The document discusses adding a delay before dispatching a message to a message queue called testqueue. A delay is added using AMQ_SCHEDULED_DELAY before sending the message to the specified queue. This allows for a scheduled delay to be introduced when dispatching messages.
Mule ESB can split a large message into smaller parts using a Splitter flow control component. This allows large messages containing repeating elements, like a list, to be broken into individual fragments that can then be processed separately. The document demonstrates using a Splitter to break up a JSON payload containing an array of "events" into separate logs of each event's ID and timestamp, handling the large list efficiently. Splitters are useful for processing large repeating data in XML or JSON payloads.
This document discusses how to send an email with an attachment from a Mule application. It shows a Mule flow that uses a Groovy component to design the email body in HTML format, an Attachment component to attach a file to the message payload, and an SMTP component to send the email. When the flow is triggered, it successfully sends an email with the HTML body and file attachment. The document demonstrates how to add attachments to emails from Mule applications.
This document discusses how to define list and map variables in Mule ESB. It shows that lists and maps can be defined as Mule variables by using square brackets and curly braces. Values can then be retrieved from the lists and maps using indexes and keys. Examples are provided to demonstrate defining a list variable, retrieving values from the list, defining a map variable, and retrieving values from the map. The document also includes an example Mule configuration that reassigns list values and logs the new list values.
This document discusses how to run a Mule application from Java code. It explains that you need to use the MuleContext to reference the Mule flow. It provides an example of creating a Java class that loads a MuleTest.xml configuration file using SpringXmlConfigurationBuilder. The Java class then deploys the Mule application on the server. The example shows logging output and hitting an HTTP endpoint to demonstrate the flow running from Java.
The document discusses how to dynamically set file connector attributes in Mule at runtime by reading values from a JSON file. It presents a Mule flow that uses a file inbound endpoint to read a JSON file containing folder path, file pattern, and work directory. These values are then parsed and set as variables to configure a file outbound endpoint, which writes the file to the specified location. This allows file connector attributes like path and name to be dynamically determined based on external configuration.
This document discusses how to process multiple files sequentially through a file endpoint in Mule. It explains that by default, all files will be processed together in parallel using multiple threads. However, by setting the processingStrategy of the flow to "synchronous", files can be picked up and processed one by one from the inbound folder and dispatched sequentially to the outbound folder. The document provides an example Mule configuration and demonstrates that when multiple files exist in the inbound folder, they will be deposited one by one into the outbound folder over time rather than all at once.
The document discusses how to filter messages from an ActiveMQ queue by JMS priority using Mule and Groovy. Specifically, it shows how to:
1. Send messages to an ActiveMQ queue with a specific JMS priority of 9 using Mule.
2. Configure a Mule flow to receive messages from the queue using a Groovy script to filter for messages with a JMS priority of 9.
3. Demonstrate that the Groovy script is correctly filtering to only consume the message from the queue with a JMS priority of 9.
Drools is a business rules management system that provides a business rules engine. It supports the JSR-94 standard. Mule has a Drools component to execute business rules in applications. The document demonstrates creating a simple Mule application that uses business rules defined in a .drl file through the Drools component. The application generates a random weight variable and checks the rules to set the destination to either WAREHOUSE_A or WAREHOUSE_B depending on if the weight is above or below 50.
Mule can run web applications using its embedded Jetty container. This allows a web application deployed in Mule to interact with Mule flows. For example, a web page can collect data from a user and pass it to a Mule flow backend service for processing, like inserting into a database. The Mule service can then return a response back to the web page. This demonstrates how a web application hosted in Mule can seamlessly interact with Mule flows and services.
This document discusses a simple yet amazing trick for transforming XML to XML in Mule. It demonstrates how to take an XML payload and transform it to a new XML structure without writing any complex transformation logic. The full post with more details on this XML transformation technique in Mule can be found at a specific URL.
The document discusses decomposing a complex Mule flow into smaller parts connected by VM components when exposing a CXF web service. Some key advantages of this approach are better control over each event, reduced complexity, and improved performance compared to other queue services. An example is provided of a flow broken into two parts - one for the CXF component and one for the implementation class, connected by a VM queue. Properties are stored before sending to the VM to preserve them across flows.
This document discusses how to schedule tasks and monitor events in a Mule application using Java classes and the Spring task scheduler rather than the Quartz scheduler or Poll component. It provides an example of a simple Java class that logs a message every 10 seconds, and the Spring configuration that schedules the class to run repeatedly. When the Mule flow is triggered from a browser, it demonstrates that the messages continue to be logged in the console by the scheduled Java class running in the background.
This document demonstrates how to filter JMS messages in Mule ESB based on JMS properties like priority and headers when using ActiveMQ. It shows how to configure Mule flows to send messages to an ActiveMQ queue with a custom priority of 9. It then shows how to consume messages from that same queue using a JMS selector to filter for only those messages with a priority of 9, ignoring others. Similarly, it explains how to filter messages based on a custom header property instead of priority.
This document discusses how to access variables in Groovy scripts and Java code within Mule applications. There are two types of variables in Mule: flow variables, which are limited to a specific flow, and session variables, which are global to the entire application. The document provides examples of how to retrieve variable values using different methods like getInvocationProperty() and getSessionProperty() in Groovy, and set variables using setProperty() and setSessionProperty(). It demonstrates retrieving variables set in a Mule flow and also setting variables in Groovy scripts to be accessed in flows.
The Mule Message Enricher allows processing of the message without modifying the original payload. It provides a scope to add message processors that perform tasks like calling external services and extracting values from the response, without changing the original message. For example, a flow can use the enricher to call a web service, extract a value from the response into a variable, and log both the variable and original payload at the end, keeping the payload unchanged throughout.
The document discusses how Mule ESB can use a splitter component to break a large XML message into smaller parts by splitting on the individual <product> elements. It provides an example XML payload and Mule configuration that splits this payload and logs the product ID and name of each product element. The splitter allows large messages to be processed individually to more easily handle large data sets.
This document shows how to use a Groovy scripting component to hold a Mule flow for a period of time before processing the payload. The Groovy component uses the sleep method to pause the flow for 3000 milliseconds. This holds the file payload before writing it to the outbound location. The example flow uses a file inbound endpoint to pick up a file, passes it to the Groovy component which sleeps for 3 seconds, and then writes the file to the outbound location. This allows dynamically pausing a flow to hold the payload for a specified interval of time.
RingCentral is an award-winning cloud-based communications system with a large developer community called "Game Changer" that connects developers, users, and customers. Through Game Changer, developers can interact, help each other, share ideas, and participate in activities to learn about RingCentral's products and APIs while earning points and rewards. The platform aims to learn and contribute to the community in a fun way.
The API allows 10 heavy, 40 medium, 50 light requests per user and 5 authorization requests; exceeding these thresholds will result in a 429 Too Many Requests HTTP error code. The document discusses that when requests begin exceeding limitations, servers commonly return responses with a 429 status.
The document discusses how to dynamically set file connector attributes in Mule at runtime by reading values from a JSON file. It presents a Mule flow that uses a file inbound endpoint to read a JSON file containing folder path, file pattern, and work directory. These values are then parsed and set as variables to configure a file outbound endpoint, which writes the file to the specified location. This allows file connector attributes like path and name to be dynamically determined based on external configuration.
This document discusses how to process multiple files sequentially through a file endpoint in Mule. It explains that by default, all files will be processed together in parallel using multiple threads. However, by setting the processingStrategy of the flow to "synchronous", files can be picked up and processed one by one from the inbound folder and dispatched sequentially to the outbound folder. The document provides an example Mule configuration and demonstrates that when multiple files exist in the inbound folder, they will be deposited one by one into the outbound folder over time rather than all at once.
The document discusses how to filter messages from an ActiveMQ queue by JMS priority using Mule and Groovy. Specifically, it shows how to:
1. Send messages to an ActiveMQ queue with a specific JMS priority of 9 using Mule.
2. Configure a Mule flow to receive messages from the queue using a Groovy script to filter for messages with a JMS priority of 9.
3. Demonstrate that the Groovy script is correctly filtering to only consume the message from the queue with a JMS priority of 9.
Drools is a business rules management system that provides a business rules engine. It supports the JSR-94 standard. Mule has a Drools component to execute business rules in applications. The document demonstrates creating a simple Mule application that uses business rules defined in a .drl file through the Drools component. The application generates a random weight variable and checks the rules to set the destination to either WAREHOUSE_A or WAREHOUSE_B depending on if the weight is above or below 50.
Mule can run web applications using its embedded Jetty container. This allows a web application deployed in Mule to interact with Mule flows. For example, a web page can collect data from a user and pass it to a Mule flow backend service for processing, like inserting into a database. The Mule service can then return a response back to the web page. This demonstrates how a web application hosted in Mule can seamlessly interact with Mule flows and services.
This document discusses a simple yet amazing trick for transforming XML to XML in Mule. It demonstrates how to take an XML payload and transform it to a new XML structure without writing any complex transformation logic. The full post with more details on this XML transformation technique in Mule can be found at a specific URL.
The document discusses decomposing a complex Mule flow into smaller parts connected by VM components when exposing a CXF web service. Some key advantages of this approach are better control over each event, reduced complexity, and improved performance compared to other queue services. An example is provided of a flow broken into two parts - one for the CXF component and one for the implementation class, connected by a VM queue. Properties are stored before sending to the VM to preserve them across flows.
This document discusses how to schedule tasks and monitor events in a Mule application using Java classes and the Spring task scheduler rather than the Quartz scheduler or Poll component. It provides an example of a simple Java class that logs a message every 10 seconds, and the Spring configuration that schedules the class to run repeatedly. When the Mule flow is triggered from a browser, it demonstrates that the messages continue to be logged in the console by the scheduled Java class running in the background.
This document demonstrates how to filter JMS messages in Mule ESB based on JMS properties like priority and headers when using ActiveMQ. It shows how to configure Mule flows to send messages to an ActiveMQ queue with a custom priority of 9. It then shows how to consume messages from that same queue using a JMS selector to filter for only those messages with a priority of 9, ignoring others. Similarly, it explains how to filter messages based on a custom header property instead of priority.
This document discusses how to access variables in Groovy scripts and Java code within Mule applications. There are two types of variables in Mule: flow variables, which are limited to a specific flow, and session variables, which are global to the entire application. The document provides examples of how to retrieve variable values using different methods like getInvocationProperty() and getSessionProperty() in Groovy, and set variables using setProperty() and setSessionProperty(). It demonstrates retrieving variables set in a Mule flow and also setting variables in Groovy scripts to be accessed in flows.
The Mule Message Enricher allows processing of the message without modifying the original payload. It provides a scope to add message processors that perform tasks like calling external services and extracting values from the response, without changing the original message. For example, a flow can use the enricher to call a web service, extract a value from the response into a variable, and log both the variable and original payload at the end, keeping the payload unchanged throughout.
The document discusses how Mule ESB can use a splitter component to break a large XML message into smaller parts by splitting on the individual <product> elements. It provides an example XML payload and Mule configuration that splits this payload and logs the product ID and name of each product element. The splitter allows large messages to be processed individually to more easily handle large data sets.
This document shows how to use a Groovy scripting component to hold a Mule flow for a period of time before processing the payload. The Groovy component uses the sleep method to pause the flow for 3000 milliseconds. This holds the file payload before writing it to the outbound location. The example flow uses a file inbound endpoint to pick up a file, passes it to the Groovy component which sleeps for 3 seconds, and then writes the file to the outbound location. This allows dynamically pausing a flow to hold the payload for a specified interval of time.
RingCentral is an award-winning cloud-based communications system with a large developer community called "Game Changer" that connects developers, users, and customers. Through Game Changer, developers can interact, help each other, share ideas, and participate in activities to learn about RingCentral's products and APIs while earning points and rewards. The platform aims to learn and contribute to the community in a fun way.
The API allows 10 heavy, 40 medium, 50 light requests per user and 5 authorization requests; exceeding these thresholds will result in a 429 Too Many Requests HTTP error code. The document discusses that when requests begin exceeding limitations, servers commonly return responses with a 429 status.
RingCentral provides a sandbox environment for developers to test and build applications using its APIs. The summary outlines 5 steps to set up a RingCentral sandbox account:
1) Get a developer account on the RingCentral developer portal.
2) Log into the developer account and locate the sandbox account details.
3) Set the password for the sandbox account and log into the online account portal.
4) Create a test application on the developer dashboard by selecting the platform and feature to access via APIs.
5) The application API key and secret can then be used to test the application using the RingCentral APIs.
This document discusses server errors that can occur in the RingCentral application programming interface (API). Server errors have an HTTP status code in the 50x range and can be difficult to diagnose without access to server logs. Common causes include high traffic or maintenance. If the error persists, the RingCentral developer support team can investigate server logs to resolve the issue.
RingCentral offers communication services like VoIP calling, web meetings, and fax through a secure cloud phone system. Creating RingCentral applications is easy and can be done within minutes by logging into the RingCentral developer account. Once created, applications can access RingCentral APIs based on permissions and features. The RingCentral API uses specific error codes to simplify error processing for client applications. There are different categories of errors like server errors (HTTP 50x), authentication errors (HTTP 40x), and rate limiting errors (HTTP 429).
RingCentral provides call recording capabilities that allow recordings to be retrieved and streamed via their API. This enables uses cases like analyzing recordings to enhance customer interactions. RingCentral's call recording APIs support streaming playback of recordings using HTML5 audio tags without needing to download the file. Automatic call recording is not turned on by default and must be enabled in the account settings.
RingCentral is integrating with Amazon Alexa to allow users to access RingCentral's communication services like voip calling, meetings, and faxes using voice commands with Alexa. This integration allows users to start and control RingCentral meetings, send messages, and make calls just by talking to Alexa. The integration authenticates users with voice recognition and securely connects RingCentral services with Alexa's voice assistant capabilities.
RingCentral provides communication services like voice calls, SMS, fax, and online meetings through their APIs. They have SDKs available for many programming languages to make it easy for developers to integrate these services into their applications. The RingCentral SDKs handle tasks like authorization and provide helper functions for working with API requests and responses. This allows developers across different platforms and languages to quickly add powerful communication capabilities to their applications.
RingCentral integrates with Salesforce to provide seamless communication capabilities within the CRM. This improves customer retention, agent productivity, and business processes. The integration empowers the Salesforce system with RingCentral phone features and creates a more collaborative CRM experience. It allows users to create accounts, leads, contacts, and opportunities while on calls through a 360-degree view of customers. The integration is available on all browsers and devices and provides customized reports and dashboards along with accurate call and SMS logs to enhance the CRM experience.
The document describes how to connect RingCentral APIs to send SMS messages using Mulesoft Mule 4. It involves creating a Mule application with a flow containing an HTTP listener, payload transformer, and HTTP request to call the RingCentral send SMS API. The flow first calls the RingCentral authorization API to get an access token, stores it in a variable, and passes it in the header when calling the send SMS API. This allows the application to authenticate and send SMS texts via the RingCentral API using Mule 4.
RingCentral exposes powerful communication APIs that allow sending messages via SMS, fax, or call from applications integrated with RingCentral. It provides SDKs for languages like Java, PHP, Python, and JavaScript to facilitate API access and handle authentication. The SDKs package RingCentral's API functionality into easy-to-use libraries for each language and handle tasks like token management. This allows developers across platforms and languages to quickly build communication features into their applications.
RingCentral and Okta integrate to provide secure single sign-on and identity management for RingCentral users. This allows users to access their RingCentral phone numbers from any mobile device securely. New users created in an organization's Active Directory will be automatically provisioned to RingCentral. The integration provides enterprise single sign-on so users only need to authenticate once to access multiple applications. It improves security, efficiency and simplicity by reducing the need for multiple credentials across different applications.
RingCentral is a leading cloud-based communication system that integrates with Salesforce to improve customer retention, agent productivity, and business processes. The integration allows users to create and manage Salesforce records while on calls through RingCentral, providing a 360-degree view of customers. It seamlessly connects the phone system and CRM for improved workflows and increased productivity.
The document discusses RingCentral call logs and APIs. It provides the API URL to access call logs and notes that there is typically a 30 second delay before completed call details are available. It also mentions an API to access active/in-progress calls and that call log and call recording permissions are required. Finally, it notes administrators can enable/disable call log delivery and permissions.
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell
With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
Procurement Insights Cost To Value Guide.pptxJon Hansen
Procurement Insights integrated Historic Procurement Industry Archives, serves as a powerful complement — not a competitor — to other procurement industry firms. It fills critical gaps in depth, agility, and contextual insight that most traditional analyst and association models overlook.
Learn more about this value- driven proprietary service offering here.
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
HCL Nomad Web – Best Practices and Managing Multiuser Environmentspanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-and-managing-multiuser-environments/
HCL Nomad Web is heralded as the next generation of the HCL Notes client, offering numerous advantages such as eliminating the need for packaging, distribution, and installation. Nomad Web client upgrades will be installed “automatically” in the background. This significantly reduces the administrative footprint compared to traditional HCL Notes clients. However, troubleshooting issues in Nomad Web present unique challenges compared to the Notes client.
Join Christoph and Marc as they demonstrate how to simplify the troubleshooting process in HCL Nomad Web, ensuring a smoother and more efficient user experience.
In this webinar, we will explore effective strategies for diagnosing and resolving common problems in HCL Nomad Web, including
- Accessing the console
- Locating and interpreting log files
- Accessing the data folder within the browser’s cache (using OPFS)
- Understand the difference between single- and multi-user scenarios
- Utilizing Client Clocking
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul
Artificial intelligence is changing how businesses operate. Companies are using AI agents to automate tasks, reduce time spent on repetitive work, and focus more on high-value activities. Noah Loul, an AI strategist and entrepreneur, has helped dozens of companies streamline their operations using smart automation. He believes AI agents aren't just tools—they're workers that take on repeatable tasks so your human team can focus on what matters. If you want to reduce time waste and increase output, AI agents are the next move.
Complete Guide to Advanced Logistics Management Software in Riyadh.pdfSoftware Company
Explore the benefits and features of advanced logistics management software for businesses in Riyadh. This guide delves into the latest technologies, from real-time tracking and route optimization to warehouse management and inventory control, helping businesses streamline their logistics operations and reduce costs. Learn how implementing the right software solution can enhance efficiency, improve customer satisfaction, and provide a competitive edge in the growing logistics sector of Riyadh.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.