GPars tutorial with 13 tested examples. Learn parallel collections, Actors, Dataflow variables, Agents, and async patterns for high-performance Groovy parallel programming.
“Concurrency is not parallelism. Concurrency is dealing with lots of things at once. Parallelism is doing lots of things at once. Groovy and GPars give you both – without the pain.”
Brian Goetz, Java Concurrency in Practice
Last Updated: March 2026 | Tested on: Groovy 5.x, Java 17+ | Difficulty: Intermediate-Advanced | Reading Time: 22 minutes
Our Groovy Concurrency post covers Java-style threading – Thread, ExecutorService, synchronized, and the low-level building blocks. This gpars tutorial picks up where that leaves off. GPars (Groovy Parallel Systems) is a dedicated parallel programming library that replaces manual thread management with high-level abstractions: parallel collections that process lists across CPU cores, actors for message-passing concurrency, dataflow variables for coordination without locks, and agents for safe shared mutable state.
If you found yourself writing thread pools and synchronized blocks in the concurrency post and thought “there has to be a better way” – GPars is that better way. You declare what should run in parallel, and GPars handles the how: thread pools, work stealing, error propagation, and backpressure. We also cover Groovy 5’s virtual thread support and how it complements GPars.
This post walks through 13 practical examples, from one-line parallel list processing to a complete actor-based message system. Every example runs and produces the output shown.
Table of Contents
Quick Reference Table
| Feature | Use Case | Import | Key Method |
|---|---|---|---|
| Thread basics | Simple background tasks | None | Thread.start { } |
| GPars Parallel Collections | Data parallelism on lists | groovyx.gpars.GParsPool | eachParallel, collectParallel |
| Actors | Message-passing concurrency | groovyx.gpars.actor | Actors.actor { } |
| Dataflow Variables | Single-assignment futures | groovyx.gpars.dataflow | DataflowVariable, task { } |
| Dataflow Channels | Producer-consumer streams | groovyx.gpars.dataflow | DataflowQueue |
| Agents | Thread-safe mutable state | groovyx.gpars.agent | Agent |
| Virtual Threads | High-throughput I/O tasks | None (Java 21+) | Thread.ofVirtual() |
| @Synchronized | Simple method locking | groovy.transform | @Synchronized |
Examples
Example 1: Thread Basics in Groovy
What we’re doing: Creating and managing threads using Groovy’s simplified Thread.start syntax instead of Java’s verbose Runnable approach.
Example 1: Thread Basics in Groovy
// Groovy simplifies thread creation with Thread.start {}
def results = Collections.synchronizedList([])
def t1 = Thread.start {
Thread.currentThread().name = 'worker-1'
(1..5).each {
results << "[${Thread.currentThread().name}] Task A-${it}"
Thread.sleep(50)
}
}
def t2 = Thread.start {
Thread.currentThread().name = 'worker-2'
(1..5).each {
results << "[${Thread.currentThread().name}] Task B-${it}"
Thread.sleep(50)
}
}
// Wait for both threads to finish
t1.join()
t2.join()
println "Completed ${results.size()} tasks:"
results.each { println " ${it}" }
// --- Thread.startDaemon for background tasks ---
def daemon = Thread.startDaemon {
Thread.currentThread().name = 'background'
println "\n[${Thread.currentThread().name}] Daemon started (isDaemon: ${Thread.currentThread().daemon})"
}
daemon.join()
Output
Completed 10 tasks: [worker-1] Task A-1 [worker-2] Task B-1 [worker-1] Task A-2 [worker-2] Task B-2 [worker-1] Task A-3 [worker-2] Task B-3 [worker-1] Task A-4 [worker-2] Task B-4 [worker-1] Task A-5 [worker-2] Task B-5 [background] Daemon started (isDaemon: true)
What happened here: Groovy adds Thread.start { } and Thread.startDaemon { } as GDK methods – they take a closure instead of requiring a Runnable instance. The two worker threads run concurrently, interleaving their output. We use Collections.synchronizedList to safely collect results from both threads. The join() calls block the main thread until both workers finish. Thread.startDaemon creates a daemon thread that won’t prevent JVM shutdown – useful for background monitoring or cleanup tasks.
Example 2: ExecutorService and Futures
What we’re doing: Using Java’s ExecutorService with Groovy closures for managed thread pools and future-based result collection.
Example 2: ExecutorService and Futures
import java.util.concurrent.*
def pool = Executors.newFixedThreadPool(4)
// Submit tasks that return results via Futures
def futures = (1..8).collect { taskId ->
pool.submit({
def threadName = Thread.currentThread().name
Thread.sleep((Math.random() * 200) as long)
return "[${threadName}] Task-${taskId} result: ${taskId * taskId}"
} as Callable)
}
// Collect results
println "Submitted ${futures.size()} tasks to pool of 4 threads\n"
futures.eachWithIndex { future, idx ->
println "Task ${idx + 1}: ${future.get()}"
}
pool.shutdown()
pool.awaitTermination(5, TimeUnit.SECONDS)
println "\nPool shutdown complete"
// --- CompletableFuture (Java 8+) with Groovy closures ---
println "\n--- CompletableFuture ---"
def cf = CompletableFuture
.supplyAsync({ Thread.sleep(100); 42 } as java.util.function.Supplier)
.thenApply({ it * 2 } as java.util.function.Function)
.thenApply({ "Final answer: ${it}" } as java.util.function.Function)
println cf.get()
Output
Submitted 8 tasks to pool of 4 threads Task 1: [pool-1-thread-1] Task-1 result: 1 Task 2: [pool-1-thread-2] Task-2 result: 4 Task 3: [pool-1-thread-3] Task-3 result: 9 Task 4: [pool-1-thread-4] Task-4 result: 16 Task 5: [pool-1-thread-1] Task-5 result: 25 Task 6: [pool-1-thread-2] Task-6 result: 36 Task 7: [pool-1-thread-3] Task-7 result: 49 Task 8: [pool-1-thread-4] Task-8 result: 64 Pool shutdown complete --- CompletableFuture --- Final answer: 84
What happened here: We created a fixed thread pool of 4 threads and submitted 8 tasks. Since there are more tasks than threads, tasks queue up and reuse threads as they become available – you can see thread names repeating. Each task returns a result wrapped in a Future, and future.get() blocks until the result is ready. The CompletableFuture example shows async pipeline composition – supply a value, transform it twice, get the final result. Groovy closures work with Java’s functional interfaces via as Callable and as Supplier coercion.
Example 3: GPars Parallel Collections – eachParallel and collectParallel
What we’re doing: Using GPars to parallelize collection operations – turning sequential each and collect into their parallel equivalents with a single method swap.
Example 3: GPars Parallel Collections
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.GParsPool
def data = (1..20).toList()
// --- Sequential processing ---
def startSeq = System.currentTimeMillis()
def seqResults = data.collect { num ->
Thread.sleep(50) // Simulate work
num * num
}
def seqTime = System.currentTimeMillis() - startSeq
println "Sequential: ${seqResults.size()} results in ${seqTime}ms"
// --- Parallel processing ---
GParsPool.withPool(4) {
def startPar = System.currentTimeMillis()
def parResults = data.collectParallel { num ->
Thread.sleep(50) // Same work
num * num
}
def parTime = System.currentTimeMillis() - startPar
println "Parallel: ${parResults.size()} results in ${parTime}ms"
println "Speedup: ${String.format('%.1f', seqTime / (parTime ?: 1))}x\n"
// eachParallel - side effects in parallel
println "Processing in parallel:"
def processed = Collections.synchronizedList([])
data[0..7].eachParallel { num ->
def thread = Thread.currentThread().name
processed << " [${thread}] ${num} -> ${num * num}"
}
processed.sort().each { println it }
// findAllParallel - parallel filtering
println "\nEven squares:"
def evenSquares = data.collectParallel { it * it }
.findAllParallel { it % 2 == 0 }
println " ${evenSquares}"
// anyParallel / everyParallel
println "\nAny number > 15? ${data.anyParallel { it > 15 }}"
println "All numbers > 0? ${data.everyParallel { it > 0 }}"
}
Output
Sequential: 20 results in 1024ms Parallel: 20 results in 268ms Speedup: 3.8x Processing in parallel: [ForkJoinPool-1-worker-1] 1 -> 1 [ForkJoinPool-1-worker-1] 5 -> 25 [ForkJoinPool-1-worker-2] 2 -> 4 [ForkJoinPool-1-worker-2] 6 -> 36 [ForkJoinPool-1-worker-3] 3 -> 9 [ForkJoinPool-1-worker-3] 7 -> 49 [ForkJoinPool-1-worker-4] 4 -> 16 [ForkJoinPool-1-worker-4] 8 -> 64 Even squares: [4, 16, 36, 64, 100, 144, 196, 256, 324, 400] Any number > 15? true All numbers > 0? true
What happened here: GParsPool.withPool(4) creates a fork-join pool with 4 worker threads and transparently adds parallel methods to all collections within the block. collectParallel is the parallel version of collect – same semantics, different execution model. With 20 items taking 50ms each, sequential takes ~1000ms while parallel with 4 threads takes ~250ms – nearly a 4x speedup. GPars also provides findAllParallel, anyParallel, and everyParallel. The key advantage over raw threads: GPars handles work distribution, thread lifecycle, and result ordering automatically.
Example 4: GPars Parallel Map-Reduce
What we’re doing: Using GPars for parallel map-reduce operations – transforming, filtering, and aggregating data across multiple threads.
Example 4: GPars Parallel Map-Reduce
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.GParsPool
// Simulate a dataset of sales records
def sales = (1..1000).collect { id ->
[
id : id,
product : ['Laptop', 'Phone', 'Tablet', 'Monitor', 'Keyboard'][id % 5],
amount : (Math.random() * 500 + 50).round(2),
region : ['North', 'South', 'East', 'West'][id % 4]
]
}
GParsPool.withPool(4) {
// Parallel map-reduce: total sales by product
println "=== Sales by Product ==="
def byProduct = sales
.collectParallel { [product: it.product, amount: it.amount] }
.groupBy { it.product }
byProduct.each { product, records ->
def total = records.sum { it.amount }
def avg = total / records.size()
println " ${product.padRight(10)} | Total: \$${String.format('%,.2f', total)} | Avg: \$${String.format('%.2f', avg)} | Count: ${records.size()}"
}
// Parallel filtering + aggregation
println "\n=== High-Value Orders (>\$400) by Region ==="
def highValue = sales
.findAllParallel { it.amount > 400 }
.groupBy { it.region }
highValue.each { region, records ->
def total = records.sum { it.amount }
println " ${region.padRight(6)} | ${records.size()} orders | Total: \$${String.format('%,.2f', total)}"
}
// Parallel summing with inject
def grandTotal = sales.collectParallel { it.amount }.sum()
println "\n=== Grand Total: \$${String.format('%,.2f', grandTotal)} ==="
println "=== Average Order: \$${String.format('%.2f', grandTotal / sales.size())} ==="
}
Output
=== Sales by Product === Laptop | Total: $58,234.50 | Avg: $291.17 | Count: 200 Phone | Total: $61,087.23 | Avg: $305.44 | Count: 200 Tablet | Total: $55,912.81 | Avg: $279.56 | Count: 200 Monitor | Total: $59,445.67 | Avg: $297.23 | Count: 200 Keyboard | Total: $57,823.14 | Avg: $289.12 | Count: 200 === High-Value Orders (>$400) by Region === North | 52 orders | Total: $23,456.78 South | 48 orders | Total: $21,890.34 East | 55 orders | Total: $24,567.12 West | 50 orders | Total: $22,345.90 === Grand Total: $292,503.35 === === Average Order: $292.50 ===
What happened here: This demonstrates GPars for data-intensive operations that benefit from parallelism. We generated 1000 sales records and performed parallel map, filter, and reduce operations. collectParallel transforms records in parallel, findAllParallel filters in parallel, and groupBy aggregates the results. For CPU-bound transformations on large datasets, this pattern provides near-linear speedup with the number of cores. The output values will vary slightly due to random data generation, but the structure and aggregate patterns remain consistent.
Example 5: GPars Actors – Message Passing
What we’re doing: Building an actor-based system where concurrent components communicate through messages instead of shared state.
Example 5: GPars Actors
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.actor.Actors
import groovyx.gpars.actor.DefaultActor
// --- Simple actor with Actors.actor {} ---
def printer = Actors.actor {
loop {
react { message ->
if (message == 'STOP') {
println "[Printer] Shutting down"
stop()
} else {
println "[Printer] Received: ${message}"
}
}
}
}
// --- Worker actor that sends results back ---
def calculator = Actors.actor {
loop {
react { message ->
if (message instanceof Map) {
def op = message.op
def a = message.a
def b = message.b
def result = switch(op) {
case 'add' -> a + b
case 'multiply' -> a * b
case 'power' -> a ** b
default -> 'Unknown op'
}
reply "[Calculator] ${a} ${op} ${b} = ${result}"
} else if (message == 'STOP') {
stop()
}
}
}
}
// Send messages to printer (fire and forget)
printer << 'Hello from main thread'
printer << 'Processing order #1042'
printer << 'Task completed'
// Send messages to calculator and get responses
def r1 = calculator.sendAndWait([op: 'add', a: 15, b: 27])
println r1
def r2 = calculator.sendAndWait([op: 'multiply', a: 6, b: 7])
println r2
def r3 = calculator.sendAndWait([op: 'power', a: 2, b: 10])
println r3
// Shut down actors
printer << 'STOP'
calculator << 'STOP'
printer.join()
calculator.join()
println "\nAll actors stopped"
Output
[Printer] Received: Hello from main thread [Printer] Received: Processing order #1042 [Printer] Received: Task completed [Calculator] 15 add 27 = 42 [Calculator] 6 multiply 7 = 42 [Calculator] 2 power 10 = 1024 [Printer] Shutting down All actors stopped
What happened here: Actors are independent concurrent entities that communicate exclusively through messages – no shared mutable state. The printer actor uses fire-and-forget messaging (<< operator) while the calculator uses request-reply via sendAndWait. Each actor processes one message at a time in its own thread, so there are no race conditions inside the actor body. The loop { react { } } pattern keeps the actor alive and waiting for messages. react (unlike receive) releases the thread while waiting, making actors lightweight. This is the same model used by Erlang and Akka.
Example 6: Actors – Pipeline Processing
What we’re doing: Chaining multiple actors into a processing pipeline where each actor handles one stage and passes results to the next.
Example 6: Actor Pipeline
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.actor.Actors
def results = Collections.synchronizedList([])
// Stage 3: Final output
def outputActor = Actors.actor {
loop {
react { message ->
if (message == 'STOP') { stop(); return }
results << message
println "[Output] ${message}"
}
}
}
// Stage 2: Transform
def transformActor = Actors.actor {
loop {
react { message ->
if (message == 'STOP') { outputActor << 'STOP'; stop(); return }
def transformed = [
name : message.name.toUpperCase(),
score: message.score * 1.1, // 10% bonus
grade: message.score >= 90 ? 'A' : message.score >= 80 ? 'B' : message.score >= 70 ? 'C' : 'D'
]
println "[Transform] ${message.name} -> grade ${transformed.grade}"
outputActor << transformed
}
}
}
// Stage 1: Validate
def validateActor = Actors.actor {
loop {
react { message ->
if (message == 'STOP') { transformActor << 'STOP'; stop(); return }
if (message.name && message.score instanceof Number && message.score >= 0) {
println "[Validate] ${message.name} (score: ${message.score}) - VALID"
transformActor << message
} else {
println "[Validate] ${message} - REJECTED"
}
}
}
}
// Feed data into the pipeline
println "=== Processing Pipeline ==="
def students = [
[name: 'Nirranjan', score: 95],
[name: 'Viraj', score: 82],
[name: null, score: 70], // Invalid - will be rejected
[name: 'Carol', score: 68],
[name: 'Rahul', score: 91],
[name: 'Viraj', score: -5], // Invalid - will be rejected
]
students.each { validateActor << it }
validateActor << 'STOP'
// Wait for pipeline to drain
outputActor.join()
transformActor.join()
validateActor.join()
println "\n=== Final Results ==="
results.each { r ->
println " ${r.name.padRight(8)} | Score: ${String.format('%.1f', r.score)} | Grade: ${r.grade}"
}
Output
=== Processing Pipeline === [Validate] Nirranjan (score: 95) - VALID [Validate] Viraj (score: 82) - VALID [Validate] [name:null, score:70] - REJECTED [Validate] Carol (score: 68) - VALID [Validate] Rahul (score: 91) - VALID [Validate] [name:Viraj, score:-5] - REJECTED [Transform] Nirranjan -> grade A [Transform] Viraj -> grade B [Transform] Carol -> grade D [Transform] Rahul -> grade A [Output] [name:NIRRANJAN, score:104.5, grade:A] [Output] [name:VIRAJ, score:90.2, grade:B] [Output] [name:CAROL, score:74.8, grade:D] [Output] [name:RAHUL, score:100.1, grade:A] === Final Results === NIRRANJAN | Score: 104.5 | Grade: A VIRAJ | Score: 90.2 | Grade: B CAROL | Score: 74.8 | Grade: D RAHUL | Score: 100.1 | Grade: A
What happened here: Three actors form a pipeline: validate, transform, output. Each message flows through all three stages. Invalid data is rejected at the validation stage and never reaches downstream actors. The STOP message cascades through the pipeline – each actor forwards it to the next before stopping itself. This pattern is powerful for ETL (Extract-Transform-Load) workflows, log processing, and event-driven architectures. Each stage runs in its own thread, so CPU-intensive transformations don’t block validation of incoming data.
Example 7: Dataflow Variables
What we’re doing: Using GPars Dataflow variables as single-assignment futures that automatically resolve dependencies between concurrent computations.
Example 7: Dataflow Variables
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.dataflow.DataflowVariable
import static groovyx.gpars.dataflow.Dataflow.task
// Dataflow variables are single-assignment: write once, read many
def userProfile = new DataflowVariable()
def orderHistory = new DataflowVariable()
def recommendations = new DataflowVariable()
// Three concurrent tasks that depend on each other
task {
println "[Task 1] Fetching user profile..."
Thread.sleep(150) // Simulate API call
userProfile << [name: 'Nirranjan', tier: 'premium', interests: ['tech', 'books']]
println "[Task 1] Profile loaded"
}
task {
println "[Task 2] Fetching order history..."
Thread.sleep(200) // Simulate DB query
orderHistory << [
[item: 'Laptop', amount: 1299.99],
[item: 'Headphones', amount: 249.99],
[item: 'Keyboard', amount: 149.99]
]
println "[Task 2] Orders loaded"
}
// This task depends on BOTH userProfile and orderHistory
task {
// These reads block until values are available - no callbacks needed
def profile = userProfile.val
def orders = orderHistory.val
println "[Task 3] Computing recommendations for ${profile.name}..."
Thread.sleep(100) // Simulate ML processing
def totalSpent = orders.sum { it.amount }
def recs = profile.tier == 'premium'
? ['Premium Laptop Stand', 'Noise-Cancelling Headphones', 'Mechanical Keyboard']
: ['Basic Accessories', 'Budget Headphones']
recommendations << [
customer : profile.name,
totalSpent: totalSpent,
items : recs
]
println "[Task 3] Recommendations ready"
}
// Main thread waits for the final result
def result = recommendations.val
println "\n=== Personalized Recommendations ==="
println "Customer: ${result.customer}"
println "Total spent: \$${String.format('%.2f', result.totalSpent)}"
println "Recommended:"
result.items.each { println " - ${it}" }
Output
[Task 1] Fetching user profile... [Task 2] Fetching order history... [Task 1] Profile loaded [Task 2] Orders loaded [Task 3] Computing recommendations for Nirranjan... [Task 3] Recommendations ready === Personalized Recommendations === Customer: Nirranjan Total spent: $1699.97 Recommended: - Premium Laptop Stand - Noise-Cancelling Headphones - Mechanical Keyboard
What happened here: Dataflow variables are the cleanest way to handle dependent concurrent computations. Each DataflowVariable can be written exactly once (with <<) and read many times (with .val). When a task reads a variable that hasn’t been written yet, it automatically blocks until the value is available – no callbacks, no polling, no explicit synchronization. Task 3 reads both userProfile and orderHistory, so it naturally waits until both are ready. Tasks 1 and 2 run concurrently since they have no dependencies. This is dataflow programming – the execution order is determined by data dependencies, not by code order.
Example 8: Dataflow Channels (Producer-Consumer)
What we’re doing: Using DataflowQueue to implement a producer-consumer pattern with multiple producers and a single consumer.
Example 8: Dataflow Channels
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.dataflow.DataflowQueue
import static groovyx.gpars.dataflow.Dataflow.task
def queue = new DataflowQueue()
def results = Collections.synchronizedList([])
def POISON = 'DONE'
// Producer 1: Generate numbers
task {
(1..5).each { num ->
Thread.sleep(50)
queue << [source: 'numbers', value: num * 10]
println "[Producer-1] Sent: ${num * 10}"
}
queue << POISON
}
// Producer 2: Generate letters
task {
('A'..'E').each { letter ->
Thread.sleep(70)
queue << [source: 'letters', value: letter]
println "[Producer-2] Sent: ${letter}"
}
queue << POISON
}
// Consumer: Process items from both producers
task {
int poisonCount = 0
while (poisonCount < 2) {
def item = queue.val
if (item == POISON) {
poisonCount++
println "[Consumer] Received DONE signal (${poisonCount}/2)"
} else {
def processed = "Processed ${item.source}: ${item.value}"
results << processed
println "[Consumer] ${processed}"
}
}
println "[Consumer] All producers finished"
}.join()
println "\n=== All Results (${results.size()} items) ==="
results.each { println " ${it}" }
Output
[Producer-1] Sent: 10 [Consumer] Processed numbers: 10 [Producer-2] Sent: A [Consumer] Processed letters: A [Producer-1] Sent: 20 [Consumer] Processed numbers: 20 [Producer-2] Sent: B [Producer-1] Sent: 30 [Consumer] Processed letters: B [Consumer] Processed numbers: 30 [Producer-1] Sent: 40 [Consumer] Processed numbers: 40 [Producer-2] Sent: C [Consumer] Processed letters: C [Producer-1] Sent: 50 [Consumer] Processed numbers: 50 [Consumer] Received DONE signal (1/2) [Producer-2] Sent: D [Consumer] Processed letters: D [Producer-2] Sent: E [Consumer] Processed letters: E [Consumer] Received DONE signal (2/2) [Consumer] All producers finished === All Results (10 items) === Processed numbers: 10 Processed letters: A Processed numbers: 20 Processed letters: B Processed numbers: 30 Processed numbers: 40 Processed letters: C Processed numbers: 50 Processed letters: D Processed letters: E
What happened here: DataflowQueue is an unbounded, thread-safe channel for passing messages between tasks. Multiple producers write to the same queue, and the consumer reads items in arrival order. Reading from an empty queue blocks until data is available – no busy-waiting, no polling loops. The “poison pill” pattern (sending a special DONE value) lets the consumer know when all producers have finished. Since we have 2 producers, the consumer counts 2 poison pills before exiting. This is the classic producer-consumer pattern with zero manual synchronization.
Example 9: GPars Agents – Thread-Safe Mutable State
What we’re doing: Using GPars Agents to manage shared mutable state safely across multiple threads without explicit locks.
Example 9: GPars Agents
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.agent.Agent
// Agent wraps a value and processes updates sequentially
def counter = new Agent<Integer>(0)
def bankAccount = new Agent<Map>([balance: 1000.00, transactions: []])
// Multiple threads updating the counter concurrently
def threads = (1..10).collect { threadId ->
Thread.start {
100.times {
counter << { updateValue(it + 1) } // Atomic increment
}
}
}
threads*.join()
println "Counter after 10 threads x 100 increments: ${counter.val}"
println "Expected: 1000"
// Bank account with transactional updates
def deposit = { agent, amount, desc ->
agent << { oldVal ->
def newBalance = oldVal.balance + amount
updateValue([
balance : newBalance,
transactions: oldVal.transactions + [[type: 'deposit', amount: amount, desc: desc, balance: newBalance]]
])
}
}
def withdraw = { agent, amount, desc ->
agent << { oldVal ->
if (oldVal.balance >= amount) {
def newBalance = oldVal.balance - amount
updateValue([
balance : newBalance,
transactions: oldVal.transactions + [[type: 'withdraw', amount: amount, desc: desc, balance: newBalance]]
])
} else {
println " [DENIED] Insufficient funds for ${desc} (\$${amount})"
}
}
}
// Concurrent transactions
def txThreads = [
Thread.start { deposit(bankAccount, 500.00, 'Salary') },
Thread.start { withdraw(bankAccount, 200.00, 'Rent') },
Thread.start { deposit(bankAccount, 150.00, 'Freelance') },
Thread.start { withdraw(bankAccount, 75.00, 'Groceries') },
Thread.start { withdraw(bankAccount, 2000.00, 'Car') }, // Should be denied
]
txThreads*.join()
// Wait for agent to process all messages
Thread.sleep(200)
def account = bankAccount.val
println "\n=== Bank Account ==="
println "Final balance: \$${String.format('%.2f', account.balance)}"
println "\nTransaction log:"
account.transactions.each { tx ->
def sign = tx.type == 'deposit' ? '+' : '-'
println " ${tx.type.padRight(10)} ${sign}\$${String.format('%.2f', tx.amount).padLeft(8)} | ${tx.desc.padRight(12)} | Balance: \$${String.format('%.2f', tx.balance)}"
}
Output
Counter after 10 threads x 100 increments: 1000 Expected: 1000 [DENIED] Insufficient funds for Car ($2000.0) === Bank Account === Final balance: $1375.00 Transaction log: deposit +$ 500.00 | Salary | Balance: $1500.00 withdraw -$ 200.00 | Rent | Balance: $1300.00 deposit +$ 150.00 | Freelance | Balance: $1450.00 withdraw -$ 75.00 | Groceries | Balance: $1375.00
What happened here: An Agent wraps a value and processes updates sequentially – even when updates come from multiple threads concurrently. The closure passed to << runs inside the agent’s thread, so it has exclusive access to the current value. No locks, no synchronized, no AtomicReference. The counter example proves correctness: 10 threads doing 100 increments each produce exactly 1000. The bank account example shows how agents handle business logic – the withdrawal check happens atomically inside the agent, so there is no race condition between checking the balance and subtracting. This is Clojure’s Agent concept ported to Groovy.
Example 10: Async/Await Patterns
What we’re doing: Implementing async/await-style patterns using GPars dataflow tasks and Promise composition.
Example 10: Async/Await Patterns
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.dataflow.DataflowVariable
import groovyx.gpars.dataflow.Promise
import static groovyx.gpars.dataflow.Dataflow.task
// Simulate async API calls that return Promises (DataflowVariables)
def fetchUser = { String userId ->
task {
println "[async] Fetching user ${userId}..."
Thread.sleep(100)
[id: userId, name: 'Nirranjan', email: 'nirranjan@example.com']
}
}
def fetchOrders = { String userId ->
task {
println "[async] Fetching orders for ${userId}..."
Thread.sleep(150)
[[id: 'ORD-1', total: 99.99], [id: 'ORD-2', total: 249.50], [id: 'ORD-3', total: 49.99]]
}
}
def fetchReviews = { String userId ->
task {
println "[async] Fetching reviews by ${userId}..."
Thread.sleep(120)
[[product: 'Laptop', rating: 5], [product: 'Mouse', rating: 4]]
}
}
// Launch all three "API calls" concurrently
def start = System.currentTimeMillis()
def userPromise = fetchUser('user-42')
def ordersPromise = fetchOrders('user-42')
def reviewsPromise = fetchReviews('user-42')
// "await" all results - .val blocks until ready
def user = userPromise.val
def orders = ordersPromise.val
def reviews = reviewsPromise.val
def elapsed = System.currentTimeMillis() - start
println "\n=== User Dashboard ==="
println "Name: ${user.name}"
println "Email: ${user.email}"
println "Orders: ${orders.size()} (total: \$${orders.sum { it.total }})"
println "Reviews: ${reviews.size()} (avg rating: ${reviews.sum { it.rating } / reviews.size()})"
println "\nLoaded in ${elapsed}ms (sequential would be ~370ms)"
// --- then/whenBound composition ---
println "\n=== Promise Composition ==="
def enrichedUser = task {
def u = userPromise.val
def o = ordersPromise.val
[name: u.name, orderCount: o.size(), totalSpent: o.sum { it.total }]
}
def result = enrichedUser.val
println "Enriched: ${result}"
Output
[async] Fetching user user-42... [async] Fetching orders for user-42... [async] Fetching reviews by user-42... === User Dashboard === Name: Nirranjan Email: nirranjan@example.com Orders: 3 (total: $399.48) Reviews: 2 (avg rating: 4.5) Loaded in 156ms (sequential would be ~370ms) === Promise Composition === Enriched: [name:Nirranjan, orderCount:3, totalSpent:399.48]
What happened here: GPars task { } returns a DataflowVariable that acts as a promise. We launched three “API calls” concurrently and then awaited all three results. Since the calls run in parallel, total time is the maximum of the three (~150ms) rather than the sum (~370ms). The .val call is Groovy’s “await” – it blocks the current thread until the promise resolves. The composition example shows how to create derived promises that depend on multiple upstream results. This pattern maps directly to JavaScript’s async/await or Kotlin’s coroutines, but uses dataflow semantics.
Example 11: Virtual Threads (Groovy 5 + Java 21+)
What we’re doing: Using Java 21 virtual threads from Groovy to handle massive concurrency with lightweight threads instead of pooled OS threads.
Example 11: Virtual Threads
import java.util.concurrent.*
// Check if virtual threads are available (Java 21+)
println "Java version: ${System.getProperty('java.version')}"
// --- Create virtual threads directly ---
def results = new ConcurrentLinkedQueue()
def vThreads = (1..10).collect { id ->
Thread.ofVirtual().name("vthread-${id}").start {
Thread.sleep(100) // Simulate I/O
results << "[${Thread.currentThread().name}] Task ${id} complete (virtual: ${Thread.currentThread().isVirtual()})"
}
}
vThreads*.join()
println "=== Virtual Thread Results ==="
results.sort().each { println " ${it}" }
// --- Virtual thread executor for massive concurrency ---
println "\n=== Massive Concurrency Test ==="
def executor = Executors.newVirtualThreadPerTaskExecutor()
def counter = new java.util.concurrent.atomic.AtomicInteger(0)
def start = System.currentTimeMillis()
def futures = (1..10_000).collect { id ->
executor.submit({
Thread.sleep(100) // Each "request" takes 100ms
counter.incrementAndGet()
return id
} as Callable)
}
// Wait for all 10,000 tasks
futures.each { it.get() }
def elapsed = System.currentTimeMillis() - start
println "Completed: ${counter.get()} tasks"
println "Time: ${elapsed}ms"
println "Throughput: ${String.format('%.0f', counter.get() / (elapsed / 1000.0))} tasks/sec"
println "With OS threads (100 pool), this would take ~${10_000 * 100 / 100 / 1000}s"
executor.shutdown()
// --- Structured concurrency pattern ---
println "\n=== Structured Virtual Thread Pattern ==="
def fetchData = { String name, long delay ->
Thread.sleep(delay)
return "${name}: loaded in ${delay}ms"
}
try (def scope = new StructuredTaskScope.ShutdownOnFailure()) {
def userTask = scope.fork { fetchData('User', 100) }
def orderTask = scope.fork { fetchData('Orders', 150) }
def profileTask = scope.fork { fetchData('Profile', 80) }
scope.join()
scope.throwIfFailed()
println " ${userTask.get()}"
println " ${orderTask.get()}"
println " ${profileTask.get()}"
}
Output
Java version: 21.0.2 === Virtual Thread Results === [vthread-1] Task 1 complete (virtual: true) [vthread-10] Task 10 complete (virtual: true) [vthread-2] Task 2 complete (virtual: true) [vthread-3] Task 3 complete (virtual: true) [vthread-4] Task 4 complete (virtual: true) [vthread-5] Task 5 complete (virtual: true) [vthread-6] Task 6 complete (virtual: true) [vthread-7] Task 7 complete (virtual: true) [vthread-8] Task 8 complete (virtual: true) [vthread-9] Task 9 complete (virtual: true) === Massive Concurrency Test === Completed: 10000 tasks Time: 312ms Throughput: 32051 tasks/sec With OS threads (100 pool), this would take ~10s === Structured Virtual Thread Pattern === User: loaded in 100ms Orders: loaded in 150ms Profile: loaded in 80ms
What happened here: Virtual threads (Project Loom, Java 21+) are lightweight threads managed by the JVM, not the OS. You can create millions of them without running out of memory. Thread.ofVirtual() creates a virtual thread builder, and Executors.newVirtualThreadPerTaskExecutor() creates a new virtual thread for every submitted task. The massive concurrency test shows 10,000 tasks each sleeping 100ms completing in ~300ms – because virtual threads are cheap and the JVM schedules them efficiently onto a small number of carrier threads. With a traditional 100-thread pool, this would take ~10 seconds. The structured concurrency example uses StructuredTaskScope to fork multiple subtasks and join them as a unit, with automatic cancellation if one fails. Groovy 5 on Java 21+ gets all of this for free.
Example 12: @Synchronized for Thread Safety
What we’re doing: Using Groovy’s @Synchronized annotation to protect critical sections without writing synchronized blocks manually.
Example 12: @Synchronized
import groovy.transform.Synchronized
class ThreadSafeCache {
private final Object lock = new Object()
private Map<String, Object> cache = [:]
private int hits = 0
private int misses = 0
@Synchronized('lock')
void put(String key, Object value) {
cache[key] = value
}
@Synchronized('lock')
Object get(String key) {
if (cache.containsKey(key)) {
hits++
return cache[key]
}
misses++
return null
}
@Synchronized('lock')
Map getStats() {
[size: cache.size(), hits: hits, misses: misses,
hitRate: hits + misses > 0 ? "${((hits / (hits + misses)) * 100).round(1)}%" : 'N/A']
}
@Synchronized('lock')
void clear() {
cache.clear()
hits = 0
misses = 0
}
}
def cache = new ThreadSafeCache()
// Pre-populate cache
(1..50).each { cache.put("key-${it}", "value-${it}") }
println "Cache loaded with ${cache.stats.size} entries\n"
// Simulate concurrent reads from multiple threads
def threads = (1..8).collect { threadId ->
Thread.start {
def random = new Random()
200.times {
def key = "key-${random.nextInt(70) + 1}" // Some keys won't exist
cache.get(key)
}
}
}
threads*.join()
def stats = cache.stats
println "=== Cache Statistics After 1600 Concurrent Reads ==="
println "Size: ${stats.size}"
println "Hits: ${stats.hits}"
println "Misses: ${stats.misses}"
println "Hit rate: ${stats.hitRate}"
// --- Without @Synchronized (demonstration of what goes wrong) ---
class UnsafeCounter {
int count = 0
void increment() { count++ } // NOT atomic!
}
class SafeCounter {
private final Object lock = new Object()
int count = 0
@Synchronized('lock')
void increment() { count++ }
}
def unsafe = new UnsafeCounter()
def safe = new SafeCounter()
def unsafeThreads = (1..10).collect { Thread.start { 10_000.times { unsafe.increment() } } }
def safeThreads = (1..10).collect { Thread.start { 10_000.times { safe.increment() } } }
unsafeThreads*.join()
safeThreads*.join()
println "\n=== Counter Test (10 threads x 10,000 increments) ==="
println "Unsafe counter: ${unsafe.count} (expected: 100000, lost: ${100000 - unsafe.count})"
println "Safe counter: ${safe.count} (expected: 100000)"
Output
Cache loaded with 50 entries === Cache Statistics After 1600 Concurrent Reads === Size: 50 Hits: 1143 Misses: 457 Hit rate: 71.4% === Counter Test (10 threads x 10,000 increments) === Unsafe counter: 87342 (expected: 100000, lost: 12658) Safe counter: 100000 (expected: 100000)
What happened here: @Synchronized is an AST transformation that wraps the method body in a synchronized(lock) block. Unlike Java’s synchronized keyword on methods (which locks on this), Groovy’s @Synchronized uses a private lock object – safer because external code cannot interfere by locking on the same object. The cache example shows thread-safe reads and writes without explicit synchronization. The counter comparison demonstrates the real danger: without synchronization, count++ is not atomic – it reads, increments, and writes in three steps, and concurrent threads can interleave these steps, losing updates. The unsafe counter loses thousands of increments; the safe counter is exactly correct.
Example 13: Combining Patterns – Parallel Web Scraper
What we’re doing: Building a realistic parallel URL processor that combines GPars parallel collections, agents for shared state, and error handling.
Example 13: Parallel Web Scraper
@Grab('org.codehaus.gpars:gpars:1.2.1')
import groovyx.gpars.GParsPool
import groovyx.gpars.agent.Agent
// Simulate URL fetching (replace with real HTTP calls in production)
def fetchUrl = { String url ->
def random = new Random()
Thread.sleep(random.nextInt(200) + 50) // Simulate network latency
// Simulate occasional failures
if (random.nextInt(10) == 0) {
throw new IOException("Connection timeout: ${url}")
}
def size = random.nextInt(50000) + 1000
return [url: url, status: 200, size: size, thread: Thread.currentThread().name]
}
// Thread-safe stats collection using Agent
def stats = new Agent([
success : 0,
failed : 0,
totalSize: 0L,
errors : [],
results : []
])
def urls = (1..20).collect { "https://example.com/page-${it}" }
println "=== Parallel URL Processor ==="
println "Processing ${urls.size()} URLs with 6 threads...\n"
def start = System.currentTimeMillis()
GParsPool.withPool(6) {
urls.eachParallel { url ->
try {
def result = fetchUrl(url)
stats << { oldVal ->
updateValue(oldVal + [
success : oldVal.success + 1,
totalSize: oldVal.totalSize + result.size,
results : oldVal.results + [result]
])
}
} catch (Exception e) {
stats << { oldVal ->
updateValue(oldVal + [
failed: oldVal.failed + 1,
errors: oldVal.errors + [e.message]
])
}
}
}
}
// Wait for agent to process all updates
Thread.sleep(300)
def elapsed = System.currentTimeMillis() - start
def finalStats = stats.val
println "=== Results ==="
println "Successful: ${finalStats.success}"
println "Failed: ${finalStats.failed}"
println "Total size: ${String.format('%,d', finalStats.totalSize)} bytes"
println "Time elapsed: ${elapsed}ms"
println "Throughput: ${String.format('%.1f', urls.size() / (elapsed / 1000.0))} URLs/sec"
if (finalStats.errors) {
println "\nErrors:"
finalStats.errors.each { println " - ${it}" }
}
println "\nSample results (first 5):"
finalStats.results.take(5).each { r ->
println " ${r.url.padRight(35)} | ${r.status} | ${String.format('%,d', r.size).padLeft(6)} bytes | ${r.thread}"
}
Output
=== Parallel URL Processor === Processing 20 URLs with 6 threads... === Results === Successful: 18 Failed: 2 Total size: 478,234 bytes Time elapsed: 423ms Throughput: 47.3 URLs/sec Errors: - Connection timeout: https://example.com/page-7 - Connection timeout: https://example.com/page-15 Sample results (first 5): https://example.com/page-1 | 200 | 23,456 bytes | ForkJoinPool-1-worker-1 https://example.com/page-2 | 200 | 41,023 bytes | ForkJoinPool-1-worker-2 https://example.com/page-3 | 200 | 12,890 bytes | ForkJoinPool-1-worker-3 https://example.com/page-4 | 200 | 8,234 bytes | ForkJoinPool-1-worker-4 https://example.com/page-5 | 200 | 35,678 bytes | ForkJoinPool-1-worker-5
What happened here: This combines three GPars concepts: eachParallel for data parallelism (6 threads processing 20 URLs), an Agent for thread-safe statistics collection, and error handling per task. The agent ensures that success/failure counters and result lists are updated atomically – no lost updates, no corrupted state. Error handling is per-URL: a failed fetch doesn’t crash the entire pipeline, it just records the error and continues. This is a production-ready pattern for any bulk I/O operation: API polling, file processing, database migrations, or actual web scraping.
Common Pitfalls
Pitfall 1: Shared Mutable State in Parallel Collections
The most common concurrency bug: using a regular ArrayList or HashMap from parallel threads without synchronization.
Pitfall: Shared Mutable State
// BAD - ArrayList is not thread-safe
def unsafeList = []
GParsPool.withPool {
(1..1000).eachParallel { unsafeList << it } // ConcurrentModificationException or lost elements
}
println "Unsafe: ${unsafeList.size()} (expected 1000)" // Often less than 1000
// GOOD - use thread-safe collections
def safeList = Collections.synchronizedList([])
GParsPool.withPool {
(1..1000).eachParallel { safeList << it }
}
println "Safe: ${safeList.size()} (expected 1000)" // Always 1000
// BETTER - use collectParallel (no shared state needed)
GParsPool.withPool {
def results = (1..1000).collectParallel { it * 2 }
println "Best: ${results.size()} (expected 1000)" // Always 1000
}
Pitfall 2: Deadlock with Dataflow Variables
If two dataflow variables depend on each other, you get a deadlock – both tasks wait forever for the other’s value.
Pitfall: Dataflow Deadlock
// BAD - circular dependency = deadlock
def a = new DataflowVariable()
def b = new DataflowVariable()
task { a << b.val + 1 } // a waits for b
task { b << a.val + 1 } // b waits for a - DEADLOCK!
// GOOD - ensure acyclic dependencies
def x = new DataflowVariable()
def y = new DataflowVariable()
def z = new DataflowVariable()
task { x << 10 } // x has no dependencies
task { y << x.val * 2 } // y depends on x
task { z << x.val + y.val } // z depends on x and y
println "z = ${z.val}" // 30
Pitfall 3: Forgetting to Shut Down Thread Pools
Failing to shut down an ExecutorService keeps the JVM alive indefinitely. GPars withPool handles this automatically, but manual pools need explicit cleanup.
Pitfall: Thread Pool Cleanup
// BAD - pool never shut down, JVM hangs
def pool = Executors.newFixedThreadPool(4)
pool.submit { println "task done" }
// Script never exits!
// GOOD - always shut down in a finally block
def pool = Executors.newFixedThreadPool(4)
try {
pool.submit { println "task done" }.get()
} finally {
pool.shutdown()
pool.awaitTermination(5, TimeUnit.SECONDS)
}
// BEST - use GParsPool.withPool which handles cleanup
GParsPool.withPool(4) {
// Pool is automatically shut down when this block exits
(1..10).eachParallel { println "task ${it}" }
}
Conclusion
Concurrency in Groovy spans a wide spectrum of abstractions, from low-level threads to high-level actors and dataflow. For most tasks, GPars parallel collections (collectParallel, eachParallel) are all you need – they turn sequential code into parallel code by changing one method name. For complex coordination, dataflow variables handle dependencies automatically, and actors provide a message-passing model that eliminates shared state entirely. Agents give you thread-safe mutable state when you genuinely need it.
Groovy 5 on Java 21+ adds virtual threads, which change the performance equation for I/O-bound workloads. Where you once needed async frameworks and callback chains, you can now write simple blocking code with virtual threads and get the same throughput. Combined with GPars for CPU-bound parallelism, Groovy has a complete concurrency toolkit that covers every use case from data processing to actor systems.
For related topics, explore Design Patterns in Groovy for patterns that complement concurrency, Groovy Closures for the foundation of GPars callbacks, and Groovy Metaprogramming for understanding how GPars adds parallel methods to collections at runtime.
Best Practices
- DO use
GParsPool.withPoolfor data-parallel operations – it handles thread pool lifecycle and adds parallel methods transparently. - DO prefer
collectParallelovereachParallelwith shared state – collecting results is inherently safer than mutating shared collections. - DO use Agents for thread-safe shared state – they process updates sequentially without explicit locks.
- DO use dataflow variables for dependent async tasks – they resolve dependencies automatically and deadlock only on circular dependencies (which are always a logic error).
- DO consider virtual threads (Java 21+) for I/O-bound workloads with high concurrency needs.
- DON’T share mutable state between parallel tasks without synchronization – use
Collections.synchronizedList,ConcurrentHashMap, Agents, or@Synchronized. - DON’T forget to shut down manually created thread pools – use
try/finallyor GPars’withPoolwhich handles cleanup automatically. - DON’T create circular dependencies between dataflow variables – they cause deadlocks that are silent and hard to debug.
- DON’T assume
@Singletonmakes internal state thread-safe – it only guarantees one instance. Protect mutable fields with@Synchronizedor atomic types.
Frequently Asked Questions
What is GPars and why should I use it in Groovy?
GPars (Groovy Parallel Systems) is a concurrency library that provides high-level abstractions for parallel programming in Groovy. It includes parallel collections (eachParallel, collectParallel), Actors for message-passing concurrency, Dataflow variables for dependency-driven execution, and Agents for thread-safe mutable state. You should use it because it eliminates the boilerplate of Java’s thread management, handles synchronization correctly, and lets you parallelize code by changing a single method name rather than rewriting your logic.
How do I parallelize a list operation in Groovy?
Use GParsPool.withPool(numThreads) { ... } and replace .each with .eachParallel or .collect with .collectParallel. For example: GParsPool.withPool(4) { myList.collectParallel { expensiveOperation(it) } }. The pool handles thread creation, work distribution, and cleanup automatically. Prefer collectParallel over eachParallel when you need results, as it avoids shared mutable state entirely.
What are GPars Actors and when should I use them?
GPars Actors are independent concurrent entities that communicate exclusively through message passing – no shared state. Each actor processes one message at a time in its own thread, eliminating race conditions inside the actor body. Use actors when you have components that need to communicate concurrently but should not share memory: worker pools, event processors, pipeline stages, or any system where the Erlang/Akka actor model fits. Actors are overkill for simple data parallelism – use parallel collections for that.
What are virtual threads and how do I use them in Groovy?
Virtual threads (Java 21+, Project Loom) are lightweight threads managed by the JVM instead of the operating system. You can create millions of them without exhausting memory. In Groovy 5 on Java 21+, create them with Thread.ofVirtual().start { ... } or use Executors.newVirtualThreadPerTaskExecutor() for a thread-per-task executor. They are ideal for I/O-bound workloads like HTTP clients, database queries, and file processing where you need high concurrency but each task spends most of its time waiting.
How does @Synchronized differ from Java’s synchronized keyword?
Groovy’s @Synchronized annotation locks on a private object (private final Object $lock = new Object[0]()) by default, while Java’s synchronized method modifier locks on this (or the Class object for static methods). Locking on this is risky because external code can also synchronize on the same object, causing unexpected contention or deadlocks. @Synchronized avoids this by using an internal lock. You can also specify a named lock: @Synchronized('myLock') with private final Object myLock = new Object().
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