Mule 3 vs Mule 4: 6 Reasons Why You Should Upgrade?

When it’s about connecting devices, data, and applications over Software-as-a-Service (SaaS), on-premises, APIs, and enterprise’s legacy systems, MuleSoft is a name that companies love to count on. The newly released Mule 4 incorporates advanced features such as self-tuning engines, which has led to the rise in demand for MuleSoft integration services across disparate industries.

Mule 4 is the new runtime offered by MuleSoft, uniquely designed to boost the developer’s confidence through highly advanced MuleSoft API management. It reduces the steps to develop applications by approximately 50%. It delivers enhanced connectivity and supports super-fast data access, which aids the learning experiences of developers. Let’s briefly understand why Mule 4 is worth upgrading to and demystify various other MuleSoft’s newly released runtime’s capabilities.

Mule 3 vs Mule 4: Differences from the Perspective of a MuleSoft Certified Developer

Mule 3 gained market traction for a long time, and post the version upgrade to Mule 4, and its reputation preceded it. Special attention was given while developing Mule 4. It is designed to boost scalability and support performance optimization and smooth upgrade. Let’s understand why you should.

• Self-Tuning Engine
  With Mule 4, there is no need to tune the runtime manually. It incorporates the self-tuning engine, allowing developers to reap the benefits of non-blocking I/O (Input/Output) calls. A self-tuning engine automates the strategy-processing configuration, dynamically adjusts the runtime, and makes it apt for disparate workloads. Thus, it essentially helps evade performance bottlenecks.
  With the Mule 4 self-tuning engine, the developers need not manually perform various steps such as threading configuration and data-pattern exchange declaration. Developers can put the entire application flow development in a single thread.


• Thread Pools
  In Mule 4, thread pooling is not done manually. The programs use the Mule runtime’s default values, which is self-tuned. However, if there is a need to do it manually, the users should configure the minimum and maximum thread values with the wait time in the conf/scheduler-pools.conf file.

  1. org.mule.runtime.scheduler.io.threadPool.threadKeepAlive=30000

  2. org.mule.runtime.scheduler.cpuLight.threadPool.size=2*cores

  3. org.mule.runtime.scheduler.cpuIntensive.workQueue.size=30000

  
  Each different connector in the Mule runtime hints the users if it is IO Intensive, CPU Light, or CPU Intensive, allowing the runtime to self-tune. In case the Mule connectors do not provide users with any hints, the Mule 4 runtime will automatically analyze the connector codes and assign thread pools.
• Error Handling
  Mule 3 did not help in identifying the nature of errors. Earlier, the handling of errors was based on Java exception, which followed a trial and error approach. Therefore, Mule 3 made the developers work lengthy and tiresome.
  On the contrary, Mule 4 supports error detection and handling during the design development time. The errors are neatly typed and come with detailed problem statements. Thus, it becomes convenient for developers to identify and resolve errors instantly.

• DataWeave 2.0
  The earlier versions of Mule runtime consisted of DataWeave and Mule Expression Language (MEL), which delivered a consistent experience while handling expressions such as JSON. However, it could not manage transformations of data and mule messages and created a fragmented environment.
  In Mule4, MEL is not used as the messages directly pass through a connector. Mule 4 incorporates DataWeave 2.0, which is an extended version of its default expression language—DataWeave. It organizes and simplifies the data and makes it easy to learn. Moreover, it boosts the integration abilities by helping create reusable codes to allow users to instantly access data without the need to transform them. The data and messages stored in JSON and CSV, or any other forms, can be directly accessed by creating expressions.

• Smooth User-Experiences
  The following charts will help you better understand the difference in the application flow development of Mule 4 and Mule 3.

Mule 4 Flow:

XML

<?xml version="1.0" encoding="UTF-8"?>

<mule xmlns:ee="http://www.mulesoft.org/schema/mule/ee/core" 
xmlns:file="http://www.mulesoft.org/schema/mule/file"
    xmlns:batch="http://www.mulesoft.org/schema/mule/batch"
    xmlns="http://www.mulesoft.org/schema/mule/core" 
xmlns:doc="http://www.mulesoft.org/schema/mule/documentation" 
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" 
xsi:schemaLocation="http://www.mulesoft.org/schema/mule/core 
http://www.mulesoft.org/schema/mule/core/current/mule.xsd
http://www.mulesoft.org/schema/mule/batch 
http://www.mulesoft.org/schema/mule/batch/current/mule-batch.xsd
http://www.mulesoft.org/schema/mule/file 
http://www.mulesoft.org/schema/mule/file/current/mule-file.xsd
http://www.mulesoft.org/schema/mule/ee/core 
http://www.mulesoft.org/schema/mule/ee/core/current/mule-ee.xsd">
    <flow name="MainProcessFlow" 
doc:id="c97e2dd9-62ca-4e9a-9635-0ae49cbf81c1" 
maxConcurrency="100">
        <file:listener doc:name="On New or Updated File" 
doc:id="92383749-1e3c-4990-bde1-36fc79449d04" 
directory="Desktop/files/input" 
moveToDirectory="Desktop/files/output">
            <scheduling-strategy>
                <fixed-frequency />
            </scheduling-strategy>
            <file:matcher filenamePattern="data.csv" />
        </file:listener>
            <ee:transform doc:name="Transform Message" 
doc:id="5a90ba3a-3919-4747-8e24-d24ee547c7c1">
            <ee:message>
                <ee:set-payload><![CDATA[%dw 2.0
output application/java
---
payload]]></ee:set-payload>
            </ee:message>
        </ee:transform>
        
        <flow-ref doc:name="Flow Reference" 
doc:id="5f006dd3-9fa5-49d4-86d3-7568e6cce714" 
name="BatchProcessFlow" />
    </flow>
    <flow name="BatchProcessFlow" doc:id="6d282a48-c024-4aea-b9ff-bcc9a5df018f" 
maxConcurrency="100">
        <batch:job jobName="BatchProcessBatch_Job" 
doc:id="b5d08ba2-651b-45f4-8fdc-7c36c18c3ee9" 
blockSize="1000" maxConcurrency="100">
            <batch:process-records >
                <batch:step name="Batch_Step" 
doc:id="a30ffad3-37d0-4af0-920e-95f43271b345" >
                    <logger level="INFO" 
doc:name="Print Logger" 
doc:id="85963f29-dd61-435d-aa8e-3b37fd3f1de8" 
message="------------#[payload]"/>
                </batch:step>
            </batch:process-records>
        </batch:job>
    </flow>
</mule>

Mule 3 Flow

XML

<?xml version="1.0" encoding="UTF-8"?>

<mule xmlns:tracking="http://www.mulesoft.org/schema/mule/ee/tracking"
    xmlns:email="http://www.mulesoft.org/schema/mule/email" xmlns:metadata="http://www.mulesoft.org/schema/mule/metadata"
    xmlns:file="http://www.mulesoft.org/schema/mule/file" xmlns:dw="http://www.mulesoft.org/schema/mule/ee/dw"
    xmlns:batch="http://www.mulesoft.org/schema/mule/batch" xmlns="http://www.mulesoft.org/schema/mule/core"
    xmlns:doc="http://www.mulesoft.org/schema/mule/documentation"
    xmlns:spring="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-current.xsd
http://www.mulesoft.org/schema/mule/core http://www.mulesoft.org/schema/mule/core/current/mule.xsd
http://www.mulesoft.org/schema/mule/file http://www.mulesoft.org/schema/mule/file/current/mule-file.xsd
http://www.mulesoft.org/schema/mule/batch http://www.mulesoft.org/schema/mule/batch/current/mule-batch.xsd
http://www.mulesoft.org/schema/mule/ee/dw http://www.mulesoft.org/schema/mule/ee/dw/current/dw.xsd
http://www.mulesoft.org/schema/mule/email http://www.mulesoft.org/schema/mule/email/current/mule-email.xsd
http://www.mulesoft.org/schema/mule/ee/tracking http://www.mulesoft.org/schema/mule/ee/tracking/current/mule-tracking-ee.xsd">
    <non-blocking-processing-strategy
        name="Non_Blocking_Processing_Strategy"

maxThreads="500" minThreads="50"
        threadTTL="1000"

doc:name="Non Blocking Processing Strategy" />
    <asynchronous-processing-strategy
        name="Asynchronous_Processing_Strategy"

maxThreads="500" minThreads="50"
        threadTTL="1000"

doc:name="Asynchronous Processing Strategy" />
    <flow name="MainProcessFlow">
        <file:inbound-endpoint path="C:/Users/sadik.ali/Desktop/files/input"
            moveToDirectory="C:/Users/sadik.ali/Desktop/files/output"
            responseTimeout="10000" doc:name="File">
            <file:filename-regex-filter pattern="dat.csv"
                caseSensitive="true" />
        </file:inbound-endpoint>
        <file:file-to-string-transformer
            returnClass="java.lang.String" mimeType="application/java"

doc:name="File to String" />
        <dw:transform-message doc:name="Transform Message">
            <dw:input-payload mimeType="application/csv">
                <dw:reader-property name="ignoreEmptyLine" value="true" />
                <dw:reader-property name="separator" value="|" />
            </dw:input-payload>
            <dw:set-payload><![CDATA[%dw 1.0
%output application/java
---
payload]]></dw:set-payload>
        </dw:transform-message>
        <batch:execute name="BatchProcessFlow"

doc:name="Batch Execute" />
    </flow>
    <batch:job name="BatchProcessFlow" block-size="500">
        <batch:process-records>
            <batch:step name="Batch_Step">
                <logger message="-------------------------------------#[payload]"
                    level="INFO" doc:name="Logger" />
            </batch:step>
        </batch:process-records>
    </batch:job>
</mule>

Having compared the application flow development in Mule3 and Mule4, we will observe no significant difference. Mule4 is only about delivering a smooth user experience. With no multiple threads, easy to learn language, instant access to data, and enhanced connectivity, Mule4 is about boosting developers’ capabilities.

• Application Log
  Following are the Mule 3 vs Mule 4 application logs. When you compare the applications logs for both versions of the runtime, the differences can easily be identified.

  Mule 4

  In the Mule 4 application log below, you will see that the runtime is assigning the thread profile during the data processing—[[MuleRuntime].io and [[MuleRuntime].cpuIntensive. These thread pools are assigned to flow process-based thread requirements.

  IO thread pool:

  

  Intensive thread pool:

  

  Mule 3

  In the Mule 3 application log given below, you will not find any such self-tuning capabilities for data processing. Profiling of the process is required to scale up the performance in Mule 3, which is self-assigned in Mule 4.

  

  

Mule 4 – Boosting Innovation, Scalability, and Performance

Mule 4 augments the speed of developers and cut short the integration time. Facilitating a single-thread pool of the entire application development flow, self-tuning of the runtime engine, enhanced message models, and effective streaming, Mule 4 boosts developer’s confidence and helps enterprises optimize performance.

This updated version of the Mule runtime engine boosts its Anypoint Platform, which facilitates seamless connectivity. With Mule 4 self-tuning capabilities, the Anypoint Platform allows developers to build robust applications with comparatively faster speed and enables them to steer to new levels of innovation successfully. Hire a reliable MuleSoft integration services provider who is a trusted MuleSoft partner along with a value-added reseller and system integrator.