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Setting Up the Azure Load Balancer with a ThingWorx High Availability Deployment Purpose In this post, one of PTC’s most experienced ThingWorx deployment architects, Desheng Xu, explains the steps to configure Azure Load Balancer with ThingWorx when deployed in a High Availability architectural model.   This approach has been used successfully on customer implementations for several ThingWorx 7.x and 8.x versions. However, with some of the improvements planned for ThingWorx High Availability architecture in the next major release, this best practice will likely change (so keep an eye out for updates to come).   Azure Load Balancer The overview article What is Azure Load Balancer? from Microsoft will give you a high-level understanding of load balancers in general, as well as the capabilities and limitations of Azure Load Balancer itself. For our purposes, we will leverage Azure Load Balancer's capability to manage incoming internet traffic to ThingWorx Platform virtual machine (VM) instances. This configuration is known as a Public Load Balancer.   Important Note: Different load balancers operate at different “layers” of the OSI Model. Azure Load Balancer operates at Layer 4 (Transport Layer) – it is indifferent to the specific TCP Payload. As a result, you must either configure both the front-end and back-end to work on SSL, or configure both of them to work on non-SSL communications. “SSL Termination” or “TLS Offload” is not supported by Azure Load Balancer.   Azure offers multiple different load balancing solutions. If you need some guidance on choosing the right one for you, I highly recommending reviewing the Microsoft DevBlog post Azure Load Balancing Solutions: A guide to help you choose the correct option.   High-Level Diagram: ThingWorx High Availability with Azure Load Balancer To keep this article focused, we will not go into the setup of ThingWorx in a High Availability architecture. It will be assumed that ThingWorx is working correctly and the ZooKeeper cluster is managing failover for the Platform instances as expected. For more details on setting up this configuration, the best place to start would be the High Availability Administrator’s Guide.   Planning In this installation, let's assume we have following plan (you will likely need to change these values for your own implementation): Azure Load Balancer will have a public facing domain name: edc.ptc.io Azure Load Balancer will have a public IP: 41.35.22.33 ThingWorx Platform VM instance 1 has a local computer name, like: vm1 ThingWorx Platform VM instance 2 has a local computer name, like: vm2   ThingWorx Preparation By default, the ThingWorx Platform provides a healthcheck end point at /Thingworx/Admin/HA/LeaderCheck , which can only be accessed with a credential configured in platform-settings.json : "HASettings": { "LoadBalancerBase64EncodedCredentials":"QWRtaW5pc3RyYXRvcjphZG1pbg==" } However, Azure Load Balancer does not permit this Health Check with a credential with current versions of ThingWorx. As a workaround, you can create a pings.jsp (using the attached JSP example code) in the Tomcat folder $CATALINA_HOME/webapps/docs . This workaround will no longer be needed in ThingWorx 8.5 and newer releases.   There are two lines that likely need to be modified to meet your situation: The hostname in final String probeURL (line 10) must match your end point domain name. It's edc.ptc.io in our example, don’t forget to replace this with your real hostname! You also need to add a line in your local hosts file and point this domain name to 127.0.0.1 . For example: 127.0.0.1 edc.ptc.io The credential in final String base64EncodedCredential (line 14) must match the credential configured in platform-settings.json. Additionally: Don't forget to make the JSP file accessible and executable by the user who starts Tomcat service for ThingWorx. These changes must be applied to both ThingWorx Platform VM instances. Tomcat needs to be configured to support SSL on a specific port. In this example, SSL will be enabled on port 8443. Please make sure similar configuration is included in $CATALINA_HOME/conf/server.xml <Connector protocol="org.apache.coyote.http11.Http11NioProtocol" port="8443" maxThreads="200" scheme="https" secure="true" SSLEnabled="true" keystoreFile="/opt/yourcertificate.pfx" keystorePass="dontguess" clientAuth="false" sslProtocol="TLS" keystoreType="PKCS12"/> The values in keystoreFile and keyStorePass will need to be changed for your implementation. While pkcs12 format is used in above example, you can use a different certificate formats, as long as it is supported by Tomcat (example: jks format). All other parameters, like maxThreads , are just examples - you should adjust them to meet your requirements.   How to Verify Before configuring the load balancer, verify that health check workaround is working as expected on both ThingWorx Platform instances. You can use following command to verify: curl -I https://edc.ptc.io:8443/docs/pings.jsp The expected result from active node should look like: HTTP/1.1 200 There will be three or more lines in output, depending on your instance configuration but you should be able to see the keyword: HTTP/1.1 200.   Expected result from passive node should look like: HTTP/1.1 503   Load Balancer Configuration Step 1: Select SKU Search for “load balancer” in the Azure market and select Load Balancer from Microsoft Verify the correct vendor before you create a Load Balancer. Step 2: Create load balancer To create a proper load balancer, make sure to read Microsoft’s What is Azure Load Balancer? overview to understand the differences between “basic” and “standard” SKU offerings. If your IT policy only requires SSL communication to the outside but doesn't require a SSL communication in a health probe, then the “basic” SKU should be adequate (not considering zone redundancy). You have to decide following parameters: Region Type (public or Internal) SKU (basic or standard) IP address Public IP address name Availability zone PTC cannot provide specific recommendations for these parameters – you will need to choose them based on your specific business needs, or consult Microsoft for available offerings in your region.   Step 3: Start to configure Once a load balancer is successfully created by Azure, You should be able to see:   Step 4: Confirm frontend IP Click frontend IP configuration at left side and you should be able to see public IP address configuration. Please make sure to register this IP with your domain name ( edc.ptc.io in our example) in your Domain Name Server (DNS). If you unfamiliar with DNS configuration, you should consult with the administrator of your DNS server. If you are using Azure DNS, this Quickstart article on creating Azure DNS Zones and records may help.   Step 5: Configure Backend pools Click Backend pools and click “Add” to add a backend pool definition. Select a name for your Backend pool (using ThingworxBackend in our example). Next step is to choose Virtual network.   Once you select Virtual network, then you can choose which VM (or VMs) you want to put behind this load balancer. The VM should be the ThingWorx VM instance.   In a high availability architecture, you will typically need to choose two instances to put behind this load balancer.   Please Note: The “Virtual machine status” column in this table only shows VM status, but not ThingWorx status. ThingWorx running status will be determined by the health probe configured in the next step.     Step 6: Configure Health Probe Health Probe will be used to determine the ThingWorx Platform’s running status. When a ThingWorx Platform instance is running as the leader, then it will give HTTP status code 200 during a health probe. The Azure Load Balancer will rely on this status code to determine if the platform is running properly.   When a ThingWorx platform VM is not responding, offline, or not the leader in a High Availability setup, then this health probe will provide response with a different HTTP status code other than 200.   For the health probe, select HTTPS for the protocol. In our example port 8443 is used, though another port can be selected if necessary. Then, provide the “/docs/pings.jsp” we created earlier as the probe’s path. You may need to change this path value if you put this file in a different location.   Step 7: Configure Load balancing rules. Select “Load balancing rules” from left side and click “Add”   Select TCP as protocol, in our example we are using 443 as front-end port and 8443 as back-end port. You can choose other port numbers if necessary.   Reminder: Azure Load Balancer is a layer 4 (Transport Layer) router – it cannot differentiate between HTTP or HTTPS requests. It will simply forward requests from front-end to back-end, based on port-forwarding rules defined.   Session persistence is not critical for current versions of ThingWorx as only one active node is currently permitted in a High Availability architecture. In the future, selecting Client IP may be required to support active-active architectures.   Step 8: Verify health probe Once you complete this configuration, you can go to the $CATALINA_HOME/logs folder and monitor latest local_access log. You should see similar entries as pictured below - HTTP 200 responses should be observed from the ThingWorx leader node, and HTTP 503 responses should be observed from the ThingWorx passive node. In the example below, 168.63.129.16 is the internal IP Address of the load balancer in the current region.   Step 9: Network Security Group rules to access Azure Load Balancer On its own, Azure Load Balancer does not have a network access policy – it simply forwards all requests to the back-end pool. Therefore, the appropriate Network Security Group associated with the backend resources within the resource group should have a policy to allow access to the destination port of the backend ThingWorx Foundation server (shown as 8443 here, for example). The following image displays an inbound security rule that will accept traffic from any source, and direct it to port 443 of the IP Address for the Azure Load Balancer.   Enjoy!! With the above settings, you should be able to access ThingWorx via: https://edc.ptc.io/Thingworx (replacing edc.ptc.io with the hostname you have selected).   Q&A Can I configure the health probe running on a port other than the traffic port (8443) in this case? Yes – if desired you can use a different port for the health probe configuration.   Can I use different protocol other than HTTPS for health probe? Yes – you can use different protocol in the health probe configuration, but you will need to develop your own functional equivalent to the pings.jsp example in this article for the protocol you choose.   Can I configure ZooKeeper to support the health probe? No – the purpose of the health probe is to inform the Load Balancer which node is providing service (the leader), not to select a leader. In a High Availability architecture, ZooKeeper determines which VM is the leader and talking with the database. This approach will change in future releases where multiple ThingWorx instances are actively processing requests.   How well does Azure Load Balancer scale? This question is best answered by Microsoft – as a starting point, we recommend reading the DevBlog post: Azure Load Balancing Solutions: A guide to help you choose the correct option.   How do I access logs for Azure Load Balancer? This question is best answered by Microsoft – as a starting point, we recommend reviewing the Microsoft article Azure Monitor logs for public Basic Load Balancer.   Do I need to configure specifically for Websocket and/or AlwaysOn communication? No – Azure Load Balancer is a Layer 4 (Transport Protocol) router - it only handles TCP traffic forwarding.   Can I leverage this load balancer to access all VMs behind it via ssh? Yes – you could configure Inbound NAT rules for this. If you require specific help in configuring this, the question is best answered by Microsoft. As a starting point, we recommend reviewing the Microsoft tutorial Configure port forwarding in Azure Load Balancer using the portal.   Can I view current health probe status on a portal? No – Unfortunately there is no current approach to do this with Azure Load Balancer.
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Smoothing Large Data Sets Purpose In this post, learn how to smooth large data sources down into what can be rendered and processed more easily on Mashups. Note that the Time Series Chart  widget is limited to load 8,000 points (hard-coded). This is because rendering more points than this is almost never necessary or beneficial, given that the human eye can only discern so many points and the average monitor can only render so many pixels. Reducing large data sources through smoothing is a recommended best practice for ThingWorx, and for data analysis in general.   To show how this is done, there are sample entities provided which can be downloaded and imported into ThingWorx. These demonstrate the capacity of ThingWorx to reduce tens of thousands of data points based on a "smooth factor" live on Mashups, without much added load time required. The tutorial below steps through setting these entities up, including the code used to generate the dummy data.   Smoothing the Data on Mashups Create a Value Stream for storing the historical data. Create a Data Shape for use in the queries. The fields should be: TestProperty - NUMBER timestamp - DATETIME Create a Thing (TestChartCapacityThing) for simulating property updates and therefore Value Stream updates. There is one property: TestProperty - NUMBER - not persistent - logged The custom query service on this Thing (QueryNamedPropertyHistory) will have the logic for smoothing the data. Essentially, many points are averaged into one point, reducing the overall size, before the data is returned to the mashup. Unfortunately, there is no service built-in to do this (nothing OOTB service). The code is here (input parameters are to - DATETIME; from - DATETIME; SmoothFactor - INTEGER): // This is just for passing the property name into the query var infotable = Resources["InfoTableFunctions"].CreateInfoTable({infotableName: "NamedProperties"}); infotable.AddField({name: "name", baseType: "STRING"}); infotable.AddRow({name: "TestProperty"}); var queryResults = me.QueryNamedPropertyHistory({ maxItems: 9999999, endDate: to, propertyNames: infotable, startDate: from }); // This will be filled in below, based on the smoothing calculation var result = Resources["InfoTableFunctions"].CreateInfoTable({infotableName: "SmoothedQueryResults"}); result.AddField({name: "TestProperty", baseType: "NUMBER"}); result.AddField({name: "timestamp", baseType: "DATETIME"}); // If there is no smooth factor, then just return everything if(SmoothFactor === 0 || SmoothFactor === undefined || SmoothFactor === "") result = queryResults; else { // Increment by smooth factor for(var i = 0; i < queryResults.rows.length; i += SmoothFactor) { var sum = 0; var count = 0; // Increment by one to average all points in this interval for(var j = i; j < (i+SmoothFactor); j++) { if(j < queryResults.rows.length) if(j === i) { // First time set sum equal to first property value sum = queryResults.getRow(j).TestProperty; count++; } else { // All other times, add property values to first value sum += queryResults.getRow(j).TestProperty; count++; } } var average = sum / count; // Use count because the last interval may not equal smooth factor result.AddRow({TestProperty: average, timestamp: queryResults.getRow(i).timestamp}); } } Create a Timer for updating the property values on the Thing. The Timer should subscribe to itself, containing this code (ensure it is enabled as well): var now = new Date(); if(now.getMilliseconds() % 3 === 0) // Randomly reset the number to simulate outliers Things["TestChartCapacityThing"].TestProperty = Math.random()*100; else if(Things["TestChartCapacityThing"].TestProperty > 100) Things["TestChartCapacityThing"].TestProperty -= Math.random()*10; else Things["TestChartCapacityThing"].TestProperty += Math.random()*10; Don't forget to set the runAsUser in the Timer configuration. To generate many properties, set the updateRate to a small value, like 10 milliseconds. Disable the Timer after many thousands of properties are logged in the Value Stream. Create a Mashup for displaying the property data and capacity of the query to smooth the data. The Mashup should run the service created in step 4 on load. The service input comes from widgets on the mashup: Bindings: Place a Time Series Chart widget in the bottom of the Mashup layout. Bind the data from the query to the chart. View the Mashup. Note the difference in the data... All points in one minute: And a smooth factor of 10 in one minute: Note that the outliers still appear, and the peaks are much easier to see. With fewer points, trends become easier to spot and data is easier to understand. For monitoring the specific nature of the outliers, utilize alerts and other types of displays. Alternative forms of data reduction could involve using the mean of each interval (given by the smoothing factor) or the min or max, as needed for the specific use case. Display multiple types of these options for an even more detailed view. Remember, though, the more data needs to be processed, the slower the Mashup will load. As usual, ensure all mashups are load tested and that the number of end users per Mashup is considered during application design.
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Joint Study by Cognizant and the Economist Intelligence Unit (EIU) - Making products smart can deliver game-changing innovation,enriched customer experiences and new, across-the board levels of efficiency. From R&D and manufacturing, through distribution and after-sales support, product data is changing how products are built, sold and cared for. Our latest research reveals practical steps business leaders can take to benefit from this quickly intensifying and accelerating trend.
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I always find it difficult to remember which version of software is supported with which version of ThingWorx, so I created a table for my reference. I hope this is also helpful to other people.     Oracle JDK Tomcat Database Options Memo PostgreSQL Neo4J H2 Microsoft SQLServer SAP HANA DetaStax Enterprise Edition ThingWorx 6.5 1.8.0(64-bit) 8.0.23(64-bit) 9.4.4 embedded N/A N/A N/A N/A IE 10 ThingWorx 6.6 1.8.0(64-bit) 8.0.23(64-bit) 9.4.4 embedded N/A N/A N/A N/A   ThingWorx 7.0 1.8.0(64-bit) 8.0.23(64-bit) 9.4.? embedded N/A N/A N/A N/A   ThingWorx 7.1 1.8.0_92-b14(64-bit) 8.0.33(64-bit) 9.4.5 embedded N/A N/A N/A 4.6.3   ThingWorx 7.2 1.8.0_92-b14(64-bit) 8.0.33(64-bit) 9.4.5 embedded embedded N/A N/A 4.6.3 IE 11 and later ThingWorx 7.3 1.8.0_92-b14(64-bit) 8.0.38(64-bit) 9.4.5 embedded embedded N/A SPS 11, 12 4.6.3, 5   ThingWorx 7.4 1.8.0_92-b14(64-bit) 8.0.38(64-bit) 9.4.5 embedded embedded 2014 and later SPS 11, 12 4.6.3, 5   ThingWorx 8.0 1.8.0_92-b14(64-bit) 8.0.44(64-bit), 8.5.13(64-bit) 9.4.5 embedded embedded 2014 and later SPS 11, 12 4.6.3, 5   ThingWorx 8.1 1.8.0_92-b14(64-bit) 8.0.44(64-bit), 8.5.13(64-bit) 9.4.5 embedded embedded 2014 and later SPS 11, 12 4.6.3, 5   ThingWorx 8.2 1.8.0_92-b14(64-bit) 8.0.47(64-bit), 8.5.23(64-bit) 9.4.5 embedded embedded 2014 and later SPS 11, 12 4.6.3, 5   ThingWorx 8.3                  
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Video Author:                     Durgesh Patel Original Post Date:            June 12, 2017 Applicable Releases:        ThingWorx   Description: In this video we review the different features available in Grid Advanced Extension version 2.0.  
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Introduction to the platform extensibility structures and options. Includes overview of setting up the eclipse plugin and build process, as well as install considerations and best practices.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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There are multiples approaches to improve the performance Increase the NetWork bandwidth between client PC and ThingWorx Server Reduce the unnecessary handover when client submit requests to ThingWorx server through NetWork Here are suggestions to reduce the unnecessary handover between client and server Eliminate the use of proxy servers between client and ThingWorx It is compulsory to download Combined.version.date.xxx.xxx.js file when the first time to load mashup page (TTFB and Content Download time). Loading performance is affected by using of proxy servers between client and ThingWorx Server. This is testing result with proxy server set up This is the test result after eliminiating proxy server from the same environment Cut off extensions that not used in ThingWorx server After installed extensions, the size of Combined.version.date.xxx.xxx.js increased Avoid Http/Https request errors There is a https request error when calling Google map. It takes more than 20 seconds
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From the documentation, a SOLR node is only needed in case of using DataTables.  If the SOLR configuration field left blank, the extension will request to provide an input. Are SOLR nodes required or optional in order to use DSE with TW (in the hypothetical case of not using DataTables)?      -- As for functionality of the Thingworx, a Solr node is not required. However, the extension does try to validate the configuration, and hence, at this point, a SOLR node is mandatory to properly configure the extension. This will be fixed in the future. When there are 2 entries for addresses, one for a Cassandra Cluster and one for a Solr Cluster, are they the same Cluster, or different Clusters?      -- They could be either. There can be one machine with SOLR enabled and using the same IP for both Cassandra and Solr. However, it's not recommend for production workloads. It would be perfectly fine for development or test environments. In a Cluster, in order to have Solr and Cassandra nodes, use of Datacenters is required. Even if a Datacenter isn't explicitlydefined, a default install of DSE will create two data centers called "Cassandra" and "Solr" which is what would be seen see in the default "Cassandra Keyspace Settings" property in the configuration. If the user does create Datacenters with specific names then they will have to update the "Cassandra Keyspace Settings" property to reflect the same. I.e. replication = {'class':'NetworkTopologyStrategy', 'Cassandra':1, 'Solr':1} The number in front (1 being the default) represents the replication factor (https://docs.datastax.com/en/cql/3.1/cql/cql_using/update_ks_rf_t.html) depending on the number of nodes in each data center
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Disclaimer: This post does of course not express any political views.   Pie Chart Coloring   In ThingWorx Pie Charts use a default color schema based on the DefaultChartStyle Definitions. These schemas are using fixed numbering and coloring systems, e.g. 1 is blue, 2 is green, 3 is red and so on. All Pie Charts will be rendered with these colors in the same order, no matter which data the chart is using. Visualization of data with the default colors might not necessarily help in creating an easy to read chart.   Just take a look at the following example with the default color schema. Let's just take political parties - as they are usually associated with a distinct color - to illustrate how the default color schema will fail depending on the data displayed.         In the first example, just by sheer coincidence the colors are perfectly matching the parties. When introducing a new party to the pool suddenly the blues are rendered green and the yellows rendered light-blue etc. This can be quite confusing, especially on election night 😉   Custom Color Schema   PoliticalParties Thing   To test a custom color schema, we first need to create a new Thing: PoliticalParties as a GenericThing Add a dataset property with the following PoliticalParties DataShape.         Save the Thing and set the InfoTable to:   Key Value The Purples 20 The Blues 20 The Greens 20 The Reds 20 The Yellows 20 Others 20   Number values don't actually matter too much, as the Pie Chart will automatically distribute them according to their percentage.   PoliticalParties Mashup   Create a new Mashup and add a PieChart to the canvas. Bind the PoliticalParties > GetPropertyValues > dataset to the Data input of the Widget. Ensure to set the LabelField to key and the ValueField to value for a correct mapping.     Save the Mashup and preview it.   It should show a non-matching color for each party listed in the InfoTable.   Custom Styles and States   Create new custom Style Definitions for each political party. As the Pie Chart is only using the Background Color other properties can stay on the default. I chose to go with a more muted version of the colors to make the chart easier to look at.         With the newly defined colors we can now generate a new State Definition as follows:       The States allow to evaluate the key-Strings in the Thing's InfoTable and assign a Style Definition depending on the actual value. In this definition we map a color schema based on the InfoTable's key-value to create a 1:1 mapping for the Strings.   This means, no matter where a certain party is positioned in the chart it will be tinted with its associated color.   Refining the Mashup   Back in the Mashup, select the PieChart. In the ColorFormat property choose the newly created State Definition.     Save the Mashup and preview it. With the States and Styles applies, colors are now displayed correctly.       Even when changing positions and numbers in the original InfoTable of the PoliticalParties Thing, the chart now considers the mapping of Strings and still displays the colors correctly.  
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This design session introduces a real-world product scenario along with requirements for developing a related IoT-based application. You will also be introduced to core ThingWorx terminology and concepts that will help to map out an efficient design plan for the model hierarchy.     For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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ThingWorx's JDBC extensions - Relational Database Management System and the JDBC Extensions allow ThingWorx to connect to variety of different databases. With that comes a natural question how and what sort of SQL statements could be executed via these extensions? Note: ​​Importing the JDBC extensions i.e. the RDBMS and JDBC Extensions, creates a Database Template for that particular database. If you are working with RDBMS extension then Template of corresponding Database will be created with similar name e.g. importing the RDBMS Extension for Oracle 12 will create Template named OracleDBServer12. While importing the JDBC driver using the JDBC extension will create Template name based on the JDBC driver used or a custom name could be given. Following examples and SQL statements are adhering to Oracle's SQL*Plus standard, however these can be easily adapted to the type of RDBMS you intend to work with. Topics How to create SQL Service in ThingWorx entity Types of SQL Statements Examples on SQL Service usage and some extended use cases / examples How to create SQL Service in ThingWorx Navigate to the Thing implementing the Database Template, e.g. OracleDBServer12         2. Click on the Services section under the Entity Information and click on Add My Service         3. A new service creation section will come up, change the Service type of JavaScript (this is default selection) to either SQL (Query) or SQL (Command) depending on the type of SQL you are to create under this particular service                       4. Here's quick example on creating SQL (Query) service which takes name as input for a select *  sql … Statement, i.e. it returns complete set of rows and columns from any given table on which the user has the access to perform Select                   Note: BaseType defaults to Infotable when creating SQL (Query) service and the returned number of rows are restricted to 500. Therefore, if table contains rows more than 500, ensure to change the Max Rows parameters         5. Example on creating SQL (Command) service that delete all the rows from the database table               Note: The Base Type defaults to Number when using SQL (Command)     Additional information:     When creating a SQL service, apart from providing changing the Service Info and  Inputs /Outputs, 3rd section Tables/Columns allows users to explore the Tables and their respective columns as part of that particular user's schema - meaning the objects on which the user has select rights in his schema in the database.     Types of SQLs This is not an exhaustive list, rather contains most commonly used types of SQL statements     1. Data Definition Language (DDL)           a. Create, Alter and drop schema objects           b. Grant and Revoke privileges and roles     2. Data Manipulation Language (DML)           a. Insert           b. Delete           c. Select Examples for SQL Service usage and some extended use cases / examples     1. Data Definition Language (DDL)           a. Create statement                       b. Alter statements                         c. Drop statement                         d. Flashback statements (Oracle specific)                         e. Grant statement                     f. Rename statement                 2. Data Manipulation Language (DML)           a. Insert statement                     b. Delete statement                     c. Select statements           Use cases - Case 1 : Backing up DataTable DataTable objects in ThingWorx are for quick lookup of data and they are most performant till ~100K rows. Exceeding rows over 100K in a DataTable makes it highly susceptible to performance issues in terms of querying or writing to it. Unless, there's sharding​ on the persistence provider or multiple persistence providers used - JDBC connectivity to external data stores like RDBMS systems could help in keeping up with growing number of rows in DataTables. RDBMS tables are more than capable of storing very large amount of rows without being taxed over the performance. JDBC extension could be used to do just that in a use case requiring backing up DataTable or any Data Storage objects from ThingWorx for that matter. Here's one quick example using one of the Insert SQL service shown above to back up the entire DataTable to the Oracle's DB table. Following ThingWorx JavaScript service wraps the InsertIntoBULKDATAINSERTDT SQL service: // result: INTEGER // getting total row count in the DataTable var totalCount = Things["BulkInsertDT"].GetDataTableEntryCount(); var params = { maxItems: totalCount /* NUMBER */ }; // result: INFOTABLE // DataTable service to fetch all the rows from it var allData = Things["BulkInsertDT"].GetDataTableEntries(params); // looping over the result fetched above to get all the rows for insertion     for (var i = 0; i<totalCount; i++) {         var result = allData.getRow(i); // mapping the data for insert     var params = {         LongCol3: result.LongCol3 /* LONG */,         numcol1: result.NumCol1 /* NUMBER */,         StringCol2: result.StringCol2 /* STRING */,         IntCol4: result.IntCol4 /* INTEGER */     }; try { // result: NUMBER // calling the SQL Service InsertIntoBULKDATAINSERTDT created under a DB Thing called OracleDBThingNew     var result = Things["OracleDBThingNew"].InsertIntoBULKDATAINSERTDT(params); } catch (err) {      Logger.info ("Failed to insert the values" + err) }     }
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Requirements:  Axeda 6.1.6+ The Axeda Applications User Interface can be extended to accommodate varying degrees of customization.  This ability to customize the base product enables repurposing the Axeda Applications User Interface to serve a specific audience. What this tutorial covers This tutorial discusses three ways to extend the Axeda Applications User Interface, which can be achieved via the following features: Customizing the Look and Feel - Use your own custom stylesheet to replace the default page styles, even on a per-user basis Extended UI Modules - Insert your own Extended UI Module into the Service > Asset Dashboard Custom Tab - Create a custom tab that loads content from a custom M2M application Customizing the Look and Feel of the Axeda Applications User Interface You can add style changes into a user.css file which you then upload like any other custom application, via the Administration > Extensions tab as a zip archive.  Make sure to adhere to the expected directory structure and follow the naming convention for the zip archive. Images - store image files in a directory called <userName>/images Styles - store user.css and any style sheet(s) that it imports in a directory called <userName>/styles Documentation - store documentation files in a directory called <userName>/doc. The naming convention is to name the archive by the username of the user who should be able to see the changes, i.e. jsmith is the username so jsmith.zip is the archive name. For step-by-step instructions for customizing the UI, Axeda customers may refer to http://<<yourdomain>>.axeda.com/help/en/stylesheets_for_user_branding.htm andhttp://<<yourdomain>>.axeda.com/help/en/upload_user_branding.htm . Extended UI Modules Extended UI Modules can be added to the Asset Dashboard to provide custom content alongside the default modules.  The modules can contain the output of a custom object or a custom application, all within the context of the particular asset being viewed. Create the Extended UI Content Option 1: an Extended UI Type Custom Object Navigate to Configuration > New > Custom Object This Custom Object should output HTML with any Javascript and/or CSS styling embedded inline.  Parameters may be defined here and made available to the script as "parameters.label". Example: def iframehtml = """<html>   <head>     <script type='text/javascript' src='https://www.google.com/jsapi'></script>     <script type='text/javascript'>       google.load('visualization', '1', {packages:['gauge']});       google.setOnLoadCallback(drawChart);       function drawChart() {         var data = new google.visualization.DataTable();         data.addColumn('string', 'Label');         data.addColumn('number', 'Value');         data.addRows([           ['$parameters.label', $parameters.value]         ]);         var options = {           redFrom: 90, redTo: 100,           yellowFrom:75, yellowTo: 90,           minorTicks: 5         };         var chart = new google.visualization.Gauge(document.getElementById('chart_div'));         chart.draw(data, options);       }     </script>   </head>   <body style="background: white;">     <div id='chart_div'></div>   </body> </html>​ """ return ['Content-Type': 'text/html', 'Content': iframehtml.toString()]      Option 2: A Custom Application Create a zip file that contains an index html file at the root of the directory, any stylesheets, scripts and images you prefer and upload the zip as a Custom Application (see the example zip file included at the end of this article). Navigate to Administration > Extensions .  Enter the information for the zip file and upload. Create the Extended UI Object Option 1: Using the Axeda Applications Console Navigate to Configuration > New > Extended UI Module Note that the parameters are entered in URI format  myvalue=mykey&othervalue=otherkey If Content Source is set to Custom Application rather than Custom Object, the Custom Applications will become available as the Extended UI Module content. Option 2: Use Axeda Artisan Check out Developing with Axeda Artisan in order to make use of this method.  Add the Extended UI Module to the apc-metadata.xml and it will be created for you automatically on Maven upload.  Note that Artisan does not support Model Preferences, so you will still have to add the module through the Axeda UI as described below. <extendedUIModule>     <!-- you can create the module here, but you still have to use the Axeda Console to apply it to the model where the module should show up -->     <title>extendedUI_name</title>     <height>180</height>     <source>         <type>CUSTOM_APPLICATION</type>         <name>customapp_name</name>     </source> </extendedUIModule> Add the Extended UI Module to the Model Preferences Navigate to Configuration > View > Model and click Preferences under UI Configuration next to the model that should display the Extended UI Module for its assets. Click Asset Dashboard Layout Select the Extended UI Module from the left and click the arrow to add it to the desired column.  The asterisks indicate Extended UI Modules, as opposed to default modules. Click Submit and navigate to an Asset Dashboard to see the module displayed. Now you have an Extended UI Module with your custom content. Custom Tabs Upload a custom application as a custom tab. And there you have it. For Artisan developers, to enable a custom application as a custom tab, insert the following into the apc-metadata.xml: <application>     <description>string</description>     <applicationId>string</applicationId>     <indexFile>string</indexFile>     <zipFile>relative path from apc-metadata.xml to the zip file containing the application files</zipFile>     <customTab>         <tabPrivilegeName>the privilege name required for the tab to be shown</tabPrivilegeName>         <afterTab>the name of the tab after which to place this tab</afterTab>         <showFooter>[true|false]</showFooter>         <tabNames>             <label>                 <locale>the i18n locale (for example en_US or ja_JP)</locale>                 <name>the name to be displayed for the locale</name>             </label>         </tabNames>     </customTab> </application>      Authentication within Extended UI Components When working with Custom Applications in custom tabs or modules, the user session ID is made available through a special variable that you can access from the landing page (such as index.html) only: %%SESSIONID_TOKEN%%      This variable is substituted directly for the session id, which makes the authentication for viewing the Extended UI component appear seamless to the end user. In order to make this ID available for AJAX calls, the index.html file should store the session ID as it is initializing.  Additionally, index.html should instruct the browser not to cache the page, or the session ID may mistakenly be used to authenticate after it expires. In index.html: <html>     <head>         <title>My Custom App</title> <META HTTP-EQUIV="CACHE-CONTROL" CONTENT="NO-CACHE"> <link media="screen" href="styles/axeda.css" rel="stylesheet" type="text/css"/> <script src="scripts/jquery-1.9.0.min.js" type="text/javascript"></script> <script type="text/javascript">             $(window).load(function () {                 App.init(encodeURIComponent("%%SESSIONID_TOKEN%%"));             })         </script>     </head>    In App.js: App.init = function (sessionID) {         // put initial processing here         storeSessionId( sessionID )         App.callScriptoWithStoredSessionID()     }    That's it!  You can now customize the look and feel of the Axeda Applications Console, as well as add an Extended UI Module and a Custom Tab. Further Reading Developing with Axeda Artisan Axeda Sample Application: Populating A Web Page with Data Items Common Questions I want to display my custom app on a custom tab. How should I manage authentication within my custom tab app? Answer:  Use Javascript to store the session ID injected as a variable into the index.html page, then use that to authenticate Scripto calls to the Axeda Platform. Are there example programs to get started? Answer:  There are several examples of Artisan projects to get started Axeda Sample Application: Populating A Web Page with Data Items An Axeda instance - https://<customerInstance>.axeda.com/artisan
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  The IoT Enterprise Deployment Center’s goal is to create and share knowledge around the best practices for architecting, designing, and deploying successful, enterprise-scale Thingworx IoT Solutions.    To accomplish this goal, the EDC team takes a “real world” approach, using simulated IoT assets and users to benchmark the capabilities of different Thingworx deployment configurations. First, each implementation is pushed to its limit in an effort to establish real-world baselines, metrics which can be used to help customers determine which architecture choices will work for their custom needs. Then, each implementation is pushed beyond its limits, providing useful insight into where and why things fail, and illuminating potential implementation changes which could push the boundaries further.   Through the simulations testing to come, the EDC will be publishing the resulting benchmarks for all to see! These benchmarks will include details on implementation goals and performance metrics for different stages of deployment. Additionally, best-practice articles which illustrate how to deploy the different architectural components (those referenced within the benchmarks) will also be posted, highlighting the optimal approach to integrating everything into the Thingworx platform.   Stay tuned to see more about just how versatile the ThingWorx Platform can be! We look forward to discussing these findings as they are published right here on the PTC Community. 
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This is a lessons learned write up that I proposed to present at Liveworx but it didn't make the cut, but I did want to share it with all the developer folks. Please note that this is before we added Influx and Micro Services, which help improve the landscape. Oh and it's long 🙂 ------------------------------------------ This is written as of Thingworx 8.2   Different ways to scale Data and Processing with Thingworx Two main issues are targeted Data Storage Platform processing Data Storage in Thingworx Background Issues around storage is that due to the limited indexing in the Persistence Provider with then the actual values according to the datashape being in a JSON Blob So when you look in the Persistence Provider you’ll see Source sourceType Location entityID Datetime Tags ValueJSONBlob   The first six carry an index, the JSON Blob which holds the values according to the datashape is not, that can read something like {value1:firstvalue,value2:secondvalue,value3:[ …. ]} etc. This means that any queries beyond the standard keys – date/time, entityID (name of Stream or DataTable), source, sourcetype, tags, location become very inefficient because it will query the records and then apply the datashape query server side. Potentially this can cause you to pull way more records over from Persistence Provider to Platform than intended. Ie: a Query on Temperature in my data, that should return 25 records for a given month, will perhaps first return 250K records and then filter own to 25. The second issue with storage is that all Streams are stored in one table in the Persistence Provider using entityID as an additional key to figure out which stream the record is for. This means that your record count per table goes up much faster than you’d expect. Ie: If I have defined 5 ValueStreams for 5 different asset types, ultimately all that data is still in one table in the Persistence Provder. So if each has 250K records, a query against the valuestream will then in actuality be a query against 1.25 million records. I think both of these issues are well known and documented? By now and Dev is working on it. Solution approaches So if you are expecting to store a lot of records what can you do? Archive The easiest solution is to keep a limited set and archive off the rest of the data, preferably into a client’s datalake that is not part of the persistence provider, remember archiving from one stream to another stream is not a solution! Unless … you use Multiple Persistence Providers Multiple Persistence Providers Thingworx does support multiple persistence providers for storing data. So you can spin up extra schemas (potentially even in the same DataBase Server) to be the store for additional Persistence Providers which then are mapped to a specific Stream/ValueStream/DataTable/Blog/Wiki. You still have to deal with the query challenge, but you now have less records per data store to query through. Direct queries in the Persistence Provider If you have full access to your persistence provider (NOTE: PTC Cloud Services does NOT provide this right now). You can create an additional JDBC connection to the Persistence Provider and query the stream directly, this allows you to query on the indexed records with in addition a text search through the JSON Blob all server side. With this approach a query that took several minutes at times Platform side using QueryStreamEntries took only a few seconds. Biggest savings was the fact that you didn’t have to transfer so many records back to the Platform server. Additional Schemas You can create your own schema (either within the persistence provider DB – again not supported by PTC Cloud Services) in a Database Server of your choice and connect to it with JDBC/REST. (NOTE: I believe PTC Cloud Service may/might offer a standalone server with actual root access) This does mean you have to create your own Getter/Setter services to retrieve and store information, plus you’ll need some event to store (like DataChange). This approach right now is probably a common if not best practice recommendation if historical information is required for the solution and the record count looks to go over 1 million records and can’t just be queried based on timestamp. Thingworx Event Processing Background Thingworx will consistently deal with many Things that have many Properties, and often times there will be Alerts/Rules that need to run based on value changes. When you are using straight up Alerts based on a limit value, this isn’t such a challenge, but what if you need to add some latch/lock/debounce logic or need to check against historical values or check multiple conditions? How can you design something that can handle evaluating these complex rules, holds some historical or derived values and avoid race conditions and be responsive? Potential Problems Race conditions Multiple Events may need to update the same Permanent or Temporary store for the determination of a condition. Duplicates If you don’t have some ‘central’ tracker, you may possibly trigger the same rule multiple times. Slow response You are potentially triggering thousands or more events at the same time, depending on how you’ve set up your logic, your response could become so slow that the next event will be firing before finish and you’ll overload the system. System queue overrun If your events trigger faster than you can handle the events, you will slowly build up and finally overrun the event queue. System Thread count overrun Based on the number of cores in your system, you can overrun the number of threads that can be handled. Connection Pool overrun Each read/write to a stream/datatable but also Property Persist is a usage of the connection pool to your persistence provider. If you fire a lot at once, you can stack up requests and cause deadlocks System out of memory Potentially in handling the events you are depending on in memory information, if that is something that grows over time, you could hit an ‘Out of Memory’ issue. Solution Approaches Batch processing Especially with Agents/Sources that write a set of property updates, you potentially trigger multiple threads that all may need the same source information or update the same target information. If you are able to process this as a batch, you can take all values in account and only process this as a single event and have just a single read from source or single write to target. This will be difficult to achieve when using something like Kepserver, unless it is transferring as something non-standard like MQTT. But if you can have the data come in as a single REST POST this approach becomes possible. In Memory vs. Table/Stream Storage To speed up response time, you can put necessary information into Memory vs. in a DataTable or Stream. For example, if you need the most current received record together with some historical values, you could: Use a Stream but carry the current value because the stream updates async. (ie adding the current value to the stream doesn’t guarantee that when you read from the stream it has already been committed) Use a DataTable because they are synchronous but it can make the execution slow, especially if you are reaching 100K records or more Use an InfoTable or JSON Property, now this information is in memory and runs the fastest and is synchronous. Note that in some speed testing JSON object was faster than InfoTable and way faster than DataTable. One challenge is that you would have to do a full overwrite if you need to persist this information. Doing a full write does open up the danger of a race condition, if this information is being updated by multiple threads at the same time. If it is ok to keep the information in memory than an InfoTable is nice because you can just add/delete rows in memory. I sadly haven’t figured out yet how to directly do this to a JSON object property :(. It is important to consider disaster recovery scenarios if you are only using this in memory Centralized Processing vs. Distributed Processing Think about how you can possibly execute some logic within the context of the Entity itself (logic within the ThingShape/ThingTemplate) vs. having it fire into a centralized Service (sync or async) on a separate Entity. Scheduler or Timer As much as Schedulers and Timers are often the culprit of too many threads at the same time, a well setup piece of logic that is triggered by a Scheduler or Timer can be the solution to avoid race conditions If you are working with multiple timers, you may want to consider multiple schedulers which will trigger at a specific time, which means you can eliminate concurrence (several timers firing at the same time) Think about staggering execution if necessary, by using the hated, looked down upon … but oft necessary … pause() function !!!! Synchronous vs. Asynchronous Asynchronous execution can give great savings on the processing speed of a thread, since it will kick off the asynch parts and continue on. The terrible draw back, you can’t tell when it is finished nor what the resulting output is. As you mix and match synch/asynch vs processing speed, you may need to consider other ways to pick up when an asynch process finishes, some Property elsewhere that will trigger into a DataChange for example. Interesting examples Batch Process With one client there was a batch process that would post several hundred results at once that all had to be evaluated. The evaluation also relied on historical information. So with some logic these properties were processed as a batch, related to each other and also compared to information held in memory besides historically storing the information that came in. This utilized several in memory objects and ultimately also an eval() statement to have the greatest flexibility and performance. Mix and Match With another client, they had a requirement to have logic to do latch/lock and escalation. This means that some information needs to be persisted, however because all the several hundred properties per asset are coming in through Kepware once a second, it also had to be very fast. The approach here was to have the DataChange place information into an in memory infotable that then was picked up by a separate latch/lock/escalation timer to move it over to the persistent side. This allowed for the instantaneous processing of DataChange and Alerts, but also a more persistent processing of latch/lock/escalation logic. In Conclusion Remember that PTC created its software for specific purposes. I don’t think there ever will be a perfect magical platform that will do everything we need and want. Thingworx started out on a specific path which was very high speed data ingest and event platform with agnostic all around connectivity, that provided a very nice holistic modeling approach and a simple way to build UI/UX. Our use cases will sometimes go right past everything and at times to the final frontier aka the bleeding edge and few are a carbon copy of another. This means we need to be innovative and creative. Hopefully all of you can use the expert knowledge you have about our products to create those, but then also be proactive and please share with everyone else!  
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Warning This post is quite long, has various chapters and you might get bored reading it. If you just want a summary read the "Use case" and "Conclusion" chapter - and maybe the "Required Logic" chapter, because I made a cool graph for it. The rest is all about implementation... Introduction I recently had the opportunity to deliver a ThingWorx training for Saint Gobain. One of the use cases for their ThingWorx application is monitoring machine errors and outages on the production line. If an outage or error status is triggered, the machine operator will see a popup on the monitoring screen where he is forced to select a root cause. This root cause will then be persisted in ThingWorx for more data transformation, analytics and reporting - like cost analysis or optimization opportunities. During the training we were also discussing on how such a forced root cause monitoring can be implemented via Mashups and the usage of modal popups. I've compiled the details into this post as it might also interest other developers. The ThingWorx Entities I'm using in this example can be downloaded from here Note: I'm using the word "Alert" here - but not in the context of a ThingWorx Property Alert... just beware to not be confused due to the wording. Use Case One of the requirements for Saint Gobain's IoT Solution was an interactive alert monitoring directly in the factory on the production machines. Let's say the machine has stopped, the root cause should be recorded. For this an interactive popup will be displayed on the machine's monitoring display and an employee has to choose the root cause from a pre-defined list. This could be planned outages, e.g. for maintenance or unplanned outages, e.g. material jam. The root cause will then be recorded and a history of outage causes can be stored in a ThingWorx value stream. This can then be later analyzed with e.g. ThingWorx Analytics capabilities to understand and optimize the machine's production capabilities and efficiency. As the root cause must be entered, the popup will be forced to be displayed when a certain condition / criteria is met - and it will only disappar when a root cause is chosen. The user should not be able to interact with any other elements of the Mashup and not be able to just close the popup. The popup will close itself and reset the initial condition once the root cause has been identified and chosen. Requirements Required Entities Required Logic Note: Just to make it easier to manage and export Entities, I will add all of the created elements in a new Project called RootCausePopups. All of the elements will have a "rcp_" added in front of their name - just to make it easier for me to find and identify them. Implementation Before we start - set a Project Context Create Entities Create a Popup Mashup Create the Main Mashup Testing the Mashups Conclusion Certain conditions (like the state of a checkbox) can be used to trigger modal popups. A modal popup forces a user interaction and the interaction will not offer any other option until a choice is made. With these parameters it's easy to have mandatory reaction from users when it's important to capture data which rely on the analysis of an engineer or a user - e.g. reasons for machine outages. Using this technique there's not much training required for staff, other than pushing a button with an option of their choice - this saves quite some time in capturing data in any other way (e.g. updating Excel files or manual pen-and-paper techniques). As this data is now part of the ThingWorx instance it can be used for further transformation, analysis or just for monitoring purposes There's of course more possibilities when it comes to states and formatting which would exhaust the context of this post - but feel free to explore... In the example we wouldn't need the textbox, but it's there to demonstrate if the correct values are persisted or not In the example we could of course also set the visibility of the checkbox to false, so that we would only see the popup and the Grid holding historic information We could also create different StateDefinitions to color-format / text-format the input differently from the output in the Grid If you found this interesting (and actually made it to the end of this post) - feel free to play with this concept a bit more... The dependencies might seem a bit difficult, but it should be easy to implement and to adjust to your own ideas and requirements.
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Concepts of Anomaly Detection used in ThingWatcher ThingWatcher is based on anomaly detection with the normal distribution. What does that mean? Actually,  normally distributed metrics follow a set of probabilistic rules. Upcoming values who follow those rules are recognized as being “normal” or “usual”. Whereas value who break those rules are recognized as being unusual. What is a normal distribution? A normal distribution is a very common probability distribution. In real life, the normal distribution approximates many natural phenomena. A data set is known as “normally distributed” when most of the data aggregate around it's mean, in a symmetric way. Also, it's extreme values get less and less likely to appear. Example When a factory is making 1 kg sugar bags it doesn’t always produce exactly 1 kg. In reality, it is around 1 kg. Most of the time very close to 1 kg and very rarely far from 1 kg. Indeed, the production of 1 kg sugar bag follows a normal distribution. Mathematical rules When a metric appears to be normally distributed it follows some interesting law. As does the sugar bag example. The mean and the median are the same. Both are equal to 1000. It’s because of  the perfectly symmetric “bell-shape” It is the standard deviation called sigma σ that defines how the normal distribution is spread around the mean. In this example σ = 20 68% of all values fall between [mean-σ; mean+σ] For the sugar bag [980; 1020] 95% of all values fall between [mean-2*σ; mean+2*σ] For the sugar bag [960; 1040] 99,7% of all values fall between [mean-3*σ; mean+3*σ] For the sugar bag [940; 1060] The last 3 rules are also known as the 68–95–99.7 rule also called the three-sigma rule of thumb When the rules get broken: it’s an anomaly As previously stated, When a system has been proven normally distributed, it follows a set of rules. Those rules become the model representing the normal behavior of the metric. Under normal conditions, upcoming values will match the normal distribution and the model will be followed. But what happens when the rules get broken? This is when things turn different as something unusual is happening. In theory, in a normal distribution, no values are impossible. If the weights of the bags of sugar were really distributed, we would probably find a bag of sugar of 860 g every billion products. In reality, we approximate this sugar bag example as normally distributed. Also, almost impossible value are approximated as impossible Techniques of Anomaly Detection Technique n°1: outlier value An almost impossible value could be considered as an anomaly. When the value deviates too much from the mean, let’s say by ± 4σ, then we can consider this almost impossible value as an anomaly. (This limit can also be calculated using the percentile). Sugar bags who weigh less than 920 g or more than 1080 g are considered anomalous. Chances are, there is a problem in the production chain. This provides a simple way to define maximum and minimum thresholds. Technique 2: detecting change in the normal distribution Technique n°2 can detect unusual distribution fast, using only some points. But it can’t detect anomalies who move from one sigma σ to another in a usual manner. To detect this kind of anomaly we use a “window” of n last elements. If the mean and standard derivation of this window change too much from usual then we can deduce an anomaly. Using a big window with a lot of values is more stable, but it requires more time to detect the anomaly. The bigger the window is the more stable it becomes. But it would require more time to detect the anomaly as it needs to aggregate more values for the detection.
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This Expert Session will walk you through the Components involved in the ThingWorx Studio Augmented Reality Environment, a detailed Architecture, supported devices, and exploring the resources. The session shall provide great insight into the working and the technicalities involved in the ThingWorx Studio.   For full-sized viewing, click on the YouTube link in the player controls.   Visit the Online Success Guide to access our Expert Session videos at any time as well as additional information about ThingWorx training and services.
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The Property Set Approach This article details an approach developed by Prachi Rath and Roy Clarke, refined by the EDC team in the December 2019's Remote Monitoring of Assets Reference Benchmark , and used to handle multi-property business rules in an Enterprise ThingWorx application.   Introduction If there are logic rules which depend upon multiple properties, and each property receives its updates one at a time, then each property will need to have an identical subscription, because there is no way for any one subscription to know the most up-to-date values for the other properties. This inefficient approach would create redundancy and sizing constraints, reducing the capacity of the application to scale up to the Enterprise level. The Property Set Approach resolves this issue by sending in all property updates as one Info Table or JSON property (called the “property set”), which can then have a single subscription. The property set is assembled on the Edge when an update needs to be sent, and then the Platform dissects, processes, and stores the data within this property set as required by the business logic.   This approach also involves caching the last property value into a runtime variable so that it can be referenced within the business logic subscription without having to be retrieved from the database. This can significantly improve the runtime of the subscription, reducing the number of resources required to sustain the business logic and ensuring that any alerts or events resulting from the business logic occur as soon as possible. It also reduces the load on the database, ensuring that data ingestion can complete unhindered.   So, while there are many benefits to this approach, it is also more complicated. It tightly couples the development of the Edge and Platform code and increases the application complexity, making it slightly less easy to maintain the application in the long run. The property set also requires a little more bandwidth and a more stable internet connection between Edge devices and the Platform since there is more metadata in an Info Table property, and therefore every update is slightly larger than it would be otherwise. So this approach is only recommended when multi-property rules are a requirement of the application and a stable internet connection exists between the Edge and Platform.   Platform Implementation I. Create an Info Table (or JSON) Property This tutorial uses the out-of-the-box Data Shape called NamedVTQ for the Info Table property, which is defined on a Thing Template as a remote property. It is important that this is not marked as persistent or logged, as the purpose is to reduce the amount of database writes and reads required by the Platform. The Info Table property has the following property definition:     <PropertyDefinition aspect.dataChangeType="ALWAYS" aspect.dataShape="NamedVTQ" aspect.isPersistent="false" baseType="INFOTABLE" isLocalOnly="false" name="numberPropertySetAsInfotable"/>       II. Create a Data Change Event Subscription for the Info Table Property The subscription has three parts: Cache the last value for the property in a runtime variable Start off the business rules processing, sending in the whole Info Table Send the Info Table to be logged as individual local properties in the database     // First step caches the last Value, refer to the next step… // Second step sets off the business rules processing with the Info Table me.ScaleTestBusinessRuleForPropertySetAsInfotable({ PropertySetAsInfotable: eventData.newValue.value }); // Third step sends the Info Table as one property into a service which parses it into the // individual properties, updating both the runtime properties on the remote thing and the database me.UpdatePropertyValues({ values: eventData.newValue.value /* INFOTABLE */ });       III. Set-Up Caching Each property which needs to be cached should be created on the Thing Template level and named in a similar way, say by placing the word “Last” at the end, such as “Property1” => “Property1Last”, “Property2” => “Property2Last”, etc. This property should NOT be logged or persistent, as the point of this is to store the most recent value in memory, removing any superfluous dependency on database queries in the process. Note that while storing the property in runtime memory makes it much more accessible, it also means that the property needs to be rewritten manually upon Platform restart. Additional code (not provided here) must be written to populate these properties from the database upon application start-up.   The following code should be placed in the data change event subscription (option 1 in the case where only a few properties need caching, or option 2 if every property value needs to be cached):   Option 1: Some but Not All Properties Need Caching     // Names of properties for which you want to cache the last value var propertyNames = ['number1', 'number2']; // Loop through the properties and cache their time if they are found in the property set propertyNames.map(assignLast); // This function can be split into two functions for Age and Last separately if need be function assignLast(propertyName) { logger.debug("Looping for property -> "+ propertyName); var searchprop = new Object(); searchprop.name = propertyName; property = eventData.newValue.value.Find(searchprop); if(property){ logger.debug("Found Row. Name= " + property.name); var lastPropertyName = propertyName+"Last"; if(property.value) { // Set the cache property on me, this entity, to the current property value me[lastPropertyName] = me[propertyName]; } } else { logger.debug("Property Not Found in property set -> " + propertyName); } }       Option 2: All Properties Need Caching     var rowCount = eventData.newValue.value.getRowCount(); for(var i=0; i<rowCount; i++){ logger.warn("property name->" + eventData.newValue.value[i].name + "----- property new value->" + eventData.newValue.value[i].value.value); var propertyName = eventData.newValue.value[i].name; var lastPropertyName = propertyName+"Last"; me[lastPropertyName] = me[propertyName]; logger.warn("done last subscription, last property value for lastPropertyName" + me[lastPropertyName]); }         Useful Platform Code Snippets I. Age Calculation     var date1 = new Date(); var date2 = me.GetPropertyTime({ propertyName: propertyName /* STRING */ }); var result = millisToMinutesAndSeconds (dateDifference(date1, date2) ); // This function converts from an unintelligibly large number in milliseconds to something formatted in minutes and seconds function millisToMinutesAndSeconds(millis) { var minutes = Math.floor(millis / 60000); var seconds = ((millis % 60000) / 1000).toFixed(0); return (seconds == 60 ? (minutes+1) + ":00" : minutes + ":" + (seconds < 10 ? "0" : "") + seconds); }       II. Sort the Info Table by Time     var params = { sortColumn: "time" /* STRING */, t: me.propertySet/* INFOTABLE */, ascending: ascending /* BOOLEAN */ }; var result = Resources["InfoTableFunctions"].Sort(params);       III. Search the Info Table for a Property     var searchprop = new Object(); searchprop.name = propertyName; property = PropertySetAsInfotable.Find(searchprop); if(property === null){ logger.info("Property Not Found -> " + propertyNumber1); } else { logger.info("Found Row. Name= [" + property.name + "], value= " + property.value.value); }         Edge Implementation This example implementation uses the .NET Edge SDK to build a property set Info Table at the Edge.   I. Define the Data Shape A standard Data Shape is used (NamedVTQ), but because this Data Shape is not exposed in the Edge SDK code, it has to be created manually.     // Data Shape definition for NamedVTQ FieldDefinitionCollection namedVTQFields = new FieldDefinitionCollection(); namedVTQFields.addFieldDefinition(new FieldDefinition(CommonPropertyNames.PROP_NAME, BaseTypes.STRING)); namedVTQFields.addFieldDefinition(new FieldDefinition(CommonPropertyNames.PROP_VALUE, BaseTypes.VARIANT)); namedVTQFields.addFieldDefinition(new FieldDefinition(CommonPropertyNames.PROP_TIME, BaseTypes.DATETIME)); namedVTQFields.addFieldDefinition(new FieldDefinition(CommonPropertyNames.PROP_QUALITY, BaseTypes.STRING)); base.defineDataShapeDefinition("NamedVTQ", namedVTQFields);     II. Define the Info Table Property The property defined should NOT be logged or persistent, and it can be read-only, since data is always pushed from the Edge and read from the server cache when accessed on the Platform. Note that the push type of the info table property MUST be set to "ALWAYS" (if set to "VALUE", the data change event will only fire if the number of rows changes).   // Property Set Definitions [ThingworxPropertyDefinition( name = "DevicePropertySet", description = "Alternative representation of properties as an Info Table for rules processing", baseType = "INFOTABLE", category = "Status", aspects = new string[] { "isReadOnly:true", "isPersistent:false", "isLogged:false", "dataShape:NamedVTQ", "cacheTime:0", "pushType:ALWAYS" } ) ]     III. Define a Property to Store the GOOD Quality Status   private static String QUALITY_STATUS_GOOD = QualityStatus.GOOD.name();     IV. Define Functions to Populate the Value Collections  An Info Table is really just made up of many Value Collections, where each Value Collection is considered a row. These services take in the name and value of a property and return a Value Collection object which can be added to the property set Info Table.   public ValueCollection createNumberValueCollection(String name, double value) { ValueCollection vc = new ValueCollection(); // Add quality and time entries to the Value Collection vc.SetStringValue(CommonPropertyNames.PROP_QUALITY, QUALITY_STATUS_GOOD); vc.SetDateTimeValue(CommonPropertyNames.PROP_TIME, new DatetimePrimitive(DateTime.UtcNow)); vc.SetStringValue(CommonPropertyNames.PROP_NAME, name); vc.SetNumberValue(CommonPropertyNames.PROP_VALUE, value); return vc; } public ValueCollection createBooleanValueCollection(String name, Boolean value) { ValueCollection vc = new ValueCollection(); // Add quality and time entries to the Value Collection vc.SetStringValue(CommonPropertyNames.PROP_QUALITY, QUALITY_STATUS_GOOD); vc.SetDateTimeValue(CommonPropertyNames.PROP_TIME, new DatetimePrimitive(DateTime.UtcNow)); vc.SetStringValue(CommonPropertyNames.PROP_NAME, name); vc.SetBooleanValue(CommonPropertyNames.PROP_VALUE, value); return vc; }     V. Build the Property Set Call this code from the processScanRequest method to build the property set.   // Create an instance of a new Info Table using the standard "NamedVTQ" Data Shape InfoTable propertySet = new InfoTable(getDataShapeDefinition("NamedVTQ")); // Set name/value for Temperature using convenience function propertySet.addRow(createNumberValueCollection("Temperature", temperature)); // Set name/value for Pressure using convenience function propertySet.addRow(createNumberValueCollection("Pressure", pressure)); // Set name/value for TotalFlow using convenience function propertySet.addRow(createNumberValueCollection("TotalFlow", this._totalFlow)); // Set name/value for InletValve using convenience function propertySet.addRow(createBooleanValueCollection("InletValve", inletValveStatus)); // Set name/value for FaultStatus using convenience function propertySet.addRow(createBooleanValueCollection("FaultStatus", faultStatus)); // Set the property set Info Table property base.setProperty("DevicePropertySet", propertySet);     VI. Update the subscribed properties These two lines of code update the properties and events, actually sending the property set (containing all property updates) to the Platform.   base.updateSubscribedProperties(15000); base.updateSubscribedEvents(60000);     Conclusion Following these steps will enable the Edge to build a property set before sending any property updates to the Platform. The Platform can then rely on caching to process the business logic with no database dependency, which is faster and more efficient than any other approach. Finally the updates are still written to the database, so in the end, there is no functional difference between using a property set and binding each property individually. Please don't hesitate to comment here with any questions about this approach.
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Having trouble remembering how to get into Flow? How about make /Flow the URL?   Since the Flow environment uses NGINX to front-end the various components that make up Flow, there is a very sophisticated set of rewrites and proxy_pass directives in the NGINX configuration. All you have to do is add another 'location' fragment to the vhost-flow.conf file that will push /Flow over to /Thingworx/Composer/apps/flow:       location /Flow {       rewrite ^/Flow$ $proxy_scheme://$server_host/Thingworx/Composer/apps/flow permanent;     }   On Linux, the file should be at /etc/nginx/conf.d/vhost-flow.conf   On Windows, the file should be at c:\Program Files\nginx-[version]\conf\conf.d\vhost-flow.conf   Test the updated config file with (nginx may not exist in your normal path): nginx -t   Restart the NGINX service: Linux (one of these will work depending upon your Linux version): systemctl restart nginx service nginx restart Windows: Net stop ThingWorxOrchestrationNginx Net start ThingWorxOrchestrationNginx -or- use the Services app to restart the service   From this point forward https://yourserver/Flow will take you to ThingWorx Flow's home page.
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In this particular scenario, the server is experiencing a severe performance drop.The first step to check first is the overall state of the server -- CPU consumption, memory, disk I/O. Not seeing anything unusual there, the second step is to check the Thingworx condition through the status tool available with the Tomcat manager. Per the observation: Despite 12 GB of memory being allocated, only 1 GB is in use. Large number of threads currently running on the server is experiencing long run times (up to 35 minutes) Checking Tomcat configuration didn't show any errors or potential causes of the problem, thus moving onto the second bullet, the threads need to be analyzed. That thread has been running 200,936 milliseconds -- more than 3 minutes for an operation that should take less than a second. Also, it's noted that there were 93 busy threads running concurrently. Causes: Concurrency on writing session variable values to the server. The threads are kept alive and blocking the system. Tracing the issue back to the piece of code in the service recently included in the application, the problem has been solved by adding an IF condition in order to perform Session variable values update only when needed. In result, the update only happens once a shift. Conclusion: Using Tomcat to view mashup generated threads helped identify the service involved in the root cause. Modification required to resolve was a small code change to reduce the frequency of the session variable update.
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