MULESOFT on DecipherMiddleware

Logging within DataWeave Script

Wed, 08 Oct 2025 00:00:00 +0000

With the release of Mule runtime 4.10 edge version, a new version of DataWeave, i.e. 2.10, is also released. One of the new features introduced in this release is DataWeave Logging Configuration. The feature facilitates the developer to have granular control over logging. This leads to faster development and debugging.

There might be a question: loggers are there; Why can’t we just put a log connector and log the variable or payload? What benefits do we get by logging within DataWeave? Where can I use this feature?

🔭 Let’s explore DataWeave examples of varying complexities.

👴🏼 Old way of debugging 🔗

Let’s consider simple Mule flow which outputs a hello world!, using a transform message.

--- config: theme: 'neutral' --- flowchart LR A["HTTP(s)"] --> B["Transform"]

DataWeave 🔗

Difficulty level | ⭐️⚝⚝⚝⚝

Print “hello world!”

%dw 2.0
output text/plain
---
"hello world!"

Output 🔗

"hello world!"

To analyze the value of payload in logs; the best way would be to add a logger after the transform message.

--- config: theme: 'neutral' --- flowchart LR A["HTTP(s)"] --> B["Transform"] --> C["Logger"]

Console Output 🔗

INFO 2025-10-07 23:50:57,309 [[MuleRuntime].uber.01: [dataweave-logs].dataweave-logsFlow.CPU_INTENSIVE @6198f22a] [processor: dataweave-logsFlow/processors/1; event: 5e704830-a3aa-11f0-bf2a-beeb1f9526e2] org.mule.runtime.core.internal.processor.LoggerMessageProcessor: hello world!

Pretty simple and easy 🫠… ❌ Not anymore, let’s increase the difficulty of the DataWeave script.

DataWeave 🔗

Difficulty level | ⭐️⭐️⚝⚝⚝

Factorial of a number

%dw 2.0
output application/json
---
(1 to payload.inp) reduce $ * $$

Input 🔗

{
 "inp": 5
}

Output 🔗

Considering the same flow, logger output will be as follows:

Console Output 🔗

INFO 2025-10-08 10:38:31,654 [[MuleRuntime].uber.07: [dataweave-logs].dataweave-logsFlow.CPU_INTENSIVE @1d9d48be] [processor: dataweave-logsFlow/processors/1; event: d569a3a0-a404-11f0-bf2a-beeb1f9526e2] org.mule.runtime.core.internal.processor.LoggerMessageProcessor: 120

Yuhhuuu!!! We did it, we got the factorial of the number printed in the logs, and the same has been sent to the calling API.

Now, if we need to know the value on each iteration of the reduce function, how are the values updated at every iteration? 🤔🤔🤔

If it were any other programming language, such as Java, Python, Go, etc. The print command would suffice for the needs. But in DataWeave, how do we do so?

We are still in the old way of debugging 😞. Let’s modify the code to get the desired outcome.

%dw 2.0
output application/json
---
(1 to payload.inp) reduce(it,acc=[1]) -> acc + acc[it-1]*it

Output 🔗

[
 1,
 1,
 2,
 6,
 24,
 120
]

Console Output 🔗

INFO 2025-10-08 11:12:32,863 [[MuleRuntime].uber.06: [dataweave-logs].dataweave-logsFlow.CPU_INTENSIVE @1bbba791] [processor: dataweave-logsFlow/processors/1; event: 961820f0-a409-11f0-bf2a-beeb1f9526e2] org.mule.runtime.core.internal.processor.LoggerMessageProcessor: [
 1,
 1,
 2,
 6,
 24,
 120
]

payload[-1] will be desired output sent to the client.

By doing this, we have significantly altered the dwl script and made it heavier, catering to the use case.

Simple task and complicated solution 😢😢😢

😎 New way of debugging 🔗

Starting with DataWeave 2.10, logging can be configured to control the verbosity of log messages using standard log levels: Debug, Info, Warn, and Error.

Let’s start with an example and remove the logger connector to make the flow simpler.

--- config: theme: 'neutral' --- flowchart LR A["HTTP(s)"] --> B["Transform"]

DataWeave 🔗

Difficulty level | ⭐️⚝⚝⚝⚝

Print “hello world!”

%dw 2.0
output text/plain
---
logInfo("hello world!")

Console Output 🔗

INFO 2025-10-08 19:27:55,625 [[MuleRuntime].uber.06: [dataweave-logs].dataweave-logsFlow.CPU_INTENSIVE @ae03158] [processor: ; event: ca3bdad0-a44e-11f0-bf2a-beeb1f9526e2] org.mule.weave.v2.model.service.DefaultLoggingService$: "hello world!"

Yeah!!! We did it…

Let’s look a little closer 🔎

Difference in packages used for logging 🔗

Logger Connector: org.mule.runtime.core.internal.processor.LoggerMessageProcessor

DataWeave: org.mule.weave.v2.model.service.DefaultLoggingService$

In above example, info level is used, for different log levels, different DataWeave logging functions are available.

DataWeave Logging Functions 🔗

logWith<T>(level: LogLevel, prefix: String, value: T): T
logDebug<T>(prefix: String = "", value: T): T
logInfo<T>(prefix: String = "", value: T): T
logWarn<T>(prefix: String = "", value: T): T
logError<T>(prefix: String = "", value: T): T
log<T>(prefix: String = "", value: T): T

More info on each function at official docs

Now let’s increase DataWeave complexity…

DataWeave 🔗

Difficulty level | ⭐️⭐️⚝⚝⚝

Factorial of a number

%dw 2.0
output application/json
---
(1 to payload.inp) reduce logInfo($ * $$)

Input 🔗

{
 "inp": 5
}

Output 🔗

Console Output 🔗

INFO 2025-10-08 20:16:32,412 [[MuleRuntime].uber.06: [dataweave-logs].dataweave-logsFlow.CPU_INTENSIVE @46ac5059] [processor: ; event: 94c8ac00-a455-11f0-bf2a-beeb1f9526e2] org.mule.weave.v2.model.service.DefaultLoggingService$: 2
INFO 2025-10-08 20:16:32,413 [[MuleRuntime].uber.06: [dataweave-logs].dataweave-logsFlow.CPU_INTENSIVE @46ac5059] [processor: ; event: 94c8ac00-a455-11f0-bf2a-beeb1f9526e2] org.mule.weave.v2.model.service.DefaultLoggingService$: 6
INFO 2025-10-08 20:16:32,413 [[MuleRuntime].uber.06: [dataweave-logs].dataweave-logsFlow.CPU_INTENSIVE @46ac5059] [processor: ; event: 94c8ac00-a455-11f0-bf2a-beeb1f9526e2] org.mule.weave.v2.model.service.DefaultLoggingService$: 24
INFO 2025-10-08 20:16:32,413 [[MuleRuntime].uber.06: [dataweave-logs].dataweave-logsFlow.CPU_INTENSIVE @46ac5059] [processor: ; event: 94c8ac00-a455-11f0-bf2a-beeb1f9526e2] org.mule.weave.v2.model.service.DefaultLoggingService$: 120

With the above log, we can infer that 4 times computation was performed, and the values were printed by the DataWeave logger. It helps in debugging script a lot.

The story doesn’t end here… Let’s make the script a little more complex… But Scope for this blog ends here… 😔😔😔

Part 2 🔗

Part 2 coming soon….

Please share your valuable feedback 😊😊😊…

Racer API | AnyPoint Speedway

Sun, 24 Mar 2024 18:27:00 +0000

Anypoint Speedway (The MuleSoft API Race Where Acceleration Meets Integration) is launched with Season 3. So, Muleys all around the world get ready to RACE. Once we scroll through the instructions, we see a need for Racer API, which will be used to compete in the race. Currently, Racer API can be built in any of the platforms, all it needs is a public endpoint with implementation as per specification.

But, we are the MuleSoft developers, so let’s build the API in MuleSoft. If you are a beginner or professional Muley, this article will help you implement the Racer API and participate in the race. Let’s fasten up the seatbelt and start developing the Racer API.

Anypoint Speedway has made our lives easier by providing the readily available specification file.

Pre-requisite 🔗

racerId 🔗

To enter the race, the RACER is required to register for the race and get a racerId.

1. Go to Anypoint Race API, and Click on Request Access. Log in with AnyPoint Platform credentials.

2. Fill in the details as shown below. If an application is not seen, create a new application by providing the name. Click on Request access. For initial access use the Basic SLA tier.

3. This will generate/show the client-id and client-secret which will be used to authenticate the requests to Anypoint Race API.

4. Go to POST /register resource. Follow the instructions to generate a racerId. You may use the portal to send a request or any rest client(e.g. Postman) to send a request and get a racerId for your racer.

{
 "displayName": "Sylvester the Cat",
 "email": "sylvester@wb.com",
 "racerId": "racer-id-value",
 "clientId": "clientId"
}

Highlighted racerId will be used in building the RACER API.

RACER API 🔗

1. Download the RAML specification file from the Anypoint Speedway portal. Click on Download and then as RAML. RAML file will be downloaded to the system.

2. Open AnyPoint Studio. Click on file, then New and then Click on Mule Project.

3. Enter the project/racer name, specify the downloaded file’s location, and click on finish.

A project will be created with scaffold flows as per the specification file.

Let’s build the flows forTier I. For Tier I , the token is required to be used in further requests. For this, an object store can be used.

4. Add the ObjectStore module in Mule Palette. Click on Global Elements, then click on Create.

5. Select ObjectStore Config under Connector configurations. Click on OK.

6. Check the Connection checkbox and click on OK. A connection configuration will be created.

7. Click on Create again to add another global configuration for ObjectStore. Select ObjectStore under Component Configuration and click OK.

8. Specify the connection reference for ObjectStore created in the previous step. Click OK.

9. Click on Message flow to go back to flows. Go to “post:\races:application\json:racer-api-config " flow configuration.

10. Drag and drop the Store connector from the Mule Palette. Specify the details as mentioned in the below screenshot.

11. Drag a set variable and set the name as raceId and value as uuid(). Configuration should look like as below.

12. Drag a transform message and set the values as shown below. Replace the “race-id-value " with the value, received from the Anypoint Race API POST /register.

13. The overall flow will look like below.

To passTier I , at least 1 lap needs to be completed. Let’s update the “post:\races\(id)\laps:text\plain:racer-api-config”

14. Drag the Retrieve connector under ObjectStore from Mule Palette, and place it after the transform message. Specify the values as shown below. Token value will be retrieved from the Object store.

15. Drag the transform connector. And update the transform connector with values as below.

The flow will look as below.

16. Save and export the API. Right-click racer project -> Export. Under Mule, select Anypoint Studio Project to Mule deployable archive. Click Next.

17. Specify the export jar location and click on Finish.

_Now the **RACER API **is ready to race for Tier I. Let’s deploy to the AnyPoint platform and start the race. _

18. Go to the AnyPoint platform, then to Runtime Manager, select the environment where you want to deploy. Click on Deploy application.

19. Specify the application name, and upload the jar file created in the previous step. Tick the checkbox to use ObjectStore V2 and click on Deploy.

After deployment, a public URL similar to “https://deciphermiddleware-racer-syivqn.5sc6y6-4.usa-e2.cloudhub.io” will be generated. This will be used to enter the race and compete with other racers.

Enter the Race 🔗

1. To make an entry in the race, trigger a request to Anypoint Race API POST /racers/{racerId}/entry.

2. Provide the public endpoint along with /api “API kit is used /api is added before every resource “. Any REST client can be used to trigger the request by providing details as mentioned in the API documentation.

The RACER has started RACING. Go to the Leaderboard and see your racer name coming up.

Oh wait there are a total of 5 Tiers! So, wear your developer hats and enhance the Rookie Racer to level up to Tier V.

All the best Racers, Let’s Race!!! 🔗

Configure Custom Domain in Private Space : CloudHub 2.0

Fri, 08 Sep 2023 14:58:00 +0000

With CloudHub 2.0, applications are deployed in public spaces or private spaces. Private space isolates the applications deployed, and different network customizations can be done within the private space. Earlier in CloudHub 1.0, DLB used to be configured to balance the incoming load along with configuring the custom/vanity domain instead of using *.cloudhub.io

We can configure vanity/custom domains in the private space, avoiding accessing the applications through the default *.cloudhub.io domain. Configuring the custom domain will require the public certificate and the private key as a prerequisite.

Generating the self-signed public certificate and the private key 🔗

For simplicity, we will try to create certificates for the wildcard domain. To generate the self-signed public certificate and private key, we will require OpenSSL.

1. Create a config file as below, name it config.cfg , and change the values as per the domain details.

[ req ]
default_bits = 2048
distinguished_name = req_distinguished_name
prompt = no
[ req_distinguished_name ]
countryName = IN
stateOrProvinceName = Punjab
localityName = Amritsar
organizationName = DecipherMiddleware
commonName = *.domain.com

2. Run the below command through the terminal. Currently, expiration is set to 3000 days.

openssl req -newkey rsa:2048 -nodes -keyout deciphermiddleware-in-private.pem -x509 -days 3000 -out deciphermiddleware-in-crt.pem -config config.cfg

This will generate 2 files: a public certificate and a private key. These 2 files will be used in the later stage.

Steps: Anypoint Platform 🔗

Let’s now log in to the Anypoint Platform.

1. Go to Runtime Manager and click private spaces.

2. Select the private space, where the custom/vanity domain needs to be configured. Click on Domains & TLS.

3. Click on Create TLS Context.

4. Enter the name for configuration. Upload the public certificate and the private key created in the previous steps. Certificates have been configured to have domains as *.mule.deciphermiddleware.in

5. Enter the keystore password, if the keystore was encrypted. If you have CA signed certificate upload that as well.

6. Click on Create TLS Context.

7. A new TLS context will be created as below.

Let’s configure an application to use the newly configured custom domain.

8. Open any CloudHub 2.0 application deployed in the Private Space. Go to settings. Click on Ingress , and click on Add Endpoint.

9. Change the host to the custom host configured. For now, leave the remaining fields as is. Click on Apply Changes. This will deploy the application with a new configuration as defined by the deployment model.

10. Before using the custom domain, make sure the DNS record with the following values is being configured.

Record Type	CNAME
Host (or Name)	wildcard or wildcard with subdomain (*.mule used in the current example)
Value (or Content)	Private Space default hostname

Validate if the DNS record is working 🔗

The below command can be used to check if the DNS records are configured properly.

nslookup

If private space static IPs and default hostname are visible as output, then configuration is proper and DNS routing is working as expected.

Apps can be accessed using the custom/vanity domains.

References 🔗

https://docs.mulesoft.com/cloudhub-2/ps-config-domains#before-you-begin

Please share your valuable feedback 😊😊😊

Experimenting Recursion in DataWeave - Fibonacci Series

Tue, 13 Jun 2023 17:20:00 +0000

In various programming languages, recursion is a process in which a method or function calls itself to simplify a complicated problem. Understanding recursive solution sometimes become difficult. Let’s try to solve a mathematical problem through recursion with a twist by using DataWeave. The Fibonacci series is a generic use case that recursion can solve. This is a very generic question in programming interviews.

Photo by Natalya Letunova on Unsplash

What is Fibonacci Series? 🔗

A Fibonacci series is a set of integers starting with 0 followed by 1, and a series continuing with the sum of the preceding two integers in the sequence. For example,

$$ F_n = 0,1,1,2,3,5,8,13,21,34… $$

Mathematically, 🔗

$$ \begin{align*} & F_0 = 0 \\ & F_1 = 1 \\ & F_n = F_{(n-1)} + F_{(n-2)} \end{align*} $$

Now transforming the above logic in the form of a recursive function to find the nth digit in the Fibonacci series.

DataWeave Recursive Function: 🔗

%dw 2.0
output application/json
fun fib_r(n)=
if (n<=0) 0
else if (n==1) 1
else
fib_r(n-1) + fib_r(n-2)
---
{
recursive:fib_r(9)
}

Output

{
 "recursive": 34
}

Testing on dataweave playground and increasing the value of n. The moment we increase the value beyond 27 , we get an ERROR.

ERROR? 🔗

The error shows the execution time taken by dataweave is more than 2.5 seconds. But how do we know how much time is taken by the recursive function? Is there a way to capture the execution time within DataWeave without any custom code/java modules? It’s a yes, The DataWeave has a utility module Timer, which can be used to capture execution time as well as the result of the functions.

%dw 2.0
import * from dw::util::Timer
output application/json
fun fib_r(n)=
if (n<=0) 0
else if (n==1) 1
else
fib_r(n-1) \+ fib_r(n-2)
---
{
recursive:time(()->fib_r(27)),
recursive_d:duration(()->fib_r(27))
}

Output

{
 "recursive": {
 "start": "2023-06-13T16:33:29.053017Z",
 "result": 610,
 "end": "2023-06-13T16:33:29.059367Z"
 },
 "recursive_d": {
 "time": 6,
 "result": 610
 }
}

Duration gives us the time taken by function execution in milliseconds and the time function output the start and end timestamp of the execution. With this, the execution time can be measured easily.

Now, the real issue is if we need to find the nth digit(greater than 27) in the Fibonacci series by optimizing space and time. How can that be done?

Going back to programming days, there is a way of using Tail Recursion. Now what exactly is tail recursion?

Tail recursion is using the recursive function as the last statement of the function. The benefit of this approach is it optimises the space as the function doesn’t need to keep the track of last 2 execution results. Since the result is populated as part of the last execution statement of the function. Putting the coding hats again for the DataWeave.

With the new function let’s find out the 500 th digit in the Fibonacci series.

%dw 2.0
import * from dw::util::Timer
output application/json
fun fib_r(n)=
if (n<=0) 0
else if (n==1) 1
else
fib_r(n-1) \+ fib_r(n-2)
//tail recursion
fun fib_tr(n, x=0, y=1)=
if(n<=0)
x
else if (n==1)
y
else
fib_tr(n-1,y,x+y)
---
{
recursive:time(()->fib_r(5)),
tailRecursive:time(()->fib_tr(500))
}

Output

{
 "recursive": {
 "start": "2023-06-13T16:48:46.007784Z",
 "result": 5,
 "end": "2023-06-13T16:48:46.007897Z"
 },
 "tailRecursive": {
 "start": "2023-06-13T16:48:46.007902Z",
 "result": 139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125,
 "end": "2023-06-13T16:48:46.010339Z"
 }
}

In nearly 4 milliseconds, the new tail recursive function can give results for the **500 th digit **in the series.

Can we improve the timings further? 🔗

In dataweave, we have a “reduce” function, which can be used to get output for the Fibonacci series even faster. Re-writing the logic with the “reduce” function is kind of challenging. After multiple trials and errors, one way is as below.

%dw 2.0
import * from dw::util::Timer
output application/json
fun fib_r(n)=
if (n<=0) 0
else if (n==1) 1
else
fib_r(n-1) \+ fib_r(n-2)
//tail recursion
fun fib_tr(n, x=0, y=1)=
if(n<=0)
x
else if (n==1)
y
else
fib_tr(n-1,y,x+y)
//with reduce
fun fib_rr(n)=
if(n<=0)
0
else
if(n==1)
1
else
([2 to n][0] reduce ((i,acc=[0, 1]) -> acc + (acc[i-1] + acc[i-2])))[n]
---
{
recursive:time(()->fib_r(5)),
tailRecursive:time(()->fib_tr(500)),
reduce:time(()->fib_rr(500))
}

Output

{
 "recursive": {
 "start": "2023-06-13T17:04:41.724469Z",
 "result": 5,
 "end": "2023-06-13T17:04:41.72455Z"
 },
 "tailRecursive": {
 "start": "2023-06-13T17:04:41.724555Z",
 "result": 139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125,
 "end": "2023-06-13T17:04:41.726913Z"
 },
 "reduce": {
 "start": "2023-06-13T17:04:41.726923Z",
 "result": 139423224561697880139724382870407283950070256587697307264108962948325571622863290691557658876222521294125,
 "end": "2023-06-13T17:04:41.729307Z"
 }
}

The above DataWeave script is one of the many ways to solve the problem. On comparing all three approaches, the solution using reduce function performs best of all.

Please share your valuable feedback 😊😊😊

Connect Free Oracle Autonomous Database from MuleSoft

Tue, 27 Dec 2022 12:29:00 +0000

A database is the most commonly used backend in the integration world. We often build a lot of integrations with databases (be it as a source or target system). Likewise, working on a proof-of-concept, we always find it hard to have a database that we can use on a local machine or on the cloud. If we install a database on a local device it will consume a lot of resources. Not only that connecting from a cloud integration platform i.e. MuleSoft cloudhub is always tricky.

Oracle has an always free-to-use Autonomous Database instance available on the cloud with limited features but is really a powerful database to use. We would say, it is one of the best suitable for doing proof of concepts and validation before working on an actual use case.

We thought of giving it a try to connect MuleSoft to the Free version of the Autonomous Oracle database. We created a free trial account for Oracle and an always-free autonomous database instance. Once we have the instance created, we can connect using SQL developer easily. But now the question is

How to connect this database using MuleSoft? 🔗

Let’s start with prerequisites.

Prerequisites 🔗

1. Log in to the oracle cloud instance and navigate to the autonomous database instance.

2. Click on the database connection. Then click on Download wallet.

3. Enter the wallet password (and make a note of it, which will be used in later steps)and click on Download. The wallet.zip file will be downloaded.

4. Extract the downloaded zip file. We will see below files extracted.

To connect to Oracle Autonomous Database, we can use either

mTLS, enabled by default.
TLS

The below steps will be familiar to both configurations

1. To fetch the connection URLs, we can either get them from the database instance page. Click on Database connection and scroll down to connection strings.

2. Select either TLS or mTLS under TLS authentication.

3. There will be 5 TNS(database service names) names available: high, low, medium, tp, and tpurgent. Click on the show, the TNS we may want to use, and copy the string. The string will contain all the details like host, port, service name, etc.

4. We can also get all these details from the file tnsnames.ora extracted from the wallet.

Connect using mTLS configurations 🔗

Whenever we create a new autonomous DB instance, only mTLS is enabled by default. To connect to the database, we need to configure 2 way SSL along with database credentials in MuleSoft.

Copy the keystore.jks and truststore.jks under the “src/main/resources " directory of the mule project. We can also keep it under a separate directory. In this example, we will be using the “wallet” folder to store these files.

Configure the database connection as below:

By providing these details, we will not be able to connect to DB as we have not configured mTLS. Click on the TLS tab and configure the details below.

Note : Keystore password, Keystore key password, and trust store password will be equal to the wallet password which we gave while generating the wallet on the OCI console.

Click on Test Connection, and we will see the connection is successful.

Connect using TLS configurations 🔗

To enable connectivity through TLS, we need to update the Access Control List(ACL) in the database instance under networking.

Please refer below link to configure TLS in the Oracle Autonomous database instance.

https://docs.oracle.com/en/cloud/paas/autonomous-database/adbsa/support-tls-mtls-authentication.html#GUID-4C2093B7-4DF0-4CF4-BB2D-33E1FC5D5184

Once TLS is enabled at Oracle Cloud Autonomous database, we can configure TLS. The only difference in the configuration will be to use only the trust store instead of configuring both the key store and the trust store.

Thank you for reading and please share any thoughts or comments.

Break it! Fix it!

Fri, 21 Oct 2022 16:10:00 +0000

One of the items on my bucket list was to do a live stream. This item always went to procrastinate bucket. There were multiple reasons for it. First, the most apparent one is “FEAR”, fear of doing something new that I never tried before. Then comes the second hurdle, okay now I have decided I will do it but what topic shall I pick. Now whatsoever topic you choose, you will see someone in some part of the world has already delivered that topic. Okay, now for instance topic is also decided, and now how to engage the audience when you cannot see them. And after getting answers to all the questions, what if you are stuck and things are not moving in the middle of the stream? The damn tense situation you are in. So, in this blog, I will discuss my first-time experience on a live stream. First-time experiences always hold importance in your life and are it good or bad are always remembered.

I did my first live stream on the MuleSoft community twitch channel and Twitter🥳🥳🥳🎉🎉🎉.

The Right Time 🔗

I am a MuleSoft mentor(a program run by the MuleSoft community, you can learn more from the link). One day, while scrolling through the messages on the MuleSoft official slack channel, I found an invitation from Alex Martinez for the live stream. I saw one line in the description: “All you need to do is come live and try whatever you want”. Okay! now this is the right time and I signed up for this. The first hurdle is crossed. Now comes the biggest of all “TOPIC”😵.

Topic 🔗

I got a suggestion from Alex to watch previous live streams. While watching I saw we can try any tutorial which is there over the Internet and follow the steps listed there and hope for everything runs as expected 😜. Now my hunt for topic selection started. I juggled around various topics, some couldn’t be done in the specified time, some were too short, and a few of them require some extra license or some involved cost, huh!. What to do…?

Finally, I found one tutorial to customize EDI X12 Schema in MuleSoft. This is a great use case. Sometimes partners have a requirement for having additional fields in the EDI message structure apart from the standard document. This tutorial walks us through customizing the EDI X12 855 v4010 (Purchase order Acknowledgement) document. But let’s have some twists and alter the 850 EDI X12 v4010 (Purchase Order).

D-Day 🔗

Finally, the day and time arrived. Oh! but there is a catch, the time I selected was 1:30 A.M. 12th October 22), midnight, a quiet time free from any distortions. I am ready with all my configurations and a bit nervous since it is for the first time.

Technical Information 🔗

EDI Electronic Data Interchange is used as a standard to transmit data among various systems. This format needs to be translated and fed to the end system for actual processing. MuleSoft x12 and EDIFACT connectors come real handy in all these.

X12 Connector : https://docs.mulesoft.com/x12-edi-connector/2.9/

EDIFACT Connector : https://docs.mulesoft.com/edifact-edi-connector/2.7/

Tutorial: https://mulesoft-labs.dev/codelabs/customize-an-edi-x12-schema-language-file/index.html

Sample EDI X12 850 v4010 : https://edidev.net/edidev-ca/help/Sample_Files/850_X12-4010.txt

Custom ESL files: https://github.com/cipherwizard9/DecipherMiddleware/tree/main/MuleSoft/custom-esl-schema

Experience 🔗

I was live on twitch and Twitter with our great host Alex. At first, it was awesome. Things were going smoothly. Whatever is written in the tutorial I was able to perform live, sharing the screen with Anypoint Studio.

The moment I started testing things, now the error started popping up. This was actually going in the back of my mind when everything was going smoothly. As a developer for me nothing runs properly in the first go, it has to fail first 😜. Now, stuck with one error, it took a while for me to understand the issue was just missing the keyword..yuk. While things went sideways, Alex was super supportive and kept on motivating me to fix it. I had only one thing in mind at that moment I have to fix it no matter what. The developer’s favorite tool to debug is the logs, for me they are always the savior. The moment one error was fixed, it was a breakthrough from the deadlock.

One error is fixed another pops up 🤣. Then it’s like a never-ending loop

I kept on trying and fixing errors one by one. And finally, the moment of satisfaction came when the code finally ran and what was required from the complete session was achieved.

Overall a great experience, to follow the tutorial, break things, identify the issue fix the issue, and then moment for celebration and satisfaction.

BREAK IT! FIX IT! 🔗

I hope to have sessions more in the future…

Please do let me know your views 😇😇😇

[Mule 4]Catch numeric exceptions inside DataWeave

Sat, 16 Jul 2022 04:32:00 +0000

Numbers are a simple data type, but become complex in real-time integration transformation scenarios. Number data type, we consider number system in base 10 and perform all kinds of arithmetic operations e.g. +, -, *, / etc. We do this starting in childhood: multiply two numbers, divide one number by another, or add two numbers. Now, where is the tricky part 🤔? Let’s start with, various tricky aspects and possible remedies to these.

This article is published on the MuleSoft Community LinkedIn page.

To continue reading please visit the below link.

https://www.linkedin.com/pulse/mule-4catch-numeric-exceptions-inside-dataweave-mulesoftcommunity

Please share your valuable feedback 😊😊😊

Number System Conversion in MuleSoft(Mule 4) using Dataweave

Wed, 23 Mar 2022 05:51:00 +0000

The number 11, we call it eleven in the decimal system. Still, it can be a representation of three in a binary system or it can be seventeen in a hexadecimal system. The actual value of a number depends on the base it is calculated. It is interesting changing the base changes the value of the representation. Let us assume in some integration flow we are getting values in binary number system but target system accepts values in a decimal system or hexadecimal system or vice versa.

Examples depicting values in various number systems:

#	DECIMAL	BINARY	HEXADECIMAL	OCTAL
	BASE 10	BASE 2	BASE 16	BASE 8
I	5	101	5	5
II	14	1110	E	16
III	54	110110	36	66
IV	123	1111011	7B	173
V	240	11110000	F0	360

In our engineering days, we have studied a lot about number systems. Below is the mathematical representation of conversion logic.

Let us assume:

d, represents the nth digit.
b, represents the base.
n, represents the number of digits

Value(V) of number with base b in the decimal system will be:

$$ V_{10} = (Σd*b(n-1))_{10} $$

Let us take example number 7B in hexadecimal(base 16) and use the above formula to convert to decimal.

$V_{10} = (7 * 16^{(2-1)} + 11 * 16^{(1-1)})_{10}$

$V_{10} = (112 + 11)_{10}$

$V_{10} = (123)_{10}$

The above conversion was through pen and paper but how to do this in Mule?

Dataweave has built-in functions under the dw::core::Numbers module, which can help us to perform these conversions without writing complex logic. To convert to decimal values we have below 3 functions. Let’s see usage one by one.

1. fromHex() 🔗

This function helps to convert hexadecimal value to decimal. This function takes a hexadecimal string as input and gives decimal value as output. Example:

%dw 2.0
import fromHex from dw::core::Numbers
output application/json
---
fromHex("7B")

Output:

2. fromBinary() 🔗

This function helps to convert the binary string value to decimal value. This function takes a binary string as input and gives decimal value as output. Example:

%dw 2.0
import fromBinary from dw::core::Numbers
output application/json
---
fromBinary("110110")

Output

3. fromRadixNumber() 🔗

This function helps to convert the numeric value in any base to decimal value. This function takes a number string and base as input and gives decimal value as output. Example:

%dw 2.0
import fromRadixNumber from dw::core::Numbers
output application/json
---
fromRadixNumber("360",8)

Here, 360 is the number in octal (base 8) and 8 represents the base.

Output

The above three ways were to convert numeric values from any base to decimal values. Dataweave also provides functions to do the reverse which is converting a decimal number to any base.

4. toHex() 🔗

This function helps to convert a decimal value to a hexadecimal value. This function takes a decimal number as input and gives a hexadecimal string value as output. Example:

%dw 2.0
import toHex from dw::core::Numbers
output application/json
---
toHex(54)

Output

"36"

5. toBinary() 🔗

This function helps to convert a decimal value to a binary string value. This function takes a decimal number as input and gives a binary string value as output. Example:

%dw 2.0
import toBinary from dw::core::Numbers
output application/json
---
toBinary(54)

Output

"110110"

6. toRadixNumber() 🔗

This function helps to convert a decimal value to a numeric string value as per the specified base. This function takes a decimal number and base as input and gives a string output of decimal value in the specified base. Example:

%dw 2.0
import toRadixNumber from dw::core::Numbers
output application/json
---
toRadixNumber(54,8)

Output

"66"

The above output is in base 8 or in octal.

Direct Conversion 🔗

What if we want to convert numeric value in base 4 directly to numeric value with base 3. In such a case, we can use a combination of fromRadixNumber and toRadixNumber.

$$ (54)_{10} = (312)_{4} = (2000)_{3} $$

Dataweave

%dw 2.0
import * from dw::core::Numbers
output application/json
---
toRadixNumber(fromRadixNumber(312,4),3)

Output

"2000"

References: 🔗

https://docs.mulesoft.com/dataweave/2.4/dw-numbers

Please share your valuable feedback 😊😊😊

Creating and Using Key-Value Lookups in MuleSoft (Mule 4)

Wed, 16 Feb 2022 17:59:00 +0000

Lookups or key-value pairs are really helpful while developing integrations. Suppose, we get a value from the source system but the target system requires a corresponding value for that key(incoming data). One way is to store such cross-reference in a database table and fetch values. What if, these values are not changed frequently, and accessing a database will probably pose an overhead. A better approach would be to have a static lookup file included within the Mule application. This lookup file can be a .csv, JSON, XML, Excel, etc. file. For simplicity, in this blog, we will be using CSV files as an example for creating and using lookups in Mule 4. A CSV file is easy to maintain and use. Let’s start with the below use case.

Let’s take a use case of Country codes. From the source system, the country field contains Country Name e.g. India but our target system requires 2 character Country Code e.g. IN as input in one of the fields. Now such fields will hardly change in a real-time scenario. We can create a csv file(country_codes.csv) as shown below.

CSV File- country_codes.csv 🔗

Country,CountryCode 
India,IN 
Indonesia,ID 
Philippines,PH 
United States,US 
Russia,RU 
Netherlands,NL 
Czech Republic,CZ

From the source system, we are getting an address payload as shown below.

Source Payload 🔗

{
 "address": {
 "streetAdd": "street1",
 "streetAdd2": "address2",
 "city": "City1",
 "state": "State1",
 "country": "Indonesia",
 "zip": "123456"
 }
}

Our target system expects only country code to be sent in the payload. Below are the steps to accomplish this

STEPS: 🔗

1. Place the file country_codes.csv under src/main/resources. In this example, we will place it inside a folder named lookup. If you are using a different folder update classpath accordingly.

2. Now, we can refer to the file inside the dataweave using the readUrl() dataweave function.

3. Let us assume input data is stored in payload, our dataweave transformation will look as below.

%dw 2.0
output application/json
var csvData = readUrl("classpath://lookup/country_codes.csv","application/csv")
fun lookupCC(country: String) : String =
(csvData filter ((item, index) -> item.Country == country))[0].CountryCode as String
---
add:{
addressLine: payload.address.streetAdd ++ " " ++ payload.address.streetAdd2,
city: payload.address.city,
state: payload.address.state,
countryCode: lookupCC(payload.address.country)
}

4. We have created a function called lookupCC() which will take one String input and match the incoming value of Country in the lookup csv file and give the country code as output. The output of the above code will look like this:

Output Payload 🔗

{
 "add": {
 "addressLine": "street1 address2",
 "city": "City1",
 "state": "State1",
 "countryCode": "ID"
 }
}

5. What if the country name is not available in the csv file, the output will come as null. In such cases, we can update the function and provide the default value. For example:

fun lookupCC(country: String) : String =
(csvData filter ((item, index) -> item.Country == country))[0].CountryCode as String default "--"

If we don’t find any matching value in the lookup file it will default country code as “–”.

Similarly, we can extend the function and the csv file to fetch values with multiple inputs and conditions.

References: 🔗

Please share your valuable feedback 😊😊😊

Playing with XML Namespaces in Mule 4 Dataweave

Sat, 22 Jan 2022 15:52:00 +0000

Today, the world is full of REST APIs which use JSON as a major message type. But we have a lot of applications and APIs in the middleware world which rely on XML message structures. XML messages are a bit more complex than JSON messages, as in XML messages namespaces play a vital role. A miss in the namespace can lead to errors that are sometimes unseen to normal eyes. In this blog, we will discuss various ways by which we can handle the transformation of XML messages with or without namespaces in Mule 4 using Dataweave.

Transforming XML messages without namespaces
Transforming XML messages with namespaces

1. Transforming XML messages without namespaces 🔗

This conversion is pretty simple, all we need to consider is XPath and do the mapping accordingly. This is similar to the JSON data transformation Let’s take an example of 2 XMLs, wherein both input and output XMLs don’t have namespaces. The output we need to specify is application/xml.

INPUT:

<InputRoot>
 <contact>
 <firstName>Test</firstName>
 <lastName>Data</lastName>
 <address>
 <street1>Street1</street1>
 <street2>Street2</street2>
 <city>City</city>
 <state>State</state>
 <country>Country</country>
 </address>
 </contact>
</InputRoot>

Suppose we want to extract the value of country from the above XML message. All we need to do is traverse to the node as we do in the case of JSON transformations i.e. (Assuming data is in the payload variable) payload.InputRoot.contact.address.country . Below is the dataweave file for the transformation transforming the data to a different XML format.

DATAWEAVE :

%dw 2.0
output application/xml
---
OutputRoot:{
Contact:{
Name: payload.InputRoot.contact.firstName ++ payload.InputRoot.contact.lastName,
Address :{
Street: payload.InputRoot.contact.address.street1 ++ payload.InputRoot.contact.address.street2 ,
City: payload.InputRoot.contact.address.city,
State:payload.InputRoot.contact.address.state
}
}
}

OUTPUT:

<OutputRoot>
 <Contact>
 <Name>TestData</Name>
 <Address>
 <Street>Street1Street2</Street>
 <City>City</City>
 <State>State</State>
 </Address>
 </Contact>
</OutputRoot>

Above is the output for the dataweave file.

2. Transforming XML messages with namespaces. 🔗

Namespaces are really important for various systems to understand the element and its data. As the same element name with a different namespace corresponds to 2 different meanings. For example element name in an XML message with a namespace corresponding to the account namespace, is different from the element name corresponding to the contact namespace. So the business meaning of an element differs and so does the representation. An element belonging to a particular namespace will have a target namespace denoted by xmlns attribute. In some cases, namespaces are declared as attributes of elements and are referenced as prefixes like:

<acc:name xmlns:acc="http://deciphermiddleware.in/ns/account"/>
<cont:name xmlns:cont="****http://deciphermiddleware.in/ns/contact"/>

Let’s take the below example:

INPUT:

<InputRoot xmlns="http://deciphermiddleware.in/ns/sample/data">
 <cont:contact xmlns:cont="http://deciphermiddleware.in/ns/sample/contact">
 <cont:firstName>Test</cont:firstName>
 <cont:lastName>Data</cont:lastName>
 <cont:address xmlns:add="http://deciphermiddleware.in/ns/sample/address">
 <add:street1>Street1</add:street1>
 <add:street2>Street2</add:street2>
 <add:city>City</add:city>
 <add:state>State</add:state>
 <add:country>Country</add:country>
 </cont:address>
 </cont:contact>
</InputRoot>

Now, in the above case, we have multiple namespaces. To deal with such scenarios we need to declare namespaces in the Dataweave file.

We can declare the namespace in the following way.

%dw 2.0
output application/xml
ns ns0 http://deciphermiddleware.in/ns/sample/data
ns cont http://deciphermiddleware.in/ns/sample/contact
ns add http://deciphermiddleware.in/ns/sample/address

In dataweave, an element with namespace will be denoted by ns# Element (ns denotes namespace prefix as declared, Element is the name of node or element of XML and, # acts as separator)

Suppose we need to extract data of the country, now dataweave expression for the same will be “payload.ns0#InputRoot.cont#contact.cont#address.add#country "

Combining the above parameters, below is a sample dataweave file that will output the XML message generated from the above sample input XML, having different namespaces.

DATAWEAVE:

%dw 2.0
output application/xml
ns ns0 http://deciphermiddleware.in/ns/sample/data
ns cont http://deciphermiddleware.in/ns/sample/contact
ns add http://deciphermiddleware.in/ns/sample/address
ns ns1 http://deciphermiddleware.in/ns/sample/data1
ns cont1 http://deciphermiddleware.in/ns/sample/contact1
ns add1 http://deciphermiddleware.in/ns/sample/address1
---
ns1#OutputRoot:{
cont1#Contact:{
cont1#Name: payload.ns0#InputRoot.cont#contact.cont#firstName ++ payload.ns0#InputRoot.cont#contact.cont#lastName,
add1#Address :{
add1#Street: payload.ns0#InputRoot.cont#contact.cont#address.add#street1 ++ payload.ns0#InputRoot.cont#contact.cont#address.add#street2 ,
add1#City: payload.ns0#InputRoot.cont#contact.cont#address.add#city,
add1#State:payload.ns0#InputRoot.cont#contact.cont#address.add#state,
add1#Country:payload.ns0#InputRoot.cont#contact.cont#address.add#country
}
}
}

Below is the output for the above dataweave.

OUTPUT:

<ns1:OutputRoot xmlns:ns1="http://deciphermiddleware.in/ns/sample/data1">
 <cont1:Contact xmlns:cont1="http://deciphermiddleware.in/ns/sample/contact1">
 <cont1:Name>TestData</cont1:Name>
 <add1:Address xmlns:add1="http://deciphermiddleware.in/ns/sample/address1">
 <add1:Street>Street1Street2</add1:Street>
 <add1:City>City</add1:City>
 <add1:State>State</add1:State>
 <add1:Country>Country</add1:Country>
 </add1:Address>
 </cont1:Contact>
</ns1:OutputRoot>

References: 🔗

https://docs.mulesoft.com/dataweave/2.3/dataweave-cookbook-include-xml-namespaces

Please share your valuable feedback 😊😊😊

Tips and tricks to handle dateTime formats in Mule 4

Sun, 12 Dec 2021 17:17:00 +0000

We generally come across various scenarios while developing integrations, wherein we have to deal with multiple formats of date, time, or DateTime. Sometimes source systems can send date data in only one format which may or may not be accepted by target systems, which we have to handle using dataweave expressions. In this blog, we will be going through a couple of generic scenarios which we come across frequently. For example, if we use now() in dataweave, we get an output as below.

DataWeave expression:

%dw 2.0
output application/json
---
now()

Output:

"2021-12-12T14:27:48.064709Z"

The above output is in the format of DateTime, and from this, it is easy to convert or extract and date or time values.

Scenario #1 🔗

What if we get date time in some different format, for e.g. “2021/12/12 14.27.48 " and we just need to extract date and time in a different format as “12-12-2021 14:27:48 “. The best way is to cast the input date string to LocalDateTime by providing format and then convert to the string by providing the desired format.

DataWeave expression:

%dw 2.0
output application/json
---
"2021/12/12 14.27.48" as LocalDateTime {format:"yyyy/MM/dd HH.mm.ss"} as String {format:"dd-MM-yyyy HH:mm:ss"}

Output:

"12-12-2021 14:27:48"

Scenario #2 🔗

Sometimes, we have to insert dateTime to databases like SQL Server, Oracle database, etc. The generally accepted format for the database date column through Mule4 is “yyyy-MM-dd HH:mm:ss "

DataWeave expression:

%dw 2.0
output application/json
---
now() as String {format:"yyyy-MM-dd HH:mm:ss"}

Output:

"2021-12-12 14:27:48"

Scenario #3 🔗

What if we want to extract year and append Y in the end to output for example “2021Y”. If we use the format as “yyyyY” or “yY”, the output will come as a year repeated 2 times. The reason is y or Y is formatting character. If we want explicit Y to be displayed, one way could be to extract Year and then concatenate year with Y. Another way is to escape the formatting character by enclosing it in ‘’. For example:

DataWeave expression:

%dw 2.0
output application/json
---
now() as String {format:"yyyy'Y'"}

Output:

"2021Y"

Tabular representation of various formatting characters that can be used.

DateTime: “2021-08-09T15:37:29.519633+05:30”

Character(s)	Description	Example
y	Year of the era (BCE or CE) or calendar year	2021
yy	Calendar Year limit to 2 digits	21
Y	Week based year	2021
YY	Week based year limit to 2 digits	21
u	Calendar Year without era (BCE or CE)	2021
uu	Calendar Year limit to 2 digits	21
M	The month with no leading 0	8
MM	The month with leading 0	08
MMM	Month name with 3 characters	Aug
MMMM	Complete Month name	August
d	Day of the Month	09
D	Day of the Year	221
E	Abbreviated day of the Week	Mon
EEEE	Day of the Week	Monday
h	An hour in 12 hour clock (1-12)	03
H	An hour in 24 hour clock (0-23)	03
m	Minute of the hour	37
s	Second of the Minute	29
S	Fraction of the second	5
a	AM/PM	PM
Z	Timezone Offset	+0530
ZZZZ	Timezone Offset	GMT+05:30

References: 🔗

https://docs.mulesoft.com/dataweave/2.3/dataweave-cookbook-format-dates

Please share your valuable feedback 😊😊😊

Reading .csv files with and without headers using different delimiters in Mule 4

Wed, 18 Aug 2021 18:29:00 +0000

Flat files especially .csv files are very common for data loading and data transfers. We generally come across scenarios where we want to read a .csv file, which may or may not contain the header row. Also, there is a chance .csv files don’t have ‘,’(comma) as delimiter rather has ‘’|"(pipe) as a delimiter or any other character as a delimiter. This article will discuss various ways to read such files in Mule 4 and create a JSON output of it.

Mule Flow 🔗

This will be a simple flow, where we will be reading a file using a file connector. Then convert the read data to JSON using transform message. Finally, log the JSON payload to the console using Logger. Here, we are not doing any complex transformation, it will be more like a MIME type conversion from application/csv to application/json, for better understanding the structure of input data.

Steps: 🔗

Create a flow in your mule application.
Drag and drop file connector with operation “On New or Updated file” on the source of mule flow.
In file connector properties under the general tab, specify or create a new connector configuration. Provide input directory, and polling frequency(here it is kept as 10 seconds). Specify the post-processing action like deleting the file, moving the file, or renaming the file. As per the below configurations, the mule application will look for a file in the inbound folder every 10 seconds. If the file is available, then it will process and move the file to the archive directory, deleting the file from the inbound location.

Drag and drop “Transform Message” from mule palette, and have output mime type as “application/json” as below.

Drag and drop logger from mule palette. Here we will be logging payload after transformation.
The final mule flow will look as below.

.csv file having header row in the file content 🔗

Let us take below employeeData.csv file as an example. For simplicity, we are taking dummy data of only 5 employees.

id,first name,last name,email,gender,designation
1,Dom,Lissett,dlissett0@test.com,Male,Recruiter
2,Susan,Moogan,smoogan1@test.com,Female,Executive Secretary
3,Carl,Cotesford,lcotesford2@test.com,Male,Tech Architect
4,Debor,Sprakes,dsprakes3@test.com,Female,Account Coordinator
5,Priscella,Genny,pgenny5@test.com,Female,Environmental Tech

In this example, data in the first row signifies the headers separated by “,” as a delimiter. Rest all other rows represent employee data.

Since the above .csv file contains a header row, all we need to do is to set the MIME type of file connector as “application/csv”. To do so, click on the file connector. Click on MIME Type and select “application/csv” from the drop-down list of values.

Place the file as defined in the file connector. Run the application. We will see the JSON response logged in the console as shown below.

INFO 2021-07-27 23:08:55,916 [[MuleRuntime].uber.03: [csv-read].csv-readFlow.CPU_INTENSIVE @1e42a374] [processor: csv-readFlow/processors/1; event: 895d1200-efb1-11eb-8134-d4d25289cb8d] org.mule.runtime.core.internal.processor.LoggerMessageProcessor: [
 {
 "id": "1",
 "first name": "Dom",
 "last name": "Lissett",
 "email": "dlissett0@test.com",
 "gender": "Male",
 "designation": "Recruiter"
 },
 {
 "id": "2",
 "first name": "Susan",
 "last name": "Moogan",
 "email": "smoogan1@test.com",
 "gender": "Female",
 "designation": "Executive Secretary"
 },
 {
 "id": "3",
 "first name": "Carl",
 "last name": "Cotesford",
 "email": "lcotesford2@test.com",
 "gender": "Male",
 "designation": "Tech Architect"
 },
 {
 "id": "4",
 "first name": "Debor",
 "last name": "Sprakes",
 "email": "dsprakes3@test.com",
 "gender": "Female",
 "designation": "Account Coordinator"
 },
 {
 "id": "5",
 "first name": "Priscella",
 "last name": "Genny",
 "email": "pgenny5@test.com",
 "gender": "Female",
 "designation": "Environmental Tech"
 }
]

Data in the first row became the keys for the JSON output.

.csv file without header row in the file content 🔗

Let us assume there is no header row in the content of employeeData.csv

1,Dom,Lissett,dlissett0@test.com,Male,Recruiter
2,Susan,Moogan,smoogan1@test.com,Female,Executive Secretary
3,Carl,Cotesford,lcotesford2@test.com,Male,Tech Architect
4,Debor,Sprakes,dsprakes3@test.com,Female,Account Coordinator
5,Priscella,Genny,pgenny5@test.com,Female,Environmental Tech

If we try to run with the same configurations, it will take the first row as keys and console output will look like.

INFO 2021-07-27 23:21:15,609 [[MuleRuntime].uber.03: [csv-read].csv-readFlow.CPU_INTENSIVE @1e42a374] [processor: csv-readFlow/processors/1; event: 4254cbd0-efb3-11eb-8134-d4d25289cb8d] org.mule.runtime.core.internal.processor.LoggerMessageProcessor: [
 {
 "1": "2",
 "Dom": "Susan",
 "Lissett": "Moogan",
 "dlissett0@test.com": "smoogan1@test.com",
 "Male": "Female",
 "Recruiter": "Executive Secretary"
 },
 {
 "1": "3",
 "Dom": "Carl",
 "Lissett": "Cotesford",
 "dlissett0@test.com": "lcotesford2@test.com",
 "Male": "Male",
 "Recruiter": "Tech Architect"
 },
 {
 "1": "4",
 "Dom": "Debor",
 "Lissett": "Sprakes",
 "dlissett0@test.com": "dsprakes3@test.com",
 "Male": "Female",
 "Recruiter": "Account Coordinator"
 },
 {
 "1": "5",
 "Dom": "Priscella",
 "Lissett": "Genny",
 "dlissett0@test.com": "pgenny5@test.com",
 "Male": "Female",
 "Recruiter": "Environmental Tech"
 }
]

By default, after setting MIME type to “application/json”, mule considers the first row as the header row. In such cases, we don’t want to consider the first row as the header row.

Click on the file connector. Click on MIME Type. Click on add parameter. Set key as “header” and value as “false”. Save the changes.

Now, by using the same file, we will be able to read data properly. But in this case, autogenerated values for keys like coloumn_0, column_1, etc. will be populated. Below is a sample log snippet.

INFO 2021-07-27 23:35:57,158 [[MuleRuntime].uber.05: [csv-read].csv-readFlow.CPU_INTENSIVE @6fbaa2fe] [processor: csv-readFlow/processors/1; event: 4fbf0590-efb5-11eb-8134-d4d25289cb8d] org.mule.runtime.core.internal.processor.LoggerMessageProcessor: [
 {
 "column_0": "1",
 "column_1": "Dom",
 "column_2": "Lissett",
 "column_3": "dlissett0@test.com",
 "column_4": "Male",
 "column_5": "Recruiter"
 },
 {
 "column_0": "2",
 "column_1": "Susan",
 "column_2": "Moogan",
 "column_3": "smoogan1@test.com",
 "column_4": "Female",
 "column_5": "Executive Secretary"
 },
 {
 "column_0": "3",
 "column_1": "Carl",
 "column_2": "Cotesford",
 "column_3": "lcotesford2@test.com",
 "column_4": "Male",
 "column_5": "Tech Architect"
 },
 {
 "column_0": "4",
 "column_1": "Debor",
 "column_2": "Sprakes",
 "column_3": "dsprakes3@test.com",
 "column_4": "Female",
 "column_5": "Account Coordinator"
 },
 {
 "column_0": "5",
 "column_1": "Priscella",
 "column_2": "Genny",
 "column_3": "pgenny5@test.com",
 "column_4": "Female",
 "column_5": "Environmental Tech"
 }
]

.csv file without header row and having a “|” as a delimiter 🔗

If we replace “,” delimiter with “|” delimiter in employeeData.csv

1|Dom|Lissett|dlissett0@test.com|Male|Recruiter
2|Susan|Moogan|smoogan1@test.com|Female|Executive Secretary
3|Carl|Cotesford|lcotesford2@test.com|Male|Tech Architect
4|Debor|Sprakes|dsprakes3@test.com|Female|Account Coordinator
5|Priscella|Genny|pgenny5@test.com|Female|Environmental Tech

On running the same mule application with the above data, the output of the above employeeData.csv file will be.

INFO 2021-07-27 23:42:06,938 [[MuleRuntime].uber.05: [csv-read].csv-readFlow.CPU_INTENSIVE @6fbaa2fe] [processor: csv-readFlow/processors/1; event: 2c2f0f20-efb6-11eb-8134-d4d25289cb8d] org.mule.runtime.core.internal.processor.LoggerMessageProcessor: [
 {
 "column_0": "1|Dom|Lissett|dlissett0@test.com|Male|Recruiter"
 },
 {
 "column_0": "2|Susan|Moogan|smoogan1@test.com|Female|Executive Secretary"
 },
 {
 "column_0": "3|Carl|Cotesford|lcotesford2@test.com|Male|Tech Architect"
 },
 {
 "column_0": "4|Debor|Sprakes|dsprakes3@test.com|Female|Account Coordinator"
 },
 {
 "column_0": "5|Priscella|Genny|pgenny5@test.com|Female|Environmental Tech"
 }
]

Now, we are getting only one key-value pair for each row. By default mule considers “,” as delimiter for “application/csv” MIME type data. We need to explicitly define “|” as a delimiter in parameters under the MIME type.

Click on the file connector. Click on MIME Type. Click on add parameter. Set Key as “separator” and Value as “|”. Save the changes.

Now, by using the same file, we will be able to read data properly as shown below.

INFO 2021-07-27 23:51:57,557 [[MuleRuntime].uber.06: [csv-read].csv-readFlow.CPU_INTENSIVE @6efd4175] [processor: csv-readFlow/processors/1; event: 8c31a710-efb7-11eb-8134-d4d25289cb8d] org.mule.runtime.core.internal.processor.LoggerMessageProcessor: [
 {
 "column_0": "1",
 "column_1": "Dom",
 "column_2": "Lissett",
 "column_3": "dlissett0@test.com",
 "column_4": "Male",
 "column_5": "Recruiter"
 },
 {
 "column_0": "2",
 "column_1": "Susan",
 "column_2": "Moogan",
 "column_3": "smoogan1@test.com",
 "column_4": "Female",
 "column_5": "Executive Secretary"
 },
 {
 "column_0": "3",
 "column_1": "Carl",
 "column_2": "Cotesford",
 "column_3": "lcotesford2@test.com",
 "column_4": "Male",
 "column_5": "Tech Architect"
 },
 {
 "column_0": "4",
 "column_1": "Debor",
 "column_2": "Sprakes",
 "column_3": "dsprakes3@test.com",
 "column_4": "Female",
 "column_5": "Account Coordinator"
 },
 {
 "column_0": "5",
 "column_1": "Priscella",
 "column_2": "Genny",
 "column_3": "pgenny5@test.com",
 "column_4": "Female",
 "column_5": "Environmental Tech"
 }
]

References: 🔗

https://docs.mulesoft.com/mule-runtime/4.3/dataweave-formats-csv

Please share your valuable feedback 😊

This is a repost of the LinkedIn article….