the Developers

  • This session wasn't recorded, but there were a number of good questions raised. I asked about possible ways to streamline the reporting and monitoring of GACKs.

Platform Cache: Why Implementing Platform Cache Is Just Like Riding a Bike

Day Two - 21st Nov

Developer Keynote

Reducing the Cost of Enforcing CRUD and FLS in the ESAPI

  • Presentation on updates to the ESAPI by Chris and myself

True to the Core

Entity Interfaces

Dreamfest

  • Bands this year were Beck and Fleetwood Mac

Day Three - 22nd

Open conversation about DX

Everything You Need to Know on Apex Debuggers

Quip and Into the blue parties

Day 4 - 23rd

Salesforce Evergreen & Evergreen Functions: Evented Serverless Consumer Apps (2)

See Also

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Friday, October 25, 2019

Dreamforce 2019 Session picks

Here are some of my current picks for Dreamforce 2019 sessions. I'm aiming for a mix of developer related topics in areas I want to learn more about plus anything that sounds informative. It isn't an exhaustive list and there are certainly some other session that I'll be adding.

Most important session that I'm definitely going to attend

I might be biased, but this session is on my must attend list. Yes, that's the same thing I said last year.

This year Chris Peterson and I we will cover how taking up stripping can improve application security!
Think of combining the existing rigid security focus of the Salesforce Enterprise Security API (ESAPI). With the flexibility of the new Security.stripInaccessible() method. Into a hybrid that is both formally secure and stripped down on the CPU usage!
  • Reducing the cost of enforcing CRUD and FLS in the ESAPI
    Reworking the ESAPI to use the new pilot Apex feature to dramatically reduce the cost of checking FLS in Apex. Explore how the new Security.stripInaccessible (pilot) method can be utilized to enforce FLS during CRU[D] operations in the context of the existing ESAPI library.

Keynotes

Meet The *'s

Apex

Customer 360

Salesforce DX

APIs

Platform Events

Platform

Einstein / AI

ISV

Fun

Lightning

  • TODO - Need something to fill out this area to be a well rounded developer. Lightning Roadmap maybe?

See also
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Monday, June 24, 2019

FuseIT SFDC Explorer 3.12.19175.1 - Summer '19

Another roundup of some of the changes to the FuseIT SFDC Explorer since the 3.11.19071.3 release.

Fix for Salesforce breaking the Partner API

One of the primary reasons to use the Partner API in an application is that it is org agnostic. It doesn't really care which org you are connecting to and will adjust to fit the shape of the org it is currently working with. So as long as you don't change the API version in the URL it will keep working indefinitely. Or at least, that is the theory...

Sadly, recent changes around the Real-Time Event Monitoring Beta caused metadata on a JSON field type to leak back into older Salesforce API versions. See Known Issue: DescribeSObject call using the v45.0 Partner API are failing due to a complex type(json) that isn't defined in the WSDL.

By updating to v46.0 of the Partner API we now avoid this issue. In older version it will manifest as a "There is an error in XML document" exception when making the describe call.

View Executed Units from an Apex Log

It's true, the Developer Console already has this information in a sub tab under the Execution Overview. Right now my version just shows the execution count for each Apex method and the basic execution time stats. It if isn't showing up, try turning on the timeline or parse tree first. The debug log will need to show the METHOD_ENTRY and METHOD_EXIT entries, so it the Apex Code logging level will need to be FINE or lower.

Wsdl2Apex : Make it easier to debug out stub objects

Currently, if you try and debug out any of the proxy sObjects that get created for a SOAP callout you end up with a ton of extra *_type_info arrays. While necessary for WebServiceCallout.invoke they have zero usage in debugging the state of the request. They are generally constant and don't really reflect the useful state details.

I'd previously written about using JSON serialization to make repeating a SOAP callout easier. Then it occured to me, why not make all the _type_info arrays transient. Then they won't even appear in the debug output. That's exactly what I've done with the current WSDL2Apex build.

Sadly, transient doesn't work for object.toString(). I did raise an idea for it - Provide a mechanism to exclude object variables/properties from toString()

Reciprocal Logging Event selection and highlighting

There are additional context menu options on a debug log to navigate between related opening and closing events. When applicable you can also select the parent event.

Other changes 3.11

  • Update all API calls to use v46.0 Summer '19
  • Support for monitoring multiple test jobs.
  • ApexTestRunner: New async methods for checking test status. Beta throttled test runner.
  • Fix bug where the 10th column in SOQL results was always being blanked out.
  • Data Export CLI: Allow for parameters to vary in order.
  • Expanded support for deploying different Metadata types.
  • MetadataServiceWrapper - Support for .layout metadata types. Improve creation of package zips - only create the required folders.
  • Option to extract login credentials from a URL formed with "un" and "pw" query string parameters.
  • Wsdl2Apex: Handle SimpleType parameters. Warn if a Union simpleType is encountered.
  • Fix serverURL construction from sfdx cli logins.
  • Handle exceptions with SFDX logins
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Tuesday, April 9, 2019

Trailhead: Deep Learning and Natural Language Processing

Recently I tackled the Deep Learning and Natural Language Processing Trailhead module.

I can best summarize the experience with an image based on the Suppose you have one rabbit meme explanation of arithmetic:

To be fair, the first unit in the module does call out some prerequisites:

This is an advanced topic, and this module assumes you have a basic understanding of machine learning vocabulary, some experience with Python, and at least a little hands-on experience working with machine learning data and algorithms. If you don’t already have that background, you can get yourself up to speed using the following resources.

I personally think I meet those prerequisites. While I don't work with Python day to day the syntax is familiar enough I figured I can fake it till I make it.

The first two units were reasonably straight forward.

However, by the third unit - Apply Deep Learning to Natural Language Processing it started to get complicated. In particular, with the Hands-on Logistic regression questions 6, and to a lesser extent question 7. These both required completing several TODO lines of Python to derive the loss after 100 epochs.

Let's look at the code from the first part of that that needed to be completed in the TODO sections:

The challenge here is, as with all programming, that all the steps need to be completed successfully before you will get the expected answer. Get any of them wrong and things will go pear shaped fast. I could save you the pain of solving this challenge and provide you the full script, but that doesn't really go with the spirit on Trailhead. Instead I'll add some debugging outputs at various points to show the state of the tensors to hopefully make it clearer what should be happening. At least then if things start to go off track you can pick up the problem immediately.

Exercise 6

TODO: Generate 2 clusters of 100 2d vectors, each one distributed normally, using only two calls of randn()

println(classApoints)
tensor([[ 0.3374, -0.1778],
        [-0.3035, -0.5880],
        [ 0.3486,  0.6603],
        [-0.2196, -0.3792],
        #...
        [-0.7952, -0.9178],
        [ 0.4187, -1.1123],
        [ 1.1227,  0.2646],
        [-0.4698,  1.0866],
        [-0.8892,  0.7647]])
assert(classApoints.size() == torch.Size([100, 2]))

println(classBpoints)
tensor([[ 0.4771,  0.7203],
        [-0.0215,  1.0731],
        [-0.1408, -0.5394],
        [-1.2782, -0.8107],
        #...
        [ 1.1051, -0.5454],
        [ 0.1073,  0.8727],
        [-1.2800, -0.4619],
        [ 1.4342, -1.2103],
        [ 1.3834,  0.0324]])
assert(classBpoints.size() == torch.Size([100, 2]))

TODO: Add the vector [1.0,3.0] to the first cluster and [3.0,1.0] to the second.

println(classApoints)
tensor([[ 1.3374,  2.8222],
        [ 0.6965,  2.4120],
        [ 1.3486,  3.6603],
        [ 0.7804,  2.6208],
        #...
        [ 0.2048,  2.0822],
        [ 1.4187,  1.8877],
        [ 2.1227,  3.2646],
        [ 0.5302,  4.0866],
        [ 0.1108,  3.7647]])

println(classBpoints)
tensor([[ 3.4771,  1.7203],
        [ 2.9785,  2.0731],
        [ 2.8592,  0.4606],
        [ 1.7218,  0.1893],
        #...
        [ 4.1051,  0.4546],
        [ 3.1073,  1.8727],
        [ 1.7200,  0.5381],
        [ 4.4342, -0.2103],
        [ 4.3834,  1.0324]])

TODO: Concatenate these two clusters along dimension 0 so that the points distributed around [1.0, 3.0] all come first

println(inputs)
tensor([[ 1.3374,  2.8222],
        [ 0.6965,  2.4120],
        [ 1.3486,  3.6603],
        [ 0.7804,  2.6208],
        #...
        [ 4.1051,  0.4546],
        [ 3.1073,  1.8727],
        [ 1.7200,  0.5381],
        [ 4.4342, -0.2103],
        [ 4.3834,  1.0324]])

println(inputs.size())
torch.Size([200, 2])

TODO: Create a tensor of target values, 0 for points for the first cluster and # 1 for the points in the second cluster.

println(classA)
tensor([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
         0,  0])

println(classA.size())
torch.Size([100])

println(classB)
tensor([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
         1,  1])

println(classB.size())
torch.Size([100])

# TODO: Initialize a Linear layer to output scores for each class given the 2d examples

println(model)
Linear(in_features=2, out_features=2, bias=True)

# TODO: Define your loss function

Here you need to decide if you are going to use:

  1. MSE Loss for Regression
  2. NLLLoss for Classification
  3. CrossEntropyLoss for Classification

Worst case here, you could just try them each one at a time until you get an answer that matches the expected answers in the Trailhead challenge.

Finishing up exercise 6

After that the rest should fall into place fairly easily based on the prior examples.

Exercise 7: Logistic Regression with a Neural Network

Firstly, the instructions when I completed this included this:

# forward takes self and x as input
#         passes x through linear_to_hidden, then a tanh activation function,
#         and then hidden_to_linear and returns the output

I suspect that should actually be "and then hidden_to_output and returns the output".

The torch.tanh function is required for forward.

Initialize your new network to have in_size 2, hidden_size 6, and out_size 2

You are going to use the NeuralNet class that you just completed defining here.

Define your loss function

As with exercise 6, you can just try the 3 example loss functions to find a good fit for the expected answers.

Finishing up exercise 7

Most of the other parts for this question all fall into place based on the prior example.

Results

It would be accurate to say that this last unit took me way longer than the 90 minutes that Trailhead indicated. But dammit I earned those 100 measly points.

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Monday, April 1, 2019

Trailhead Electric Imp plus plus

After completing the the Trailhead Electric Imp project I was left wanting an excuse to access a number of other features of the imp001 hardware. Namely the accelerometer and pressure sensor. Yet there didn't seem many opportunities for a static fridge to fully utilize them.

While I was experimenting with this back in May 2018 the #BeABuilder challenge happened and I got sidetracked using the hardware for that instead. Somewhere in that process I never got around to hitting publish on this blog post, so here we are a year or so latter with a better late than never post.

Firstly I expanded the data model in Salesforce so I'd have fields to receive the additional data. This included the acceleration and pressure readings.

Out of interest I tried getting the IoT Orchestration Traffic into a Lightning component. I didn't have much luck at the time and it didn't really liked to be iframed in.

Hunting through the Salesforce Labs I found the salesforce-iot-toolkit. This provided some great visualizations via platform events as the data came in from the fridge. It directly monitored the platform events to drive the graphs. I found I needed to use the source based version rather than the app exchange version, which was missing a number of features.

Dramatic reenactment of sensor usage

Modified Agent Nut Source

A few points of interest:

  1. My source was the version before they added proper refresh token support to handle expired sessions. So you probably don't want my version of getStoredCredentials()
  2. Added #require statements to access additional sensors.
  3. Dropped the READING_INTERVAL_SEC down to 1 second to improve the accelerometer data.
  4. Retrieved the X,Y, and Z axis acceleration values.
  5. Retrieved the pressure sensor and additional temperature reading from that sensor as well.
  6. Set the RGB color based on the current orientation indicated by the acceleration.
  7. The LIS2DH12 sensor needed a non-default I2C address of 0x32.

Modified Salesforce Platform Event Trigger Code

Nothing fancy here. Just collect the additional properties the map them to the new custom fields.

trigger SmartFridgeReadingReceived on Smart_Fridge_Reading__e (after insert) {
    List records = new List(); 
    for (Smart_Fridge_Reading__e event : Trigger.New) {
        SmartFridge__c record = new SmartFridge__c();
        record.deviceId__c = event.deviceId__c;
        record.temperature__c = event.temperature__c;
        record.humidity__c = event.humidity__c;
        record.door__c = event.door__c;
        record.ts__c = event.ts__c;
        record.accX__c = event.accX__c;
        record.accY__c = event.accY__c;
        record.accZ__c = event.accZ__c;
        record.pressure__c = event.pressure__c;
        record.temp2__c = event.temp2__c;
        records.add(record); }
    insert records;
}
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Tuesday, March 12, 2019

FuseIT SFDC Explorer 3.11.19071.3 - Spring '19

Another roundup of some of the changes to the FuseIT SFDC Explorer since the 3.9.18190.1 release.

Bypass sObject types based of suffix to speed up metadata retrieval

With the update to v45.0 of the APIs there is now describe metadata coming back for change data capture with the suffix __ChangeEvent. This can be useful, except every custom object also gets a change event. If you have a large number of custom objects it can quickly become overwhelming.

I've added a filter option to the sObject metadata browser that defaults to only showing __c, __mdt, and __e. Other options can be turned on if you want to see them. A refresh is required for the changes to take affect.

Expanded Asynchronous Test Results view

After the async tests have finished running you will set the total duration by Apex class and method. This can be useful for identifying which test methods contribute the most to the overall test run time.

Expose the logging levels in the menu

You can alter the current logging levels for the current DebugLevel and then use "Update Current TraceFlag" to apply them to Salesforce.

The "Disable Logging" menu option will remove the current TraceFlag, which will stop further logging from occurring.

Wsdl2Apex

There have been a number of changes to the Wsdl2Apex processing to support more edge cases:

  1. Allow for schema imports via schema imports (nested imports). Includes checks for circular references.
  2. If the complex type can't be found in the current schema search the imported schema for it.
  3. If a SimpleType has a restriction that isn't primitive then search the underlying types for the primitive type that can be converted to Apex.
  4. Detect repeated imported schema target namespaces
  5. Use common code to create Apex class names from complex types.
  6. Add generated Apex comments to classes that extend a base type.
  7. Attempt to compile the XmlSchema to resolve groups so they can be automatically expanded.
  8. Include warning messages on web methods where the return type can't be resolved from the namespace.

Include Apex class name in code coverage breakdown

The Apex class name and test method name will be included with the class coverage breakdown details.

JWT Authentication for Salesforce sessions

It is now possible to use the OAuth 2.0 JWT Bearer Token flow to authenticate with Salesforce. I'll expand on the steps to do this shortly, which will include configuring the connected app and registering the X509 Certificate.

Other changes 3.11

  • Middle click to hide tab. Improve tab hiding.
  • Improve support for using the ToolingAPI in the EntityViewer.
  • If directly updating a nullable int in the SOQL results treat it as such (rather than a string)
  • Format a VF_PAGE_MESSAGE in the log timeline
  • Format display of ApexResult and HeapDump from the Tooling API SOQL Queries.
  • Allow for a CultureInfo.DateTimeFormat.TimeSeparator other than ":". Force the SOQL building code to use the InvarientCulture so the required format is applied.
  • Update core SalesforceSession to use v45.0 (Spring 19) APIs
  • For the Apex Log Stack, include the Soql Execute Count
  • Option to run the most recent async test cases again after a successful metadata deployment.
  • EntityViewer - Allow for ToolingAPI entities.
  • Event Log Viewer. Only show the first 4000 rows of data.
  • Log Timeline control. Show icons for Test.startTest() and Test.stopTest() if they are detected.
  • Handle failure to query for recent apex test runs.
  • When generating anonymous Apex to get the current Session ID include the SalesforceBaseUrl
  • Code Generation - If generating without custom fields, exclude picklist values containing "__". These typically represent relationships to custom sObjects
    Detect and skip duplicate picklist value entries.
    Fix the generated properties for *Picklist to set based on the enum value rather than the description attribute.
    Detect references to sObjects that aren't exposed via the metadata.
  • Regenerate sObjects (plus associated services and data sources) for API v44.0.
  • CodeGeneration - If configured, exclude relationships to custom fields.
    • Don't generate types for __ChangeEvent, __History, __Share
  • CodeGeneration - generate "extends" for Apex classes extending other classes.
    • Expand details for WebServiceCallout.invoke with comments.
  • ApexClass (for generation) - Track if the Apex classes extends a base class.
  • FieldService - When extracting child elements from a query result skip nodes that aren't XmlElements.
  • ToolingServiceWrapper - Add describeGlobalCached() that caches describeGlobal() to improve performance.
  • SalesforceSession - If an exception occurs with a WebRequest include the complete URL in the Exception.Data
  • Internal caching of the Tooling API DescribeGlobalResult Metadata
  • Auto-size columns in SOSL & single page SOQL results
  • Improve usage of direct Csv loading. Support bulk load for larger operations. Allow the primary sObject type to be defined.
  • Change the Apex log layout with the treeview between horizontal and vertical.
  • Define logging levels via common menu, Handle hitting the maximum debug log count
  • Capture SSL/TLS config issues and provide help link to dedicated help page.
  • When checking metadata deployment successes, highlight changed components vs. unchanged.
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