Working with collections is one of the most common tasks in any programming language — whether you're cleaning up data, processing API responses, or building automation scripts. Groovy makes this easier and more expressive with its powerful collection methods, concise syntax, and closure-based operations.

Unlike Java, where filtering and transforming collections often requires verbose loops or streams, Groovy provides a rich toolbox of functions such as find, findAll, collect, groupBy, and more. These methods turn collection processing into readable, declarative code that’s easy to maintain.

In this tutorial, you’ll learn how to:

Filter lists and maps using expressive conditions
Find single or multiple elements efficiently
Transform collections using Groovy’s mapping functions
Group, sort, and aggregate data
Apply these techniques to real-world scenarios like JSON parsing and data cleanup

By the end, you’ll be able to write cleaner, more idiomatic Groovy code and fully leverage the power of collection methods to simplify your scripts and applications.

Prerequisites

Before diving into Groovy’s powerful collection methods, make sure you have the following setup and baseline knowledge.

What You Need

1. Groovy Installed

You should have Groovy 4.x or later installed on your machine.
To check your version, run:

groovy -version

If you need to install or upgrade, download it from the official Groovy website or use SDKMAN:

sdk install groovy

2. Java Installed (JDK 11 or Later)

Groovy runs on the JVM, so ensure you have at least JDK 11 installed:

java -version

3. A Text Editor or IDE

Any of the following works well:

IntelliJ IDEA with Groovy plugin
VS Code with Groovy extensions
Groovy Console (comes with Groovy)
Any text editor + Groovy CLI

How to Run the Code Examples

Throughout this tutorial, you can run Groovy scripts using:

Option 1: Groovy Console

Open the interactive console:

groovyConsole

Paste and execute code instantly.

Option 2: Groovy CLI

Create and run a Groovy script:

groovy script.groovy

Option 3: IntelliJ or VS Code

Create a Groovy project and run .groovy files directly.

Overview of Groovy Collections

Groovy enhances Java’s collection framework with cleaner syntax, additional helper methods, and powerful closure support. Before we dive into filtering and transforming collections, let’s take a quick look at how Groovy collections work and what makes them so convenient.

Groovy Collection Types

Groovy builds on top of standard Java collections, but makes them easier to work with.

1. Lists

Lists are ordered collections and the most commonly used type.

def numbers = [1, 2, 3, 4, 5]
def names = ['Ana', 'Budi', 'Charlie']

Groovy automatically creates a java.util.ArrayList when using [].

2. Maps

Maps store key–value pairs.

def user = [
    name: 'Djamware',
    role: 'admin',
    age : 30
]

Internally, this becomes a java.util.LinkedHashMap.

3. Sets

Sets contain unique elements.

def unique = [1, 2, 2, 3] as Set  // => [1, 2, 3]

Literal Syntax Improvements

Groovy allows you to create collections with very clean syntax:

Lists → []
Maps → [:]
Sets → [ ... ] as Set
Ranges → 1..10, 'a'..'z'

Ranges can also be used to generate lists:

def rangeList = (1..5).toList()  // [1, 2, 3, 4, 5]

Accessing Values

Groovy adds flexible ways to access collection items:

Why Groovy Collections Are Powerful

Groovy collections shine because they work seamlessly with closures. For example, you can:

Filter collections with short, readable conditions
Transform items using closures
Search, group, and aggregate data easily
Chain operations in a functional programming style

Here’s a simple example:

def evens = (1..10).findAll { it % 2 == 0 }
println evens  // [2, 4, 6, 8, 10]

This expressive syntax is the foundation for everything we’ll cover next.

Filtering Collections

Filtering is one of the most powerful and commonly used features of Groovy collections. Thanks to closures, you can express filtering logic concisely without writing loops or verbose conditions. The primary tool for filtering is the findAll method.

1. Using `findAll` on Lists

findAll returns all elements that match a given condition.

Example: Filter numbers

def numbers = [1, 2, 3, 4, 5, 6]
def evens = numbers.findAll { it % 2 == 0 }

println evens  // [2, 4, 6]

Example: Filter strings

def fruits = ['apple', 'banana', 'avocado', 'blueberry']
def startsWithA = fruits.findAll { it.startsWith('a') }

println startsWithA  // [apple, avocado]

2. Filtering Complex Objects

Groovy makes it easy to filter lists of maps or lists of objects.

Example: Filter the list of maps

def users = [
    [name: 'Alice',  role: 'admin'],
    [name: 'Bob',    role: 'user'],
    [name: 'Charlie', role: 'admin']
]

def admins = users.findAll { it.role == 'admin' }

println admins
// [[name:Alice, role:admin], [name:Charlie, role:admin]]

Example: Filter by multiple conditions

def products = [
    [name: 'Laptop',  price: 800],
    [name: 'Mouse',   price: 25],
    [name: 'Monitor', price: 150]
]

def expensive = products.findAll { it.price > 100 && it.name.contains('o') }

println expensive
// [[name:Monitor, price:150]]

3. Filtering Maps

findAll also works on maps — returning a new map containing only the entries that meet the condition.

Example: Filter map values

def scores = [
    Ana : 90,
    Budi: 70,
    Cici: 85
]

def highScores = scores.findAll { key, value -> value >= 80 }

println highScores
// [Ana:90, Cici:85]

Example: Filter by key

def filtered = scores.findAll { name, score -> name.startsWith('A') }

println filtered  // [Ana:90]

4. Filtering Nested Collections

Groovy handles nested structures elegantly.

Example: Filtering inside lists of lists

def matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

def rowsWithEvenNumbers = matrix.findAll { row ->
    row.any { it % 2 == 0 }
}

println rowsWithEvenNumbers
// [[1,2,3], [4,5,6], [7,8,9]]

5. Chaining Filters

You can chain multiple findAll calls for more complex pipelines.

def result = (1..20)
    .findAll { it % 2 == 0 }
    .findAll { it > 10 }

println result  // [12, 14, 16, 18, 20]

Groovy makes filtering collections readable and expressive — no loops, no boilerplate.

Finding Elements

While findAll returns all matching elements. Groovy also provides several methods for finding single elements, checking conditions, and performing quick validations on collections. These methods make your code more expressive and often replace traditional loops and conditionals.

1. Using `find` to Get the First Match

find returns the first element that satisfies the condition — or null if no element matches.

Example: Find the first even number

def numbers = [1, 3, 6, 7, 10]

def firstEven = numbers.find { it % 2 == 0 }

println firstEven  // 6

2. Using `any` and `every`

These methods are perfect for quick checks.

any

Returns true if at least one element satisfies the condition.

def names = ['Alex', 'Budi', 'Charlie']

println names.any { it.startsWith('A') }  // true

every

Returns true only if all elements match the condition.

println names.every { it.length() > 2 }  // true

3. Counting Matches with `count`

count returns the number of elements matching a condition.

def numbers = [1, 2, 3, 4, 4, 5, 6]

def countEven = numbers.count { it % 2 == 0 }

println countEven  // 3

4. Finding in Maps

Just like lists, you can search through maps using key–value closures.

Example: Find the first high score

def scores = [
    Ana : 90,
    Budi: 70,
    Cici: 85
]

def firstHighScore = scores.find { k, v -> v > 80 }

println firstHighScore  
// Ana=90

5. Finding in Complex Objects

Example: Find a user by email

def users = [
    [name: 'Alice',  email: '[email protected]'],
    [name: 'Bob',    email: '[email protected]'],
    [name: 'Charlie', email: '[email protected]']
]

def bob = users.find { it.email == '[email protected]' }

println bob
// [name:Bob, email:[email protected]]

6. Default Values Using the Elvis Operator

Combine find with ?: to provide a fallback value.

def result = users.find { it.name == 'David' } ?: [name: 'Guest']

println result
// [name:Guest]

7. Chaining Find Operations

You can chain find/findAll for expressive filtering + lookup flows.

def firstBigEven = (1..50)
    .findAll { it % 2 == 0 }
    .find { it > 20 }

println firstBigEven  // 22

Transforming Collections

Transformation is one of Groovy’s strongest features. Whether you want to reshape data, modify values, convert maps to lists, or flatten nested structures, Groovy provides a rich set of transformation methods that make these tasks expressive and concise.

The main transformation tools include:

collect → map each element to a new value
collectEntries → transform maps into new maps
collectMany → flatten and map at the same time
Type coercion → convert collections into specific types effortlessly

Let’s go through each one with practical examples.

1. Transforming with `collect`

collect creates a new list where each element is replaced with a transformed value.

Example: Square numbers

def numbers = [1, 2, 3, 4]

def squares = numbers.collect { it * 2 }

println squares  // [2, 4, 6, 8]

Example: Extract fields from objects

def users = [
    [name: 'Alice',  age: 25],
    [name: 'Bob',    age: 30],
    [name: 'Charlie', age: 28]
]

def names = users.collect { it.name }

println names  // [Alice, Bob, Charlie]

Example: Convert values to uppercase

def fruits = ['apple', 'banana', 'avocado']

println fruits.collect { it.toUpperCase() }
// [APPLE, BANANA, AVOCADO]

2. Transforming Maps with `collectEntries`

collectEntries returns a new map, allowing you to transform keys and/or values.

Example: Modify map values

def scores = [
    Ana : 90,
    Budi: 70
]

def bonusScores = scores.collectEntries { name, score ->
    [name, score + 10]
}

println bonusScores
// [Ana:100, Budi:80]

Example: Transform keys and values

def result = scores.collectEntries { name, score ->
    [(name.toUpperCase()): score * 2]
}

println result
// [ANA:180, BUDI:140]

3. Flatten and Map with `collectMany`

collectMany is perfect when each element returns a list or nested structure.

Example: Flatten nested lists

def matrix = [
    [1, 2],
    [3, 4],
    [5, 6]
]

def flat = matrix.collectMany { row -> row }

println flat  
// [1, 2, 3, 4, 5, 6]

Example: Transform + flatten

def words = ['hi', 'hello']

def chars = words.collectMany { it.toList() }

println chars
// ['h', 'i', 'h', 'e', 'l', 'l', 'o']

4. Type Coercion for Transformation

Groovy can convert collections into sets, maps, and lists using simple syntax.

Example: Convert list to set

def items = ['a', 'b', 'a', 'c']
def unique = items as Set

println unique  // [a, b, c]

Example: Convert list of key-value pairs to map

def pairs = [['a', 1], ['b', 2]]

def map = pairs.collectEntries { [it[0], it[1]] }

println map  // [a:1, b:2]

5. Chaining Transformations

Complex transformations become readable pipelines.

Example: Filter → Transform → Aggregate

def result = (1..10)
    .findAll { it % 2 == 0 }
    .collect { it * 10 }

println result  
// [20, 40, 60, 80, 100]

Groovy’s transformation utilities help convert raw data into clean, structured results with minimal code.

Groovy’s Powerful Iteration Methods

Groovy provides several iteration methods that make looping, reducing, and processing collections far more expressive compared to traditional Java loops. These methods work seamlessly with closures, allowing you to write cleaner, functional-style code.

In this section, we’ll explore:

each and eachWithIndex
inject (Groovy’s version of reduce/fold)
Chaining iteration operations for cleaner pipelines

1. Iterating with `each`

each is Groovy’s simplest iteration method. It loops through a collection and executes the closure for each item.

Example: Loop through a list

def numbers = [1, 2, 3]

numbers.each { println it }
// 1
// 2
// 3

Example: Loop through a map

def user = [name: 'Djamware', role: 'admin']

user.each { key, value ->
    println "$key = $value"
}
// name = Djamware
// role = admin

2. Iterating with `eachWithIndex`

Use eachWithIndex when you need the index of each item.

Example: Display list with index

def names = ['Ana', 'Budi', 'Charlie']

names.eachWithIndex { name, index ->
    println "$index: $name"
}
// 0: Ana
// 1: Budi
// 2: Charlie

3. Reducing Collections with `inject`

inject is one of Groovy’s most powerful iteration tools.
It works like reduce in JavaScript or Python — combining all elements into a single value.

Syntax

collection.inject(initialValue) { acc, value -> ... }

Example 1: Sum numbers

def total = [1, 2, 3, 4].inject(0) { acc, val ->
    acc + val
}

println total  // 10

Example 2: Build a string

def sentence = ['Groovy', 'makes', 'collections', 'fun']
    .inject('') { acc, word -> acc + word + ' ' }
    .trim()

println sentence
// Groovy makes collections fun

Example 3: Count character frequency

def chars = ['a', 'b', 'a', 'c', 'b']

def freq = chars.inject([:]) { acc, ch ->
    acc[ch] = (acc[ch] ?: 0) + 1
    acc
}

println freq
// [a:2, b:2, c:1]

4. Chaining Iteration Methods

Groovy allows method chaining for clean, functional-style pipelines.

Example: Even → Square → Sum

def result = (1..10)
    .findAll { it % 2 == 0 }
    .collect { it * it }
    .inject(0) { acc, val -> acc + val }

println result  
// 220

5. When to Use Each Method

Method	Best For
`each`	Simple iteration, logging, printing, side effects
`eachWithIndex`	Iteration when index matters
`inject`	Calculate totals, build strings, aggregate data
Chaining methods	Clean data processing pipelines

Working with Maps

Maps are one of Groovy’s most powerful collection types. They provide fast lookups, flexible key/value handling, and—thanks to Groovy’s closure support—extremely expressive filtering, finding, and transforming capabilities. In this section, you’ll learn how to filter maps, search for entries, transform key/value pairs, and convert maps to other structures.

1. Filtering Map Entries

Just like lists, maps support findAll for filtering. The closure receives both key and value.

Example: Filter values

def scores = [
    Ana : 90,
    Budi: 70,
    Cici: 85,
    Dodi: 60
]

def passed = scores.findAll { name, value -> value >= 80 }

println passed
// [Ana:90, Cici:85]

Example: Filter keys

def namesStartingWithC = scores.findAll { name, value ->
    name.startsWith('C')
}

println namesStartingWithC
// [Cici:85]

2. Finding Entries in Maps

Use find to retrieve the first matching key-value pair.

Example: Find entry with highest score above 80

def firstHighScore = scores.find { name, value -> value > 80 }

println firstHighScore
// Ana=90

Example: Find entry by custom logic

def shortName = scores.find { name, value -> name.length() <= 4 }

println shortName
// Budi=70

3. Transforming Maps with `collectEntries`

collectEntries lets you transform keys, values, or both—returning an entirely new map.

Example: Increase all scores

def boosted = scores.collectEntries { name, score ->
    [name, score + 5]
}

println boosted
// [Ana:95, Budi:75, Cici:90, Dodi:65]

Example: Convert keys to lowercase

def lowerKeys = scores.collectEntries { name, score ->
    [(name.toLowerCase()): score]
}

println lowerKeys
// [ana:90, budi:70, cici:85, dodi:60]

Example: Transform keys and values

def transformed = scores.collectEntries { name, score ->
    [(name.reverse()): score * 2]
}

println transformed
// [anA:180, iduB:140, iciC:170, idoD:120]

4. Converting Maps to Lists

Maps can be converted into many forms using Groovy’s expressive syntax.

Example: Convert map values to list

def scoreList = scores.collect { name, score -> score }

println scoreList
// [90, 70, 85, 60]

Example: Convert entries to list of maps

def entryList = scores.collect { name, score ->
    [name: name, score: score]
}

println entryList
// [[name:Ana, score:90], [name:Budi, score:70], ...]

Example: Convert map to list of pairs

def pairs = scores.collect { name, score -> [name, score] }

println pairs
// [[Ana,90], [Budi,70], [Cici,85], [Dodi,60]]

5. Sorting Maps

Groovy makes sorting maps easy using closures.

Example: Sort by value

def sortedByScore = scores.sort { key, value -> value }

println sortedByScore
// [Dodi:60, Budi:70, Cici:85, Ana:90]

Example: Sort by key

def sortedByName = scores.sort { key, value -> key }

println sortedByName
// [Ana:90, Budi:70, Cici:85, Dodi:60]

6. Reversing Maps

Maps can be reversed using .reverse().

def reversed = scores.reverse()

println reversed
// [Dodi:60, Cici:85, Budi:70, Ana:90]

7. Merging Maps

Groovy supports easy map merging using the + operator or the spread operator.

Example: Merge two maps

def defaults = [role: 'user', active: true]
def custom   = [active: false, email: '[email protected]']

def result = defaults + custom

println result
// [role:user, active:false, email:[email protected]]

8. Map Truthiness

Groovy treats empty maps as false and non-empty maps as true.

def m = [:]

if (!m) println 'Map is empty'

Maps in Groovy are incredibly flexible and expressive thanks to closure-based operations.

Grouping, Sorting, and Aggregation

Groovy makes it extremely easy to group, sort, and aggregate data—tasks that would typically require verbose loops in Java. With concise methods and closure support, you can process complex datasets elegantly.

In this section, you'll learn:

How to group collections with groupBy
How to sort lists and maps using closures
How to compute aggregates like sum, min, max, and averages

Let’s explore these powerful features one by one.

1. Grouping Collections with `groupBy`

The groupBy method organizes a collection into a map of groups, where each key represents a group name and the value is a list of items in that group.

Example: Group numbers by even/odd

def numbers = (1..10).toList()

def grouped = numbers.groupBy { it % 2 == 0 ? 'even' : 'odd' }

println grouped
// [odd:[1,3,5,7,9], even:[2,4,6,8,10]]

Example: Group objects by property

def users = [
    [name: 'Ana',   role: 'admin'],
    [name: 'Budi',  role: 'user'],
    [name: 'Cici',  role: 'admin'],
    [name: 'Dodi',  role: 'user']
]

def groupedByRole = users.groupBy { it.role }

println groupedByRole
// [admin:[[name:Ana, role:admin], [name:Cici, role:admin]], 
//  user:[[name:Budi, role:user], [name:Dodi, role:user]]]

2. Sorting Collections

Groovy supports sorting lists and maps using closures for custom logic.

Sorting Lists

Example: Sort list of numbers

def nums = [5, 1, 7, 2]

println nums.sort()
// [1, 2, 5, 7]

Example: Sort with custom criteria

def fruits = ['apple', 'banana', 'kiwi']

println fruits.sort { it.length() }
// [kiwi, apple, banana]

Sorting Maps

Example: Sort by key

def scores = [Ana: 90, Budi: 70, Cici: 85]

println scores.sort { key, value -> key }
// [Ana:90, Budi:70, Cici:85]

Example: Sort by value

println scores.sort { key, value -> value }
// [Budi:70, Cici:85, Ana:90]

3. Aggregation Functions

Groovy includes several handy methods for summarizing data.

3.1 Summing Values

Example: Sum list of numbers

def total = [1, 2, 3, 4].sum()

println total  // 10

Example: Sum using closure

def users = [
    [name: 'Ana',  age: 25],
    [name: 'Budi', age: 30]
]

def totalAge = users.sum { it.age }

println totalAge  // 55

3.2 Max and Min

def numbers = [4, 9, 1, 6]

println numbers.max()  // 9
println numbers.min()  // 1

Max/Min by property

def maxUser = users.max { it.age }
println maxUser
// [name:Budi, age:30]

3.3 Average (Using `sum` and count)

Groovy doesn’t have a direct avg() method, but it’s easy to compute.

def nums = [10, 20, 30]

def avg = nums.sum() / nums.size()

println avg  // 20

4. Combining Grouping + Aggregation

These methods can be chained to build powerful analytics pipelines.

Example: Total score per group

def data = [
    [category: 'A', score: 10],
    [category: 'B', score: 20],
    [category: 'A', score: 30],
    [category: 'B', score: 40]
]

def totals = data
    .groupBy { it.category }
    .collectEntries { cat, items ->
        [cat, items.sum { it.score }]
    }

println totals
// [A:40, B:60]

Grouping, sorting, and aggregation are essential for transforming data into meaningful insights, and Groovy makes these tasks simple and expressive.

Real-World Examples

Now that you’ve learned Groovy’s powerful filtering, finding, and transforming methods, it’s time to apply them to real-world scenarios. These examples simulate tasks you would commonly perform when processing data from APIs, files, databases, or automation scripts.

This section will cover:

Cleaning and transforming JSON data
Working with API-like responses
Extracting meaningful insights from collections
Building functional pipelines (filter → map → reduce)

1. Cleaning and Transforming JSON Data

When dealing with APIs, you often receive raw JSON that needs to be filtered or transformed before use.

Example: Filter active users and extract emails

def json = [
    [name: 'Ana',   active: true,  email: '[email protected]'],
    [name: 'Budi',  active: false, email: '[email protected]'],
    [name: 'Cici',  active: true,  email: '[email protected]']
]

// 1. Keep only active users
// 2. Transform into list of emails
def emails = json
    .findAll { it.active }
    .collect { it.email }

println emails
// [[email protected], [email protected]]

This pattern is extremely common when cleaning API responses before passing them into UIs or databases.

2. Transforming API Response Objects

Let’s say an API returns a list of products, but you only need a simplified structure for the frontend.

Example: Map product objects to a lightweight DTO

def products = [
    [id: 1, name: 'Laptop',  price: 900, stock: 12],
    [id: 2, name: 'Mouse',   price: 20,  stock: 0],
    [id: 3, name: 'Monitor', price: 150, stock: 5]
]

def result = products.collect { p ->
    [
        id   : p.id,
        name : p.name,
        inStock: p.stock > 0
    ]
}

println result
/* [
    [id:1, name:Laptop, inStock:true],
    [id:2, name:Mouse, inStock:false],
    [id:3, name:Monitor, inStock:true]
] */

A clean, declarative transformation pipeline—ideal for backend-to-frontend data shaping.

3. Data Cleanup Script

If you often parse text files or logs, Groovy’s filtering and transformation methods help clean data quickly.

Example: Remove blanks and normalize strings

def lines = [' Groovy', '', '  ', 'is', ' awesome  ']

def cleaned = lines
    .findAll { it.trim() }        // remove blank lines
    .collect { it.trim().toLowerCase() }

println cleaned
// ['groovy', 'is', 'awesome']

Perfect for preparing input before analysis.

4. Analytics: Group + Sum + Sort

Let’s calculate total sales per category and list them from highest to lowest.

def sales = [
    [category: 'A', amount: 100],
    [category: 'B', amount: 50],
    [category: 'A', amount: 200],
    [category: 'C', amount: 150]
]

def totals = sales
    .groupBy { it.category }
    .collectEntries { cat, items ->
        [cat, items.sum { it.amount }]
    }
    .sort { -it.value }

println totals
// [A:300, C:150, B:50]

A powerful analytic pipeline in just a few lines.

5. Extracting Unique Values

You often need unique values from a dataset.

Example: Unique skills from a list of employees

def employees = [
    [name:'Ana', skills:['Groovy', 'Java']],
    [name:'Budi', skills:['Java', 'SQL']],
    [name:'Cici', skills:['Groovy', 'Kotlin']]
]

def uniqueSkills = employees
    .collectMany { it.skills }
    .toSet()

println uniqueSkills
// [Groovy, Java, SQL, Kotlin]

6. Detecting Inconsistencies

Example: Find incomplete objects

def records = [
    [id:1, name:'Ana'],
    [id:2],
    [id:3, name:'Cici']
]

def invalid = records.findAll { !it.name }

println invalid
// [[id:2]]

Great for data validation tasks.

7. Building Full Pipelines

Finally, here’s a realistic multi-step pipeline:

Scenario:

You receive raw transaction data. You must:

Filter only completed transactions
Extract the amounts
Convert to USD
Sum the final results

def transactions = [
    [id: 1, status: 'complete', amount: 100, currency: 'EUR'],
    [id: 2, status: 'pending',  amount: 200, currency: 'EUR'],
    [id: 3, status: 'complete', amount: 150, currency: 'EUR']
]

def eurToUsd = 1.1

def totalUsd = transactions
    .findAll { it.status == 'complete' }
    .collect { it.amount * eurToUsd }
    .sum()

println totalUsd
// 275.0

Elegant, readable, and extremely efficient—this is how Groovy shines in real-world data processing.

Best Practices

Groovy’s collection APIs are powerful, expressive, and concise — but with great power comes the need for clarity and maintainability. This section covers practical best practices to help you write clean, efficient, and idiomatic Groovy code.

1. Write Clear and Readable Closures

Avoid overly complex inline closures. Aim for readability.

❌ Hard to read

users.findAll { it.age > 18 && it.active && it.roles.contains('admin') }

✅ More readable

def isEligible = { u -> u.age > 18 && u.active && u.roles.contains('admin') }

def eligibleUsers = users.findAll(isEligible)

Readable closures help your future self—and your team.

2. Use Method Chaining Wisely

Chaining makes code elegant, but too many chained operations can hurt readability.

❌ Over-chained

def result = nums.findAll { it % 2 == 0 }.collect { it * 3 }.findAll { it > 10 }.sum()

✅ Well-structured chain

def result = nums
    .findAll { it % 2 == 0 }
    .collect { it * 3 }
    .findAll { it > 10 }
    .sum()

Break long chains into logical steps.

3. Avoid Unnecessary Intermediate Collections

If performance matters, reduce temporary steps.

❌ Creates unnecessary lists

def evens = nums.findAll { it % 2 == 0 }
def doubled = evens.collect { it * 2 }
def total = doubled.sum()

✅ Efficient pipeline

def total = nums
    .findAll { it % 2 == 0 }
    .collect { it * 2 }
    .sum()

4. Prefer `collectEntries` for Map Transformations

Avoid manually building new maps with loops.

❌ Verbose loop

def newMap = [:]
oldMap.each { k, v -> newMap[k] = v * 2 }

✅ Groovy way

def newMap = oldMap.collectEntries { k, v -> [k, v * 2] }

Cleaner and more idiomatic.

5. Know Map Truthiness

Remember:

[:] → false
Non-empty map → true

Use this for quick validations.

if (!config) {
    println "Configuration is missing!"
}

6. Use Spread Operator for Extracting Values

Groovy’s spread operator makes extracting properties easy.

Instead of:

def names = users.collect { it.name }

Use:

def names = users*.name

It works for nested properties too:

users*.address*.city

7. Use Default Values to Avoid Null Checks

Combine find with the Elvis operator:

def admin = users.find { it.role == 'admin' } ?: [name: 'Guest Admin']

Useful for handling missing or optional data.

8. Prefer Immutable Collections (When Possible)

Mutable structures can cause hidden bugs in large systems.
Use as ImmutableList or copy maps/lists before mutation.

def safeList = [1,2,3].asImmutable()

9. Don’t Overuse Groovy Magic

Groovy allows many shortcuts — but too much “magic” can confuse readers.

Example: prefer clarity over clever one-liners.

❌ Clever but cryptic

nums.findAll{it&1==0}*.toString()*.

✅ Clear and maintainable

nums.findAll { it % 2 == 0 }.collect { it.toString() }

10. Benchmark If Performance Matters

Groovy is expressive but not always the fastest.
When handling large datasets (hundreds of thousands+ entries):

Prefer each over heavy chaining
Use inject carefully (may be slower for large loops)
Consider using Java Streams for tight loops
Use profiling tools like GroovyConsole.inspect(List) or JMH

Groovy's collection utilities are incredibly powerful, but following these best practices will keep your code clean, fast, and professional.

Conclusion

Groovy’s collection APIs are one of the language’s strongest features. With expressive syntax, closure-based operations, and a large set of built-in helper methods, Groovy makes filtering, searching, and transforming collections not only simpler—but also more readable and maintainable.

In this tutorial, you learned how to:

Filter data using findAll, even across nested and complex structures
Find elements efficiently using find, any, every, and count
Transform collections with collect, collectEntries, and collectMany
Iterate cleanly with each, eachWithIndex, and inject
Manipulate maps in expressive and idiomatic ways
Group, sort, and aggregate your data using powerful built-in operations
Apply these techniques to real-world scenarios such as JSON processing, data cleanup, analytics, and pipelines

By mastering these core techniques, you’ll be able to write elegant, concise, and highly maintainable Groovy scripts—perfect for automation, API data handling, backend development, and any task involving lists, maps, or nested structures.

Groovy continues to be a top choice for developers who want the power of the JVM with the flexibility of a dynamic language. With the knowledge from this tutorial, you’re well-equipped to harness that power effectively.

You can find the full source code on our GitHub.

That's just the basics. If you need more deep learning about Groovy and Grails, you can take the following cheap course:

Thanks!

How to Filter, Find, and Transform Collections in Groovy

Master Groovy collection operations with this guide. Learn to filter, find, transform, group, and aggregate data using expressive and powerful Groovy methods.

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