The document summarizes a MuleSoft meetup that took place in Warsaw, Poland on January 23rd, 2019. The meetup agenda included introductions, an introduction to DataWeave 2.0 focusing on map, filter and reduce functions, examples and use cases of these functions, and a Q&A session. The speaker was introduced and details were provided on organizing future meetups and providing feedback.

1. January 23rd, 2019
Warsaw Group

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Our Partners
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Share
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• Share the Meetup
• Use Hashtags
– #MuleMeetup
– #WarsawMuleMeetup

4. Agenda

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Agenda
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6:00 PM Introductions
6:15 PM Intro into DataWeave 2.0
6:35 PM Map/Filter/Reduce
7:15 PM Break
7:30 PM Map/Filter/Reduce/Q&A
7:45 PM DataWeave 2.0 cases
8:05 PM Q&A
8:35 PM Closing the Meetup

6. Introduction

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Speaker
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• Joshua Erney
• Mountain State Software Solutions
• @mulemadeeasy
• www.jerney.io / @jerney_io
• Brief work history...

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Organiser & Speaker
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• Senior Integration Developer at PwC Poland
• MuleSoft Meetup Leader for Warsaw, Poland
• Recognized by MuleSoft as one of the top
contributors in the MuleSoft Community
• Mule blogger https://profit-online.pl/blog
• Doing Mule for over 7 years now

9. November + December 2018
Community Success Months:
Get trained and become MuleSoft-Certified

10. Data Weave 2.0
Mule 4

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Introduction
DataWeave
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• Entirely replaces everything that MEL does
• Default language
• Syntax changes
• Modularization
• And many more …
MEL
MEL stands for Mule
Expression Language

12. What's new
By example

13. A Deep Dive
DataWeave 2.0:
map/filter/reduce

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Agenda
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• About me
• What are map/filter/reduce?
• Filter (overview, graphic, examples)
• Map (overview, graphic, examples)
• Reduce (overview, graphic, examples)
• Reusable functions with map/filter/reduce

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What are map, filter, and reduce?
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• Functions that transform arrays
• As input: an array, and a function
– Higher order functions (a function taking another function as an argument, OR a
function that returns another function)
– The input function for map/filter/reduce define the implementation details of the
transformation
• Output: array (map/filter), any (reduce)
• Declarative - implementation of iteration is hidden from the client
• Transformations are non-destructive (they do not modify any input)

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Why do a talk on them?
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• They form the core functionality that you’ll need for data
transformations (removing records, modifying records, general-
purpose transformation tool)
• A mastery of map/filter/reduce is essential to writing clear and
efficient transformations.
• A lot of other functions that work on transforming arrays are just
flavors of map/filter/reduce.
• Gives you a great base to build off of because you need to
understand two key concepts: immutability, and higher-order
functions

17. filter
Removing elements from arrays
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filter
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• Input: An array and a predicate function (function that returns true
or false)
– Predicate function example: (n) -> (n == 2)
• Output: a new array
• Creates new output array, applying the predicate function to every
element in the input array
• If the predicate returns true, add the value to the output, if the
predicate returns false, ignore it
• The only thing filter can do is create new arrays that contain the
same or less elements than the previous array. It cannot change the
elements.

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filter (cont.)
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filter (examples)
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// Filter by content
[1,2,3,4,5] filter (mod($, 2) == 0)
// -> [2, 4]
// Filter by index
[2,4,6,8,10] filter ($$ == 4)
// -> [10]
// Filter by nested content (popular use case)
var arr = [{“name”:”Josh”}, {“name”:”Patryk”}, {“name”:”Max The Mule”}]
---
arr filter $.name == “Patryk”
// -> [{”name”:”Patryk”}]

21. map
Transforming elements in an array
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map
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• Input: an array and a mapping function
• Output: a new array
• The mapping function describes how each element in the array will
be modified before it is added to the output array
• map can create a new array that is the result of a modification of
each element in the input array. map cannot modify the number of
elements returned in the resulting array

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map (cont.)
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map (examples)
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// Map using each element
[1,2,3] map ($ + 1)
// -> [2,3,4]
// Map using index
[1,2,3] map ({loc: $$, n: $})
// -> [{loc: 0, n: 1},{loc: 1, n: 2}, {loc: 2, n: 3}]
// Map nested values down to single-item array (great for list of IDs)
[{id: 123}, {id: 456}, {id: 789}] map $.id
// -> [123, 456, 789]

25. reduce
... pretty much anything else you’d want to do with an array

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reduce
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• Takes in an array and an accumulator function
• returns a new Any (object, string, Boolean, null, array, etc.) we will
call this the result
• The accumulator function defines how each element in the input
array will be folded into the result
– The value returned from the accumulator function will be passed to the next
iteration
• Reduce is the most general-purpose of the three core functions
• Can implement both map and filter in terms of reduce, so map and
filter are just specialized reduce functions
– Keep this in mind as you write your transformations. Don’t use reduce when a
map or filter would work instead

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reduce (cont.)
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reduce (examples)
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// Classic example: Adding numbers
[1,2,3] reduce ((n, acc) -> acc + n)
// -> 6
// A simple joinBy functionality
[“1”,”2”,”3”] reduce ((str, out) -> out ++ “, ” ++ str
// “1, 2, 3”
// The same thing wrapped in a helpful function (I wrap most non-trivial reduce
// code in a simple function so that the client does not need to pass a lambda)
fun myJoinBy(arr, delim) =
arr reduce ((str, out) -> out ++ delim ++ str

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reduce (examples cont.)
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// Flattening an array into a object (common use case)
var arr = [
{dev: "c6824476-b7e2-4e8a-9281-bdc40663bb93"},
{prod: "7f44b450-18ff-45fa-85f3-8ef5c82b4989"}
]
---
arr reduce ((env, obj) -> obj ++ env)
// -> { ”dev”: “c6824476-b7e2-4e8a-9281-bdc40663bb93”,
// “prod”: “7f44b450-18ff-45fa-85f3-8ef5c82b4989” }
// You could also do this ($$ defaults to the first value in the array):
arr reduce ($$ ++ $)

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reduce (examples cont.)
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// Make map/filter code more efficient. This loops twice:
[1,2,3,4,5] filter (mod($, 2) == 0) map ($ + 2)
// -> [4,6]
// This loops once and accomplishes the same job
[1,2,3,4,5] reduce ((n, acc=[]) ->
if (mod(n, 2) == 0) // Filter replacement
acc + (n + 2) // Map replacement
else
acc)

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reduce (examples cont.)
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// Implement map:
fun myMap(arr, transformFn) =
arr reduce ((e, acc=[]) ->
acc + transformFn(e)
// Implement filter:
fun myFilter(arr, predicateFn) =
arr reduce ((e, acc=[]) ->
if (predicateFn(e))
acc + e
else
acc

32. Reusable functions w/
map/filter/reduce

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Wrapping map/filter/reduce
• Passing functions to functions is nice because of the flexibility, but
it’s not always an ideal API because of that flexibility
– Reader needs to understand map/filter/reduce, and then understand the
workings of passed function
• To make functions less flexible, but far more readable, you can do
the following
1. Name the functionality that the passed-in function is providing
2. Wrap the map/filter/reduce code in a function
3. Name that function according to the name you came up w/ in step 1
4. Make sure parameters passed to your new function do not take in a lambda

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Example
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• Let’s take a reduce example from earlier. We start with this:
var arr = [{dev: ”123"}, {prod: ”456"}]
---
arr reduce ((env, obj={}) -> obj ++ env)
// -> {dev: “123”, prod: “456}

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Example (cont.)
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We will name the function based off of that functionality, and save the
client from having to pass in that functionality:
fun flattenToObj(arr) =
arr reduce (kvPair, obj={}) -> obj ++ kvPair)

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Moving Forward
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• You can do this with most functions that use map/filter/reduce
– e.g. fun removeOdds(arr) = arr filter ($ mod 2 == 0)
• This is especially helpful if the functions are going to be reused (put
them in a module!)
• Might not be as helpful if not reused, but you will get a nice
readability boost that comes from effective naming of your functions

37. Closing Thoughts

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Conclusion
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• map/filter/reduce are a trio of higher-order functions that form the
core of array transformations
– Filter is used to remove elements from an array
– Map is used to modify elements from an array
– Reduce is used to do all the of the above, as well as transform arrays to any
other type. Very general-purpose. Reach for this last.
• Map and filter tend to be pretty easy to learn, reduce typically
requires more practice to master
• Master these functions first, then learn mapObject, filterObject,
groupBy, pluck

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More Info
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• MuleSoft documentation:
– Map: https://docs.mulesoft.com/mule-runtime/4.1/dw-core-functions-map
– Filter: https://docs.mulesoft.com/mule-runtime/4.1/dw-core-functions-filter
– Reduce: https://docs.mulesoft.com/mule-runtime/4.1/dw-core-functions-reduce
• You can find more detailed information on my blog:
– Map post: www.jerney.io/dataweave-the-map-function/
– Filter post: On It’s Way 
– Reduce post 1: www.jerney.io/dataweave-when-to-use-reduce/
– Reduce post 2: www.jerney.io/dataweave-mapping-an-array-to-an-object/
– Practice exercises (map/filter/reduce and others):
www.jerney.io/dataweave-practice-exercises/

40. Questions?
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DataWeave question
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Which one is not a valid condition in Choice component
1. #[payload.type == 'Mule Meetup']
2. ${file:complexCondition.dwl}
3. payload.type == 'Mule Meetup'
4. All options are valid :)

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DataWeave question
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Which one is not a valid condition in Choice component
1. #[payload.type == 'Mule Meetup']
2. ${file:complexCondition.dwl}
3. payload.type == 'Mule Meetup'
4. All options are valid :)
Correct syntax is:
${file::complexCondition.dwl}

43. DataWeave 2.0 DEMO
Sample cases

44. What’s next

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What’s next
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• Feedback:
– Contact your organizer Patryk Bandurski to suggest topics
– Contact MuleSoft at meetup@mulesoft.com for ways to improve the program
• Our next meetup:
– Date: March/April 2019
– Location: PwC Poland
– Topic: …

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What’s next
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Invite your network to join
– https://meetups.mulesoft.com/warsaw/

47. See you next time
Please send topic suggestions to the organizer