Accessing jms by groovy
•
0 likes•306 views
This document discusses how to filter messages from an ActiveMQ queue by JMS priority using Groovy script in Mule. It shows sending a message to the "StudioOUT" queue with a JMS priority of 9. Then it demonstrates consuming the message from the queue using a Groovy script filter to retrieve only the message with that priority of 9. The Groovy script allows filtering messages from the ActiveMQ queue by JMS priority when integrating with Mule.
Report
Share
Report
Share
Recommended
Create dynamic json using mule
This document discusses how to dynamically generate JSON payloads in Mule ESB flows. It shows how to use an Expression Transformer to generate a JSON string from flow variables and send it as a request to an external service. The JSON is constructed from flow variables that can be assigned values dynamically within the flow. Testing the flow by hitting a URL generates and posts the JSON, then logs the request and response. The Expression Transformer executes expressions to set the message payload, allowing dynamic JSON generation.
Map in Mule
Mule ESB allows storing data in variables that can be accessed throughout a flow or entire application. This document demonstrates how to define lists and maps as Mule variables, including using Groovy scripts or expression components to initialize variables as Java HashMaps and assign values. Various techniques are shown for declaring map variables directly, or initializing and assigning them to flow variables for later use in a Mule flow.
Power Mta 4.0
I have a knowledge of power mta 4.0 or 3.5 use for bulk mail if you want to purchase power Mta video tutorial contact me powermta4.0@gmail.com and also gud knowledge of virtual mta,Dkim,Spf
Mulesoft Using Groovy Component
The document discusses using a Groovy component in Mulesoft to pause a process for a specified time interval before continuing. It provides a sample use case where a file is polled from a directory, held for 3 seconds using a Groovy component script containing the Java sleep method, and then pushed to an outbound target location. Configuration and testing details are included to demonstrate implementing the pause functionality with Groovy in Mulesoft.
power mta
This video tutorial covers the configuration and use of PowerMTA, an email server software. It discusses topics like installation, licensing, DNS configuration, features, setting up virtual servers, queue management, spam filtering through SPF and DKIM, debugging, and integrating with other applications. The full tutorial is available by contacting the presenter.
Activemq installation and master slave setup using shared broker data
This document discusses how to set up an Apache ActiveMQ master-slave topology using a shared broker data directory. It provides instructions for installing ActiveMQ on two machines, configuring the brokers, and starting them in a way that the slave will take over if the master fails. The master and slave brokers are configured to share a data directory so they can maintain the same state across the cluster.
Mule SOAP Router
The document discusses the Mule SOAP Router component in MuleSoft. The SOAP Router allows messages to be routed to multiple outbound endpoints after processing. It is configured with routing constraints and a catch-all strategy. The configuration wizard for the SOAP Router allows setting the display name, global router configuration reference, WSDL location, service address, and port number. Additional notes can also be added in the notes tab.
testing1
This document discusses the differences between using var and let to declare variables in JavaScript. It explains that variables declared with var are hoisted to the top of their scope and can cause unexpected behavior. Variables declared with let are block scoped and not hoisted, avoiding issues like variables being visible outside their intended scope. The document demonstrates a function that displays different loading messages depending on the number of arguments. It shows how using var can cause errors but using let produces more predictable behavior aligned with other programming languages.
Recommended
Create dynamic json using mule
This document discusses how to dynamically generate JSON payloads in Mule ESB flows. It shows how to use an Expression Transformer to generate a JSON string from flow variables and send it as a request to an external service. The JSON is constructed from flow variables that can be assigned values dynamically within the flow. Testing the flow by hitting a URL generates and posts the JSON, then logs the request and response. The Expression Transformer executes expressions to set the message payload, allowing dynamic JSON generation.
Map in Mule
Mule ESB allows storing data in variables that can be accessed throughout a flow or entire application. This document demonstrates how to define lists and maps as Mule variables, including using Groovy scripts or expression components to initialize variables as Java HashMaps and assign values. Various techniques are shown for declaring map variables directly, or initializing and assigning them to flow variables for later use in a Mule flow.
Power Mta 4.0
I have a knowledge of power mta 4.0 or 3.5 use for bulk mail if you want to purchase power Mta video tutorial contact me powermta4.0@gmail.com and also gud knowledge of virtual mta,Dkim,Spf
Mulesoft Using Groovy Component
The document discusses using a Groovy component in Mulesoft to pause a process for a specified time interval before continuing. It provides a sample use case where a file is polled from a directory, held for 3 seconds using a Groovy component script containing the Java sleep method, and then pushed to an outbound target location. Configuration and testing details are included to demonstrate implementing the pause functionality with Groovy in Mulesoft.
power mta
This video tutorial covers the configuration and use of PowerMTA, an email server software. It discusses topics like installation, licensing, DNS configuration, features, setting up virtual servers, queue management, spam filtering through SPF and DKIM, debugging, and integrating with other applications. The full tutorial is available by contacting the presenter.
Activemq installation and master slave setup using shared broker data
This document discusses how to set up an Apache ActiveMQ master-slave topology using a shared broker data directory. It provides instructions for installing ActiveMQ on two machines, configuring the brokers, and starting them in a way that the slave will take over if the master fails. The master and slave brokers are configured to share a data directory so they can maintain the same state across the cluster.
Mule SOAP Router
The document discusses the Mule SOAP Router component in MuleSoft. The SOAP Router allows messages to be routed to multiple outbound endpoints after processing. It is configured with routing constraints and a catch-all strategy. The configuration wizard for the SOAP Router allows setting the display name, global router configuration reference, WSDL location, service address, and port number. Additional notes can also be added in the notes tab.
testing1
This document discusses the differences between using var and let to declare variables in JavaScript. It explains that variables declared with var are hoisted to the top of their scope and can cause unexpected behavior. Variables declared with let are block scoped and not hoisted, avoiding issues like variables being visible outside their intended scope. The document demonstrates a function that displays different loading messages depending on the number of arguments. It shows how using var can cause errors but using let produces more predictable behavior aligned with other programming languages.
Adding dynamic file
The document discusses how to dynamically set file connector attributes at runtime in Mule. It provides an example where a JSON file specifies a folder, file pattern, and work directory. The Mule flow reads the file, parses the JSON to extract these values, and then sets them as attributes on the file outbound endpoint to dispatch the file dynamically based on the values in the JSON file. This demonstrates how Mule allows configuring connectors dynamically rather than just at design time.
Xml transform
This document discusses using XSLT to transform XML payloads in Mule applications. It provides an example of transforming an input XML payload containing name and id elements to an output XML payload that adds a designation element. The Mule flow uses an XSLT transformer applied to the input XML, along with an XSL stylesheet, to produce the modified output XML payload. Testing the flow by deploying it and sending a request demonstrates how XSLT can easily transform XML payloads in Mule.
Introduction java messaging services
This document provides an overview of Java Messaging Service (JMS) including the core JMS concepts of queues and topics, how messages flow through a JMS system using publishers and subscribers, and how to configure JMS connectors and endpoints in Mule. It describes the two messaging models of JMS - point-to-point queues and publish-subscribe topics. It also covers backchannels, message filtering, transformers between JMS and Java types, and an example ActiveMQ configuration with inbound and outbound endpoints.
Using map and list starter
This document discusses how to define lists and maps using Mule variables. It shows that lists and maps can be defined by assigning a Mule variable to a value expressed as a JSON array or object. Values can then be retrieved from the list or map using array indexing or object properties. The document provides an example Mule flow that demonstrates defining, accessing, and modifying list variable values.
Finance connectors with mule
- Financial institutions need a new way to connect traditional delivery channels with emerging digital business models as they struggle with operational inefficiencies, disparate systems, legacy technology, and security concerns.
- Mulesoft provides connectors that allow for syncing of data and automation of processes between applications like QuickBooks, Avalara, FreshBooks, PayPal, and Zuora to help address these issues.
- The connectors enable seamless integration and consumption of APIs to help reduce double data entry and automate common business processes.
Multi database access
The document discusses using the Scatter-Gather routing message processor in Mule to retrieve data from multiple databases concurrently. It demonstrates a flow that retrieves data from both a SQL Server and PostgreSQL database in parallel using Scatter-Gather and aggregates the results into a single payload. The flow is tested by hitting a URL that returns the combined data from both databases.
Request in mule
The Mule requester component allows messages to be retrieved from an inbound endpoint, like JMS or HTTP, in the middle of a Mule flow. It can load a file, fetch messages from a JMS queue, or pull messages from a mail server on demand. In the example, a Mule requester fetches a message from a JMS queue named "input" when an HTTP URL is hit, and the message payload is logged.
Expression language in mule
The document provides an overview of Mule Expression Language (MEL), including its main uses and syntax. MEL allows accessing and manipulating message properties and context through expressions. Key points include:
- MEL expressions can make decisions, select values, replace tokens, and act as scripts using message properties and context.
- Syntax is Java-like with property expressions to access message fields. Operators include arithmetic, comparison, logical, and control flow statements.
- Context objects model the message, server, Mule instance, and application to access environment properties in expressions.
- Variables store expression values globally or across flows. Functions extract data through XPath and regex expressions.
Using groovy in mule
This document discusses how to use Groovy scripts in Mule to filter messages sent to and received from an ActiveMQ queue based on JMS priority. Specifically, it shows sending a message to a queue with a priority of 9, and then using a Groovy script to consume only messages from that queue with a priority of 9.
Accessing jms in mule using groovy
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 filter works by consuming the message from the queue that was sent with a priority of 9.
Accessing jms in mule using groovy
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.
Accessing jms in mule using groovy
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.
Filtering jms messages with mule
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.
Running mule from java
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.
Simple http and jms for beginners
used of HTTP as inbound endpoint and JMS as inbound and outbound with ACTIVE mq for messaging technology
Mule with stored procedure
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in a database like MS SQL, then call it from a Mule flow by configuring the database connector and invoking the stored procedure using SQL. Testing the application by hitting a URL in the browser returns results from executing the stored procedure in the Mule console.
Mule with stored procedure
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in a database like MS SQL, then call it from a Mule flow by configuring the database connector and invoking the stored procedure using SQL. Testing the application by hitting a URL in the browser returns results from executing the stored procedure in the Mule console.
Stored procedure in Mule
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in a database like MS SQL, then call it from a Mule flow by configuring the database connector and invoking the stored procedure using SQL. Testing the application by hitting a URL in the browser returns results from executing the stored procedure in the Mule console.
Mule stored procedure
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in a database like MS SQL, then call it from a Mule flow by configuring the database connector and invoking the stored procedure using SQL. Testing the application by hitting a URL in the browser confirms that the stored procedure runs successfully from Mule.
Deploying and running in mule standalone
Mule Standalone Server is a proprietary application server that can host Java code, web services, and Mule applications. To deploy a Mule application, it must be exported from Anypoint Studio as a zip file and copied to the Mule Standalone app folder. Starting the mule.bat file launches the Mule Standalone server, deploying any applications in the app folder. The document then walks through creating a simple Mule application in Anypoint Studio, exporting and deploying it to the Mule Standalone server, and invoking it to confirm it is running successfully.
Deploying and running in mule standalone
Mule Standalone Server is a proprietary application server that can host Java code, web services, and Mule applications. To deploy a Mule application, it must be exported from Anypoint Studio as a zip file and copied to the app folder of the Mule Standalone server. Once the zip file is copied over, the Mule Standalone server can be started using the mule.bat file in the bin folder. When the server starts, it will deploy the Mule application. The application can then be accessed through its exposed URL.
Deploying and Running in Mule
Mule Standalone Server is a proprietary application server that can host Java code, web services, and Mule applications. To deploy a Mule application, it must be exported from Anypoint Studio as a zip file and copied to the Mule Standalone app folder. Starting the mule.bat file launches the Mule Standalone server, deploying any applications in the app folder. The document then walks through creating a simple Mule application in Anypoint Studio, exporting and deploying it to the Mule Standalone server, and invoking it to confirm it is running successfully.
More Related Content
Viewers also liked
Adding dynamic file
The document discusses how to dynamically set file connector attributes at runtime in Mule. It provides an example where a JSON file specifies a folder, file pattern, and work directory. The Mule flow reads the file, parses the JSON to extract these values, and then sets them as attributes on the file outbound endpoint to dispatch the file dynamically based on the values in the JSON file. This demonstrates how Mule allows configuring connectors dynamically rather than just at design time.
Xml transform
This document discusses using XSLT to transform XML payloads in Mule applications. It provides an example of transforming an input XML payload containing name and id elements to an output XML payload that adds a designation element. The Mule flow uses an XSLT transformer applied to the input XML, along with an XSL stylesheet, to produce the modified output XML payload. Testing the flow by deploying it and sending a request demonstrates how XSLT can easily transform XML payloads in Mule.
Introduction java messaging services
This document provides an overview of Java Messaging Service (JMS) including the core JMS concepts of queues and topics, how messages flow through a JMS system using publishers and subscribers, and how to configure JMS connectors and endpoints in Mule. It describes the two messaging models of JMS - point-to-point queues and publish-subscribe topics. It also covers backchannels, message filtering, transformers between JMS and Java types, and an example ActiveMQ configuration with inbound and outbound endpoints.
Using map and list starter
This document discusses how to define lists and maps using Mule variables. It shows that lists and maps can be defined by assigning a Mule variable to a value expressed as a JSON array or object. Values can then be retrieved from the list or map using array indexing or object properties. The document provides an example Mule flow that demonstrates defining, accessing, and modifying list variable values.
Finance connectors with mule
- Financial institutions need a new way to connect traditional delivery channels with emerging digital business models as they struggle with operational inefficiencies, disparate systems, legacy technology, and security concerns.
- Mulesoft provides connectors that allow for syncing of data and automation of processes between applications like QuickBooks, Avalara, FreshBooks, PayPal, and Zuora to help address these issues.
- The connectors enable seamless integration and consumption of APIs to help reduce double data entry and automate common business processes.
Multi database access
The document discusses using the Scatter-Gather routing message processor in Mule to retrieve data from multiple databases concurrently. It demonstrates a flow that retrieves data from both a SQL Server and PostgreSQL database in parallel using Scatter-Gather and aggregates the results into a single payload. The flow is tested by hitting a URL that returns the combined data from both databases.
Request in mule
The Mule requester component allows messages to be retrieved from an inbound endpoint, like JMS or HTTP, in the middle of a Mule flow. It can load a file, fetch messages from a JMS queue, or pull messages from a mail server on demand. In the example, a Mule requester fetches a message from a JMS queue named "input" when an HTTP URL is hit, and the message payload is logged.
Expression language in mule
The document provides an overview of Mule Expression Language (MEL), including its main uses and syntax. MEL allows accessing and manipulating message properties and context through expressions. Key points include:
- MEL expressions can make decisions, select values, replace tokens, and act as scripts using message properties and context.
- Syntax is Java-like with property expressions to access message fields. Operators include arithmetic, comparison, logical, and control flow statements.
- Context objects model the message, server, Mule instance, and application to access environment properties in expressions.
- Variables store expression values globally or across flows. Functions extract data through XPath and regex expressions.
Using groovy in mule
This document discusses how to use Groovy scripts in Mule to filter messages sent to and received from an ActiveMQ queue based on JMS priority. Specifically, it shows sending a message to a queue with a priority of 9, and then using a Groovy script to consume only messages from that queue with a priority of 9.
Viewers also liked (9)
Similar to Accessing jms by groovy
Accessing jms in mule using groovy
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 filter works by consuming the message from the queue that was sent with a priority of 9.
Accessing jms in mule using groovy
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.
Accessing jms in mule using groovy
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.
Filtering jms messages with mule
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.
Running mule from java
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.
Simple http and jms for beginners
used of HTTP as inbound endpoint and JMS as inbound and outbound with ACTIVE mq for messaging technology
Mule with stored procedure
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in a database like MS SQL, then call it from a Mule flow by configuring the database connector and invoking the stored procedure using SQL. Testing the application by hitting a URL in the browser returns results from executing the stored procedure in the Mule console.
Mule with stored procedure
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in a database like MS SQL, then call it from a Mule flow by configuring the database connector and invoking the stored procedure using SQL. Testing the application by hitting a URL in the browser returns results from executing the stored procedure in the Mule console.
Stored procedure in Mule
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in a database like MS SQL, then call it from a Mule flow by configuring the database connector and invoking the stored procedure using SQL. Testing the application by hitting a URL in the browser returns results from executing the stored procedure in the Mule console.
Mule stored procedure
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in a database like MS SQL, then call it from a Mule flow by configuring the database connector and invoking the stored procedure using SQL. Testing the application by hitting a URL in the browser confirms that the stored procedure runs successfully from Mule.
Deploying and running in mule standalone
Mule Standalone Server is a proprietary application server that can host Java code, web services, and Mule applications. To deploy a Mule application, it must be exported from Anypoint Studio as a zip file and copied to the Mule Standalone app folder. Starting the mule.bat file launches the Mule Standalone server, deploying any applications in the app folder. The document then walks through creating a simple Mule application in Anypoint Studio, exporting and deploying it to the Mule Standalone server, and invoking it to confirm it is running successfully.
Deploying and running in mule standalone
Mule Standalone Server is a proprietary application server that can host Java code, web services, and Mule applications. To deploy a Mule application, it must be exported from Anypoint Studio as a zip file and copied to the app folder of the Mule Standalone server. Once the zip file is copied over, the Mule Standalone server can be started using the mule.bat file in the bin folder. When the server starts, it will deploy the Mule application. The application can then be accessed through its exposed URL.
Deploying and Running in Mule
Mule Standalone Server is a proprietary application server that can host Java code, web services, and Mule applications. To deploy a Mule application, it must be exported from Anypoint Studio as a zip file and copied to the Mule Standalone app folder. Starting the mule.bat file launches the Mule Standalone server, deploying any applications in the app folder. The document then walks through creating a simple Mule application in Anypoint Studio, exporting and deploying it to the Mule Standalone server, and invoking it to confirm it is running successfully.
Deploying and running in mule standalone
Mule Standalone Server is a proprietary application server that can host Java code, web services, and Mule applications. To deploy a Mule application, it must be exported from Anypoint Studio as a zip file and copied to the app folder of the Mule Standalone server. Once the zip file is copied over, the Mule Standalone server can be started from its bin folder, which will deploy and run the Mule application. The success of the deployment can be verified by checking for an anchor file and by hitting the application URL.
Stored procedure in mule
The document discusses how to call stored procedures from a Mule application. It explains that Mule's database component allows connecting to JDBC databases and running SQL operations like stored procedures. It then demonstrates creating a stored procedure in MS SQL, calling it from a Mule flow, and testing the application.
Mule with stored procedure
The document discusses how to call stored procedures from a Mule application using Mule's database component. It explains how to create a stored procedure in MS SQL, call the stored procedure from a Mule flow, and test the application by hitting a URL that triggers the flow. The goal is to demonstrate how simple it is to leverage stored procedures through Mule's database capabilities.
Deploying and running mule standalone
This document provides instructions for deploying and running a simple Mule application on a Mule Standalone server. It explains that a Mule application needs to be exported from Anypoint Studio as a deployable ZIP file and copied to the Mule Standalone server's app folder. It then describes starting the Mule Standalone server from the bin folder and viewing the application running at a URL to confirm successful deployment and operation.
Jms queues
This document discusses using Java Message Service (JMS) with MuleSoft's messaging integration capabilities. It describes the two JMS messaging models of queues and topics. It then provides a step-by-step example of configuring Mule to use ActiveMQ for JMS integration, including importing libraries, creating a connector, sending messages to a queue, and receiving messages with a subscriber.
Cache for community edition
This document discusses implementing a cache in Mule Community Edition using a custom interceptor since Community Edition does not include the cache scope feature of Enterprise Edition. It provides an example of a custom interceptor class that caches payload data and includes a code sample. The document then demonstrates configuring and testing the custom cache interceptor on a web service that retrieves database records, showing that it continues returning the cached response even after the underlying database record is deleted.
Cache for community edition
This document discusses implementing a cache in Mule Community Edition using a custom interceptor, as the cache scope is only available in the Enterprise Edition. It provides an example of a custom interceptor that caches payload data and applies a filter expression. The flow is divided into two parts connected by a VM component. The interceptor is used and configured with a filter expression property. Testing is done using SoapUI to call the web service and validate that the cached response is returned even after deleting the cached data from the database.
Similar to Accessing jms by groovy (20)
More from Son Nguyen
Wsdl connector introduction
A WSDL endpoint in Mule allows invoking web services without generating a client. At runtime, it reads the WSDL to determine how to call the remote service and constructs SOAP messages using the payload and WSDL. CXF and Axis provide WSDL connectors that create a dynamic proxy for the service. The WSDL URL and operation must be specified. CXF is generally recommended, but Axis may be needed if CXF has issues. Multiple arguments can be passed from the console to invoke a service and output the response.
Android intergrate with mule
This document provides steps for integrating an Android application with a Mule API. It outlines how to build an Android app that can make requests to a backend service API, connect from the Android app to the service API using Retrofit, and test and publish the application. The steps include creating a new Android project, setting up permissions, applying Retrofit to connect to the API, accessing MuleSoft's Anypoint Platform to update and test the API, and then publishing the final application.
Mule flow overview
This document provides an overview of a Mule Flow that includes a custom filter. It shows how to configure an XML flow definition that receives an HTTP request, logs the payload, converts the XML payload to JSON, and passes it through a custom Java class filter. The flow demonstrates how to build a custom filter that can manipulate the payload as needed within a Mule application.
Mule flow and filter
This document describes a Mule Flow that uses a custom Java filter class to filter XML payload data converted to JSON. The flow contains an HTTP listener, XML to JSON transformer, and custom filter that can manipulate the JSON data as needed before it is logged. The full XML configuration for the flow is provided.
Handle exceptions in mule
Mule provides default exception handling but also allows custom exception strategies to implement more precise error management. Exception strategies include choice, catch, and rollback strategies. A rollback strategy reverts a message flow to an earlier state if an exception occurs during a transactional flow.
Spring security integrate with mule
This document discusses how to integrate Spring Security as a security manager in Mule. It provides an example configuration that uses an in-memory database of users for authentication. Key aspects covered include configuring Spring Security providers, adding the necessary XML schema references, using security filters on connectors to handle authentication and attaching credentials to requests, and defining a proxy bean to apply security interceptors.
Message processor in mule
The document summarizes various message processors in Mule, including their purpose and example usage. Key message processors described include:
- All - Sends the same message to multiple targets.
- Async - Runs a chain of processors in a separate thread.
- Choice - Sends a message to the first matching processor.
- Collection Aggregator - Groups messages by correlation ID before forwarding.
- Collection Splitter - Splits messages whose payload is a collection.
- Custom processors - Allow custom message processing logic.
- Filtering processors - Filter messages based on properties.
- Routing processors - Route messages in various ways like first successful, round robin, etc.
Mule with data weave
The document discusses various ways to transform data and integrate systems using Mule and DataWeave including: calling global MEL functions from DataWeave; using the read, write, and log functions; and calling external flows. It provides code examples for defining global functions, reading and writing different data formats, logging output, and triggering other flows.
Using spring scheduler mule
Using Spring Scheduler Mule allows scheduling tasks in Mule applications using a Java class and Spring task scheduler. This provides an alternative to using Quartz scheduler or Poll components. The document demonstrates creating a Java class that prints a message every 10 seconds, and configuring the Spring task scheduler in Mule to trigger that class repeatedly to continuously monitor an application.
Composite source in bound and out-bound
The document discusses using a composite source in Mule to accommodate multiple inbound endpoints to trigger a flow. A composite source listens in parallel on different channels for incoming messages, and passes any received message to the first message processor to trigger the flow. This allows a single flow to be triggered by messages from different inbound endpoints like HTTP, file, FTP, etc. The document provides an example where a flow is triggered either by a Quartz scheduled task or by an HTTP request using a composite source with both endpoints.
Batch job processing
The document discusses batch job processing in Mule. It describes a use case where a batch job queries a database to retrieve approved users and generates a CSV file with the user account attributes. Batch processing in Mule allows splitting messages into records, performing actions on each record, and reporting results. It is useful for integrating or synchronizing data sets, ETL processes, and handling large amounts of incoming data.
Using message enricher
The document discusses using a message enricher in Mulesoft to enrich messages by adding additional data from external sources. It provides an example of using an enricher to lookup the two-letter state based on the zip code contained in the original message. The enricher calls an external resource, retrieves the state, and adds it to the message as a new variable before sending it to the target system. This allows target systems to receive messages with more information than what the source system originally provided.
Google drive connection
The document discusses various Google connectors provided by MuleSoft that allow integration between Google services like Prediction, Tasks, Contacts, Calendar, and Spreadsheets with third party applications. It provides descriptions of each connector and explains how they enable access and synchronization of data between the Google services and applications like Salesforce, Outlook, and Excel through APIs.
Using properties in mule
Properties files allow configuration values to be externalized and reused throughout a Mule application. Property placeholders can be used to reference values from properties files, enabling environment-specific configurations. Values for placeholders can come from global properties, property files, environment variables, or runtime arguments, with earlier sources taking priority.
Mule integrate with microsoft
MuleSoft provides solutions for integrating Microsoft applications and platforms like .NET, Microsoft SharePoint, Microsoft Dynamics CRM, and MSMQ. This allows organizations to leverage existing Microsoft investments by connecting applications across diverse codebases on-premises and in the cloud on a modern integration platform. MuleSoft's solutions for Microsoft give IT organizations using .NET the ability to connect different applications and services wherever needed.
Jms queue
This document discusses JMS queues and provides an example of using a JMS queue with ActiveMQ. It describes JMS and the queue model, then provides steps to set up a Mule application that enqueues messages to an ActiveMQ queue using HTTP and receives messages from the queue using JMS. The example shows importing ActiveMQ libraries, creating an ActiveMQ connector, adding messages to the queue from HTTP and receiving them from the queue using JMS.
Anypoint connectors
Anypoint Connectors provide integration solutions for connecting applications, data, and devices across cloud and on-premises systems using a variety of pre-built connectors available on Anypoint Exchange. Connectors include those for ActiveMQ, Cassandra DB, generic databases, Amazon S3, Amazon SQS, Oracle Siebel, and more. They allow for CRUD operations and data synchronization between the target systems and MuleSoft's Anypoint Platform.
Mule esb basic introduction
Mule ESB is an open source enterprise service bus (ESB) that allows for lightweight and flexible integration. It uses a loosely coupled architecture and supports major protocols and technologies. Mule ESB uses a staged event-driven (SEDA) architecture that decomposes services into stages for modularity and code reuse. It processes messages using universal message objects (UMOs) and endpoints, with transports handling specific protocols and connectors sending/receiving messages. Transformation and routing are done through transformers, routers, and an exception strategy handles errors.
Runing batch job in mule
Mule ESB allows processing of messages in batches. A batch job splits large messages into individual records, processes each record through batch steps, and generates a report with results. Batch jobs are useful for processing large datasets from APIs, databases, or between applications. The key parts of a batch job are the input phase, loading phase, processing phase, and completion phase.
Mule integration with cloud hub
CloudHub is a platform as a service component of Anypoint Platform that provides a fully-managed, multi-tenanted, globally available, secure, and highly available cloud platform for integration and APIs. With CloudHub, users do not need to maintain hardware or manage software updates. It offers a fully-managed cloud environment and built-in elastic scalability.
More from Son Nguyen (20)
Recently uploaded
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
In this blog post, we'll delve into the intersection of AI and app development in Saudi Arabia, focusing on the food delivery sector. We'll explore how AI is revolutionizing the way Saudi consumers order food, how restaurants manage their operations, and how delivery partners navigate the bustling streets of cities like Riyadh, Jeddah, and Dammam. Through real-world case studies, we'll showcase how leading Saudi food delivery apps are leveraging AI to redefine convenience, personalization, and efficiency.
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Recently uploaded (20)
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Accessing jms by groovy
- 1. ACCESSING JMS BY GROOVY
- 2. We have earlier seen how to integrate Mule and ActiveMQ . We have also seen how to send message to ActiveMQ queue from Mule with a specific JMS priority and to fetch messages from ActiveMQ queue filtering a particular JMS Here we will see how to filter messages to ActiveMQ from Groovy script with a particular JMS priority
- 3. Yes, we can filter payload using Groovy script for the message from ActiveMQ queue with a particular JMS priority ….
- 4. Let’s consider we have a simple Mule flow as following:- Here in we will send messages to an ActiveMQ queue with a particular JMS priority. Let’s say our queue name be StudioOUT. So we will be sending messages to queue StudioOUT.
- 5. So, our Mule config will be as following:- You can see we are sending message to queue StudioOUT with JMS priority 9 using the following code :- <message-properties-transformer> <add-message-property key="Priority" value="9"/> </message-properties-transformer>
- 6. Now, we will test the flow by hitting url http://localhost:8081/test the message will be send to queue with JMS priority 9 :-
- 7. And, we will also check our ActiveMQ portal we will find the following :- You can see the message priority is 9 as we have configured JMS priority in our flow
- 8. Now let’s design a flow which will receive message from ActiveMQ queue and filter it by JMS priority using Groovy script:- You can see we have used Groovy Script to fetch the message from queue StudioOUT
- 9. Now our Mule config which is using Groovy script is as follow:-
- 10. Now if we start our application again we will get as follow:-
- 11. And you can see it has consumed the message from queue which has the JMS priority 9 :- Thus, you can see that we can also set the filter to consume message from ActiveMQ using Groovy
- 12. In my next slide I will bring some other techniques in Mule implementation . Hope you have enjoyed this simpler version. Keep sharing your knowledge and let our Mule community grow