On my previous 3-part blog, I showed how Mule ESB can be used to service-enable and orchestrate traditional on-premise technologies like an Oracle database and IBM Websphere MQ. Using Mule ESB, we created a service that accessed employee information from an Oracle database table and transmitted this to IBM WebSphere MQ. An observant customer I was showing this to noticed a security flaw with how sensitive employee information was being transmitted in plain text and also asked how the employee record can be sent to SalesForce.com. This blog will show how these can be easily addressed using MuleSoft’s AnyPoint Platform. We’ll make use of the PGP encryption features from AnyPoint Enterprise Security to encrypt the data before sending it to WebSphere MQ. Then, we’ll create another message flow to retrieve this message, decrypt it and send it to SalesForce.com using the AnyPoint Connector for SalesForce.com.

Mule has a very extensive support for data stores, which covers pretty much the whole spectrum of what’s available out there, from key/value stores to document-oriented databases. The only piece that was missing in the puzzle was connectivity to a graph database: with the introduction of the Neo4j connector, the gap is now closed.

Popularized by the advent of social media, the need for efficiently storing, indexing, traversing and querying graphs of objects has become prominent in less than a decade. During this time, Neo4j has risen to the number one graph database on the market, with successful deployments across all types of industries and a strong commitment to open source.

The new connector, presented in this blog, allows Mule users to leverage the incredibly rich API that Neo4j offers with convenient configuration elements. Read on to discover a simple example built with this connector.

Have you already tried the Visual Flow Debugger? It’s one of the new shiny features that comes with Mule Studio Enterprise 3.4. Well, if you haven’t used it yet, this post is for you:

1. Message BrowsingAll the information you ever wanted, now at a click’s distance.

Before Visual Debugger, if you wanted to see the contents of the payload at each point you had to clutter your mule configuration with loggers all over the place, well, those days are over. Just put in a breakpoint et voilà!

You can also edit most values dynamically at runtime by clicking them.

Janet Revell on Monday, June 10, 2013

10 Little Mule Studio Gems

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Every so often, while using , I come across clever little gems that our team thoughtfully inserted into the product to improve usability. These gems don’t get a lot of fanfare, nor do they often warrant much attention on their own, but put together, they make for a smoother, intuitive user experience. Nearly invisible, they have become nearly indispensable to me.

 

#1 Wrap in and Extract to

is an ever-present concern for IT. It can be a rather daunting area when one considers all of the different possible dangers and the large variety of solutions to address them. But, the aim of Enterprise Security really just boils down to establishing and maintaining various levels of access control. Mule itself has always facilitated secure message processing both at the level of the transport, the service layer and of the message . Mule configurations can include all that Spring Security has to offer giving, for example, easy access to an LDAP server for authentication and authorisation. On top of that Mule Applications can apply WS-Security thus facilitating, for example, the validation of  incoming SAML messages. But in this post, rather than delve into all the details of the very extensive security feature set , I would rather approach the subject by considering the primary concerns that drive the need for security in a Service Oriented Architecture, how the industry as a whole has addressed those concerns, the consequent emergence of popular technologies based on this Industrial best practice and finally, the implementation of these technologies in Mule. 

At MuleSoft, we’ve been saying for years that point-to-point integration is evil. With time to market measured in minutes or hours, point-to-point integration projects measured in man-years are headed the way of the Dodo. And the no-software no-hardware model of iPaaS promises to shrink time to market even more.

But how fast can you deploy an enterprise-grade integration from scratch? We’re setting out to break preconceived notions of time to market with the 15-minute integration. Like the 4-minute mile before Roger Bannister, the 15-minute integration sounds like a myth. So is it for real?

As Nicolas pointed out in “7 things you didn’t know about DataMapper“, it’s not a trivial task to map a big file to some other data structure without eating up a lot of memory.

If the input is large enough, you might run out of memory: either while mapping or while processing the data in your flow-ref

Enabling the “” function in DataMapper makes this a lot easier (and efficient!).

just enable "streaming"

When we started working on the Mule High Availability () solution we wanted to create the simplest and most complete ESB HA solution out there. With Mule 3.4 we have further enhanced the capabilities of the Mule HA solution. In this blog post we would like to share with you some details about some of the the following highlight HA features of Mule 3.4:

  • Dynamic Scale Out
  • Unicast Cluster Discovery
  • Distributed Locking
  • Concurrent File Processing

If you think that telemetry should only be dealt with by Mr. Chekov, think again… When the “Internet of things” met publish/subscribe, the need for a lightweight messaging protocol became more acute. And this is when the MQ Telemetry Transport ( in acronym parlance) came into play. From its own words, this connectivity protocol “was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium“.

In a world where everything will eventually have an IP address, messages will be constantly flowing between devices, message brokers and service providers. And when there are messages to massage, who you gonna call? Mule ESB of course! With this new MQTT Connector, built on Dan Miller‘s solid ground work, the Internet of things, which had its rabbit, will now have its Mule!

In this blog we will look at an example where Mule is used to integrate conference booth QR Code scanners with an MQTT broker. Why using MQTT? If you’ve ever been to a technical conference and expo, you’ve surely tasted how abysmal the quality of a WIFI network can be. Beside confirming that the shoemaker’s children always go barefoot, it is also an encouragement for using a messaging protocol that’s both resilient and modest in its network needs. With this said, let’s first start by looking at the overall architecture diagram.

Apache Cassandra is a column-based, distributed database.  Until recently the only way to interact with databases from Mule was to reuse one of the existing Java clients, like Hector or Astyanax, in a component.  Mule’s Cassandra DB Module now provides message processors to insert, update, query and delete data in Cassandra.

To show off some of the features of the Cassandra module I’ll show implement a simple account management API.  This API will allow clients to perform CRUD operations on accounts, behaving similarly to something like an LDAP directory.