Andy in the Cloud

From BBC Basic to Force.com and beyond…


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Streaming Debug Logs to your console

Debug logs are a key tool in triaging and profiling on the Lightning Platform (formerly Force.com) both in development and production. While the Apex Interactive Debugger provides an interactive experience, sometimes you want to monitor, parse or filter logs. Maybe you are reproducing a bug and are watching for a certain SOQL query or method being executed or we just want filter output in different ways.

taillog.png

A recent addition to the DX command line from Chris Wall is the ability to effectively stream debug logs from any org connected to DX to the command line console. This is similar to the experience in Developer Console logs pane, but is effectively opening the logs and dumping them out as they are produced on the server for you automatically.

sfdx force:apex:log:tail

You can install the Salesforce DX CLI here. Note that you do not need to have a DX project to use this command.

In the following command line example, I have piped the output to another command (grep) that filters the output to show only USER_DEBUG log lines.

sfdx force:apex:log:tail --color | grep USER_DEBUG 

Pictures do not really do it justice, so here is a short demo video!

The command works against any org you have connected to the DX CLI, including production and sandbox orgs. However, if you run it from the same folder as a DX project it will use the currently configured default user/scratch org for that project.

Adding a bit of color to your debug logs!

The –color parameter used above enables some basic color highlighting for method, constructor, variable assignments etc.

colordebuglog.png

You can also customize your own colors by setting the SFDX_APEX_LOG_COLOR_MAP environment variable to an absolute file path to a JSON file per the format shown below.

{
    CONSTRUCTOR_: 'magenta',
    EXCEPTION_: 'red',
    FATAL_: 'red',
    METHOD_: 'blue',
    SOQL_: 'yellow',
    USER_: 'green',
    VARIABLE_: 'cyan'
}

Power to the pipe!

One of the most exciting features for me is the ability to pipe debug logs. Maybe you want to parse out some information to profile how many SOQL statements have been used or aggregate timestamp values (the bit in brackets after the time!) to do some performance profiling… I am looking forward to seeing what folks do with this, please share!

Anything else?

The –debugLevel command is optional but allows you to define your own debug level by inserting records into the TraceFlag object (via the DX CLI command force:data:record:create). Finally, you can run the command with the –help parameter to get the latest help.

Usage: sfdx force:apex:log:tail [-c] [-d ] [-s] [-u ] [--json] [--loglevel ] 

start debug logging and display logs

Flags:

 -c, --color                          colorize noteworthy log lines

 -d, --debuglevel DEBUGLEVEL          debug level for trace flag

 -s, --skiptraceflag                  skip trace flag setup

 -u, --targetusername TARGETUSERNAME  username or alias for the target org;

                                      overrides default target org

 --json                               format output as json

 --loglevel LOGLEVEL                  logging level for this command invocation

                                      (error*,trace,debug,info,warn,fatal)



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User Notifications with the Utility Bar API

utilityprogressIn this blog, I want to highlight a couple of great UI features provided by the Utility Bar in Lightning Experience. These are relatively new and accessed only via the Utility Bar API, so are not immediately accessible. This blog is based on code and material I prepared for Dreamforce 2017. However, I did not have time to dig into the code during that session so this blog provides that opportunity. My session also covered other cool features in Lightning Experience, such as the amazing App Console mode!

Enabling and Understanding the Utility Bar API
The utility bar API is enabled at a component level though it does have access to the whole utility bar. You can specify the lightning:utilityBarAPI component in any component, regardless if its in the utility bar or not. This component will not display anything but it does have a very useful selection of methods!

<lightning:utilityBarAPI aura:id="utilitybar"/>

In your component code you simple access it like any other component.

var utilityAPI = cmp.find("utilitybar");

Once you have access to an instance of the component you can call any of its methods. All methods take a utilityId parameter. Although if you call it within the context of a component running in the utility bar you can omit this parameter and the API will discover it for you. All the methods take a single JavaScript object with properties representing the parameters to the method.

utilityAPI.setPanelHeaderLabel({ label: "My Label" });

One interesting design aspect of these methods is they do not respond immediately, all responses are returned via a callback. To do this the API uses the JavaScript Promises pattern. Fortunately, its a pretty easy convention to pick up and use. It is worth taking the time to understand, it has fast become defacto callback approach.

Providing Notifications

notificationdemo

There are many occasions that you want to notify the user of something that’s happened since they last logged in or during login as a result of some background process. The setUtilityHighlighted method is a good way to drive such notifications.

You can, of course, evaluate on initialize of your component, but it’s worth considering using Platform Events, it’s really easy to send them from your Apex code or Process Builder and you can easily integrate my Streaming API component to respond to the event. The code below is a very simple isolated example using browser timers, but it helps illustrate the API and give you a basis to build one.

<lightning:button 
   class="slds-m-around_medium" 
   label="{! v.readNotification ? 'Mark as Read' : 'Wait' }" 
   onclick="{!c.demoNotifications}"/>
<aura:if isTrue="{!v.readNotification}">
   <ui:message title="Confirmation"severity="info">
      This is a confirmation message.</ui:message>
</aura:if>
    demoNotifications: function (cmp, event) {
		var utilityAPI = cmp.find("utilitybar"); 
        var readNotification = cmp.get('v.readNotification');
        if(readNotification == true) {
			utilityAPI.setUtilityHighlighted({ highlighted : false });                        
            cmp.set('v.readNotification', false);
        } else {
            utilityAPI.minimizeUtility();                            
            setTimeout($A.getCallback(function () {
                utilityAPI.setUtilityHighlighted({ highlighted : true });            
				cmp.set('v.readNotification', true);
            }), 3000);                    
        }
    }, 

Providing Progress Updates

progressdemo

By using a combination of setUtilityLabel and setUtilityIcon you can create an eye-catching progress updating effect. This sample is a pretty simple browser timer based example. However, you could again use Platform Events to send events as part of a Batch Apex execution to update on progress or just poll the AsyncApexJob object.

 <lightning:button 
    class="slds-m-around_medium" 
    label="{! v.isProgressing ? 'Stop' : 'Start' }" 
    onclick="{!c.demoProgressIndicator}"/>
 <lightning:progressBar 
    value="{! v.progress }" size="large" />
demoProgressIndicator: function (cmp, event) {
    var utilityAPI = cmp.find("utilitybar"); 
    if (cmp.get('v.isProgressing')) {
        // stop
        cmp.set('v.isProgressing', false);
        cmp.set('v.progress', 0);
        cmp.set('v.progressToggleIcon', false);
        clearInterval(cmp._interval);
        utilityAPI.setUtilityLabel({ label : 'Utility Bar API Demo' });                    
        utilityAPI.setUtilityIcon({ icon : 'thunder' } );                                    
    } else {
        // start
        cmp.set('v.isProgressing', true);
        utilityAPI.minimizeUtility();        
        cmp._interval = setInterval($A.getCallback(function () {
            var progresToggleIcon = 
               cmp.get('v.progressToggleIcon') == true ? false : true;
            var progress = cmp.get('v.progress');
            cmp.set('v.progress', progress === 100 ? 0 : progress + 1);
            cmp.set('v.progressToggleIcon', progresToggleIcon);
            utilityAPI.setUtilityLabel(
                { label : 'Utility Bar API Demo (' + progress + '%)' });        
            utilityAPI.setUtilityIcon(
                { icon : progresToggleIcon == true ? 'thunder' : 'spinner' });
        }), 400);
    }
}

Summary

There is still plenty to dig into in the code samples from the session. You can also deploy the sample code into an org and try out some of the other interactive API demos. Enjoy!

utildemo


6 Comments

Adding User Feedback to your Package

featurefeedbackFeature Management has become GA in Winter’18 and with it the ability to have finer control and visibility over how users of your package consume its functionality. It provides an Apex API and corresponding objects in your License Management Org (LMO). With these objects, you can switch features on and off or even extend the capacity or duration of existing ones. For features that you simply want to monitor adoption on you can also track adoption and send metrics back to your LMO as well. This is all done within the platform, no HTTP callouts are required.

This blog focuses on a tracking and metrics use case and presents a Lightning Component to allow users to activate a given feature and after that contribute to an aggregated scoring of that feature sent back to you the package owner!

Consider this scenario. The latest Widgets App package has added a new feature to its Widgets Overview tab. The ability to analyze widgets! Using Feature Management they can now track who activates this feature and ratings their customers give it.

featureactivate

Trying it Out: You can find the full source code for this sample app and component here. You can also easily give it a try via the handy Deploy to SFDX button! Do not worry though, it will not write back to your production org, it’s totally isolated in this mode. Unless you choose to package it up and associate your LMA etc.

withoutmanagefeatures.pngYour customers may not want every user to have the ability to activate and deactivate features as shown above. The sample application associated with this blog includes a Manage Featurescustom permission that controls this ability. Users without this custom permission assigned only see the feedback portion of the component. If the feature has not been activated a message is displayed informing the user to consult with their admin.

The component only sends the average score per feature per customer back to you. However, a user’s individual score is captured in the subscriber org via custom objects. Enabling you to pick up the phone and dig a bit deeper together with customers showing particularly low or high scores.

featureadoption.png

The following shows what you get back in your License Management Org LMO (typically your companies production org). By using standard reporting you can easily see what features have been activated, their average score and how many users contributed to the rating.

featurereport

NOTE: The average score returned to the production org is represented as a value from 1 to 50.

So let’s now dig into the code and the architecture of this solution. Firstly we need some feature parameters, then some corresponding Apex API to read and write to them. You define the parameters via XML under the/featureParameters folder.

FeatureParam

NOTE: In this blog, we are dealing with parameter values that flow from your customers org back into your production org, hence the SubscriberToLmo usage. Also keep in mind that per the docs, updates to your LMO may take up to 24 hrs.

You can also create package feature parameters via a new tab on the Package Details page in your packaging org. However, if you are using Scratch Orgs you define them via metadata directly. The Feedback component needs you to define three parameters per feature. To support our scenario, the following parameters are defined. WidgetAnalysis (Boolean), WidgetAnalysisCount (Integer), and WidgetAnalysisScore (Integer).

featuremanagementapi

NOTE: When your code sets parameter values, mixed DML rules apply. So typically you would set these via an async context such as @future or a queueable.

To use the Feedback Component included in the sample code for this blog, you set two attributes, the name of your feature parameter and an attribute that the rest of your component code can use to conditionally display your new feature! The following shows how in the above scenario, the new Widget Analysis component has used c:featureFeedback component to activate the feature, get a user rating and control the display of the new graph to the user.

feedbackcomponent.png

Take a deeper look at the Feature Feedback Component Apex controller to see the above Feature Management API in action, as well as how it aggregates the scores.

Back over in your License Management Org, the above report was based on one of the four new custom objects provided by a Salesforce package. The Feature Parameter object represents the package parameters defined above. The Feature Parameter Date, Feature Parameter Integer, and Feature Parameter Boolean are the values set via code running in the subscriber org associated with the License record. Per the documentation, it can take up to 24hrs for these to update.

featureparamsschema

Summary

Knowing more about how your customers consume applications you build on the platform is a big part of customer success and your own. You should also check out the Usage Metrics feature if you have not already.

There is a quite a bit more to Feature Management to explore. Such as controlling feature activation exclusively from your side, useful for pilots or enabling paid features. Of special note is the ability tohide related Custom Objects until features are activated. This is certainly a welcome feature to your customer’s admins when working with large packages since they only see in Object Manager objects relating to activated features.

Finally, I recommend you read the excellent documentation in the ISVForce guide very carefully before going ahead and trying this out in your main package. Since this feature integrates with your live production data. The documentation recommends to try this out in a temporary package first and discuss rollout with your legal team.

P.S. You can also use the FeatureManagement.checkPermission method to check if the user has been assigned a given custom permission without SOQL. Very useful!


24 Comments

Swagger / Open API + Salesforce = LIKE

In my previous blog i covered an exciting new integration tool from Salesforce, which consumes API’s that have a descriptor (or schema) associated with them. External Services allows point and click integration with API’s. The ability for Salesforce to consume API’s complying with API schema standards is a pretty huge step forward. Extending its ability to integrate with ease in a way that is in-keeping with its low barrier to entry development and clicks not code mantra.

swaggerlike

At the time of writing my previous blog, only Interagent schema was supported by External Services. However as of the Winter’18 release this is no longer the case. In this blog i will explore the more widely adopted Swagger / Open API 2.0 standard, using Node.js and Heroku and External Services. As bonus topic, i will also touch on using Swagger Code Generator with Apex!

One of the many benefits of supporting the Swagger / Open API standard is the ability to generate documentation for it. The following screenshot shows the API schema on the left and generated documentation on the right. What is also very cool about this, is the Try this operation button. Give it a try for yourself now!

asciiartswagger

oai

Whats the difference between Swagger and Open API  2.0? This was a question i asked myself and thought i would cover the answer here. Basically as at, Swagger v2.0, there is no difference, the Open API Initiative is a rebranding, born out of the huge adoption Swagger has seen since its creation. This move means its future is more formalised and seems to have more meaningful name. You can read more about this amazing story here.

Choosing your methodology for API development

The schema shown above might look a bit scary and you might well want to just get writing code and think about the schema when your ready to share your API. This is certainly supported and there are some tools that support generation of the schema via JSDoc comments in your code or via your joi schema here (useful for existing API’s).

However to really embrace an API first strategy in your development team i feel you should start with the requirements and thus the schema first. This allows others in your team or the intended recipients to review the API before its been developed and even test it out with stub implementations. In my research i was thus drawn to Swagger Node, a tool set, donated by ApiGee, that embraces API-design-first. Read more pros and cons here. It is also the formal Node.js implementation associated with Swagger.

The following describes the development process of API-design-first.

overview2

(ref: Swagger Node README)

Developing Open API’s with “Swagger Node” 

Swagger Node is very easy to get started with and is well documented here. It supports the full API-design-first development process show in the diagram above. The editor (also shown above) is really useful for getting used to writing schemas and the UI is dynamically refreshed, including errors.

The overall Node.js project is still pretty simple (GitHub repo here), now consisting of three files. The schema is edited in YAML file format (translated to JSON when served up to tools). The schema for the ASCIIArt service now looks like the following and is pretty self describing. For further documentation on Swagger / Open API 2.0 see here.

https://createasciiart.herokuapp.com/schema/
swagger: "2.0"
info:
  version: "1.0.0"
  title: AsciiArt Service
# during dev, should point to your local machine
host: localhost:3000
# basePath prefixes all resource paths 
basePath: /
# 
schemes:
  # tip: remove http to make production-grade
  - http
  - https
# format of bodies a client can send (Content-Type)
consumes:
  - application/json
# format of the responses to the client (Accepts)
produces:
  - application/json
paths:
  /asciiart:
    # binds a127 app logic to a route
    x-swagger-router-controller: asciiart
    post:
      description: Returns ASCIIArt to the caller
      # used as the method name of the controller
      operationId: asciiart
      consumes:
        - application/json
      parameters:
        - in: body
          name: body
          description: Message to convert to ASCIIArt
          schema:
            type: object
            required: 
              - message
            properties:
              message:
                type: string
      responses:
        "200":
          description: Success
          schema:
            # a pointer to a definition
            $ref: "#/definitions/ASCIIArtResponse"
  /schema:
    x-swagger-pipe: swagger_raw
# complex objects have schema definitions
definitions:
  ASCIIArtResponse:
    required:
      - art
    properties:
      art:
        type: string

The entry point of the Node.js app, the server.js file now looks like this…

'use strict';

var SwaggerExpress = require('swagger-express-mw');
var app = require('express')();
module.exports = app; // for testing
var config = {
  appRoot: __dirname // required config
};

SwaggerExpress.create(config, function(err, swaggerExpress) {
  if (err) { throw err; }
  // install middleware for swagger ui
  app.use(swaggerExpress.runner.swaggerTools.swaggerUi());
  // install middleware for swagger routing
  swaggerExpress.register(app);
  var port = process.env.PORT || 3000;
  app.listen(port);
});

Note: I changed the Node.js web server framework from hapi (used in my previous blog) to express. As I could not get the Swagger UI to integrate with hapi.

The code implementing the API has been moved to its asciiart.js file.

var figlet = require('figlet');

function asciiart(request, response) {
    // Call figlet to generate the ASCII Art and return it!
    const msg = request.body.message;
    figlet(msg, function(err, data) {
        response.json({ art: data});
    });
}

module.exports = {
    asciiart: asciiart
};

Note: There is no parameter validation code written here, the Swagger Node module dynamically implements parameter validation for you (based on what you define in the schema) before the request reaches your code! It also validates your responses.

To access the documentation simply use the path /docs. The documentation is generated automatically, no need to manage static HTML files. I have hosted my sample AsciiArt service in Heroku so you can try it by clicking the link below.

https://createasciiart.herokuapp.com/docs/

swaggerui

Consuming Swagger API’s with External Services

The process described in my earlier blog for using the above API via External Services has not changed. External Services automatically recognises Swagger API’s.

externalservicesasciiart

NOTE: There is a small bug that prevents the callout if the basePath is specified as root in the schema. Thus this has been commented out in the deployed version of the schema for now. Salesforce will likely have fixed this by the time you read this.

Swagger Tools

  • SwaggerToolsSwagger Editor, the interactive editor shown in the first screenshot of this blog.
  • Swagger Code Generator, creates server stubs and clients for implementing and calling Swagger enabled API’s.
  • Swagger UI, the browser based UI for generating documentation. You can call this from the command line and upload the static HTML files or use frameworks like the one used in this blog to generated it on the fly.

Can we use Swagger to call or implement API’s authored in Apex?

Swagger Tools are available on a number of platforms, including recently added support for Apex clients. This gives you another option to consume API’s directly in Apex. Its not clear if this is going to a better route than consuming the classes generated by External Services, i suspect it might have some pros and cons tbh. Time will tell!

Meanwhile i did run the Swagger Code Generator for Apex and got this…

public class SwagDefaultApi {
    SwagClient client;

    public SwagDefaultApi(SwagClient client) {
        this.client = client;
    }

    public SwagDefaultApi() {
        this.client = new SwagClient();
    }

    public SwagClient getClient() {
        return this.client;
    }

    /**
     *
     * Returns ASCIIArt to the caller
     * @param body Message to convert to ASCIIArt (optional)
     * @return SwagASCIIArtResponse
     * @throws Swagger.ApiException if fails to make API call
     */
    public SwagASCIIArtResponse asciiart(Map<String, Object> params) {
        List<Swagger.Param> query = new List<Swagger.Param>();
        List<Swagger.Param> form = new List<Swagger.Param>();

        return (SwagASCIIArtResponse) client.invoke(
            'POST', '/asciiart',
            (SwagBody) params.get('body'),
            query, form,
            new Map<String, Object>(),
            new Map<String, Object>(),
            new List<String>{ 'application/json' },
            new List<String>{ 'application/json' },
            new List<String>(),
            SwagASCIIArtResponse.class
        );
    }
}

The code is also generated in a Salesforce DX compliant format, very cool!


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Highlights from TrailheaDX 2017

IMG_2857.JPGThis was my first TrailheaDX and what an event it was! With my Field Guide in hand i set out into the wilderness! In this blog i’ll share some of my highlights, thoughts and links to the latest resources. Many of the newly announced things you can actually get your hands on now which is amazing!

Overall the event felt well organized, if a little frantic at times. With smaller sessions of 30 minutes each, often 20 mins after intros and questions, each was bite sized, but quite well tuned with demos and code samples being shown.

SalesforceDX, Salesforce announced the public beta of this new technology aimed at improving the developer experience on the platform. SalesforceDX consist of several modules that will be extended further over time. Salesforce has done a great job at providing a wealth of Trailhead resources to get you started.

Einstein, Since its announcement, myself and other developers have been waiting to get access to more custom tools and API’s, well now that wait is well and truly over. As per my previous blogs we’ve had the Einstein Vision API for a little while now. Announced at the event where no less than three other new Einstein related tools and API’s.

  • Einstein Discovery. Salesforce demonstrated a very slick looking tool that allows you to point and click your way through to analyzing various data sets, including those obtained from your custom objects! They have provided a Trailhead module on it here and i plan on digging in! Pricing and further info is here.
  • Einstein Sentiment API. Allows you to interpret text strings for terms that indicate if its a positive, neutral or negative statement / sentiment. This can be used to scan case comments, forum posts, feedback, twitter posts etc in an automated way and respond or be alerted accordingly to what is being said.
  • Einstein Intent API.  Allows you to interpret text strings for meanings, such as instructions or requests. Routing case comments or even implementing bots that can help automate or propose actions to be taken without human interpretation.
  • Einstein Object Detection API. Is an extension of the Einstein Vision API, that allows for individual items in a picture to be identified. For example a pile of items on a coffee table, such as a mug, magazine, laptop or pot plant! Each can then be recognized and classified to build up more intel on whats in the picture for further processing and analysis.
  • PredictionIO on Heroku. Finally, if you want to go below the declarative tools or intentional simplified Einstein API’s, you can build your own machine learning models using Heroku and the PredictionIO build pack!

Platform Events. These allow messages to be published and subscribed to using a new object known as an Event object, suffixed with __e. Once created you can use a new Apex API’s or REST API’s to send messages and use either Apex Triggers or Streaming API to receive them. There is also a new Process Builder action or Flow element to send messages. You can keep your messages within Force.com or use them to integrate between other cloud platforms or the browser. The possibilities are quite endless here, aysnc processing, inter app comms, logging, ui notifications…. i’m sure myself and other bloggers will be exploring them in the coming months!

External Services. If you find a cool API you want to consume in Force.com you currently have to write some code. No longer! If you have a schema that describes that API you use the External Services wizard to generate some Apex code that will call out to the API. Whats special about this, is the Apex code is accessible via Process Builder and Flow. Making clicks not code API integration possible. Its an excellent way to integrate with complementary services you or others might develop on platforms such as Heroku. I have just submitted a session to Dreamforce 2017 to explore this further, fingers crossed it gets selected! You can read more about it here in the meantime.

Sadly i did have to make a key decision to focus on the above topics and not so much on Lightning. I heard from other attendees these sessions where excellent and i did catch a brief sight of dynamic component rendering in Lightning App Builder, very cool!

Thanks Salesforce for filling my blog backlog for the next year or so! 😉

 

 


7 Comments

Working with Apex Mocks Matchers and Unit Of Work

The Apex Mocks framework gained a new feature recently, namely Matchers. This new feature means that we can start verifying what records and their fields values are being passed to a mocked Unit Of Work more reliably and with a greater level of detail.

Since the Unit Of Work deals primarily with SObject types this does present some challenges to the default behaviour of Apex Mocks. Stephen Willcock‘s excellent blog points out the reasons behind this with some great examples. In addition prior to the matchers functionality, you could not verify your interest in a specific field value of a record, passed to registerDirty for example.

So first consider the following test code that does not use matchers.

	@IsTest
	private static void callingApplyDiscountShouldCalcDiscountAndRegisterDirty()
	{
		// Create mocks
		fflib_ApexMocks mocks = new fflib_ApexMocks();
		fflib_ISObjectUnitOfWork uowMock = new fflib_SObjectMocks.SObjectUnitOfWork(mocks);

		// Given
		Opportunity opp = new Opportunity(
			Id = fflib_IDGenerator.generate(Opportunity.SObjectType),
			Name = 'Test Opportunity',
			StageName = 'Open',
			Amount = 1000,
			CloseDate = System.today());
		Application.UnitOfWork.setMock(new List<Opportunity> { opp };);

		// When
		IOpportunities opps =
			Opportunities.newInstance(testOppsList);
		opps.applyDiscount(10, uowMock);

		// Then
		((fflib_ISObjectUnitOfWork)
			mocks.verify(uowMock, 1)).registerDirty(
				new Opportunity(
					Id = opp.Id,
					Name = 'Test Opportunity',
					StageName = 'Open',
					Amount = 900,
					CloseDate = System.today()));
	}

On the face of it, it looks like it should correctly verify that an updated Opportunity record with 10% removed from the Amount was passed to the Unit Of Work. But this fails with an assert claiming the method was not called. The main reason for this is its a new instance and this is not what the mock recorded. Changing it to verify with the test record instance works, but this only verifies the test record was passed, the Amount could be anything.

		// Then
		((fflib_ISObjectUnitOfWork)
			mocks.verify(uowMock, 1)).registerDirty(opp);

The solution is to use the new Matchers functionality for SObject’s. This time we can verify that a record was passed to the registerDirty method, that it was the one we expected by its Id and critically the correct Amount was set.

		// Then
		((fflib_ISObjectUnitOfWork)
			mocks.verify(uowMock, 1)).registerDirty(
				fflib_Match.sObjectWith(
					new Map<SObjectField, Object>{
						Opportunity.Id => opp.Id,
						Opportunity.Amount => 900} ));

There is also methods fflib_Match.sObjectWithName and fflib_Match.sObjectWithId as kind of short hands if you just want to check these specific fields. The Matcher framework is hugely powerful, with many more useful matchers. So i encourage you to take a deeper look David Frudd‘s excellent blog post here to learn more.

If you want to know more about how Apex Mocks integrates with the Apex Enterprise Patterns as shown in the example above, refer to this two part series here.