A Lunch Break Revelation: When My friend Jimmy Explained Garbage Collection over lunch

Tuesday, 2:30 PM — Company Cafeteria

I’m standing in line with my tray, watching the chaos unfold in our company cafeteria. The lunch rush is in full swing, but suddenly everything comes to a halt. The cleaning crew has swooped in with their carts and spray bottles, and now we’re all stuck waiting while they sanitize the tables and food prep areas.

“Seriously?” I mutter, checking my watch. “We only have a 30-minute lunch break.”

My friend Jimmy, who’s been unusually quiet, suddenly perks up with that familiar gleam in his eyes. “You know what, buddy? This whole scene reminds me of Java Garbage Collection.”

I turn to him, raising an eyebrow. “How on earth does a messy cafeteria remind you of programming?”

Jimmy grins, clearly excited to dive into one of his tech analogies. “Oh man, you’re about to see why this is actually the perfect example! Grab your sandwich and let me blow your mind.”

The Revelation Begins

We find a spot near the window, and Jimmy starts gesturing toward the cafeteria like he’s presenting some grand revelation.

“Look around,” he says, unwrapping his sandwich. “What do you see?”

“Annoyed coworkers waiting for lunch?” I reply, not sure where this is going.

“Exactly! But think bigger. This cafeteria is like a Java application’s memory, and what just happened? We had a garbage collection pause!”

I lean back in my chair. “Okay, I’m listening. But start from the beginning — what even is garbage collection?”

Jimmy’s Memory Management 101

“Alright,” Jimmy says, pulling out his phone to sketch on a napkin. “Imagine our office is a Java application, and this cafeteria is its memory. Every time someone eats lunch here, they’re like Java objects — they take up space, use resources, and eventually, they finish and leave behind trash.”

He draws a rough layout of our cafeteria:

Press enter or click to view image in full size

“See, in Java memory, we have similar areas:

• Food Counter (Eden Space): Where new objects are ‘served up’

• Temporary Storage (Survivor Spaces): Where objects hang out briefly

• Dining Tables (Old Generation): Where long-lived objects settle in”

I’m starting to see it. “And the garbage?”

“The garbage is all the stuff people leave behind — empty plates, napkins, food scraps. In Java terms, these are objects that are no longer being used by the application.”

The Cleaning Crew Types

Just then, another cleaning crew member walks by, and Jimmy’s eyes light up again.

“And here’s where it gets interesting! Just like our office has different cleaning approaches, Java has different garbage collectors — different ‘cleaning crews’ with their own personalities.”

“Different personalities?”

“Oh yeah! Let me introduce you to the crew…”

Meet the Serial Cleaner

“First up,” Jimmy says, pointing to the solo janitor mopping in the corner, “is what we call the Serial Garbage Collector. See that guy working alone?”

I nod, watching the methodical cleaner moving from table to table.

“That’s exactly how Serial GC works. When cleanup time comes:

• Everyone stops eating (application pauses)

• One cleaner does everything methodically

• Takes longer, but uses minimal resources

• Perfect for small cafeterias”

He mimics the cleaner: “I work alone, I’m thorough, and yes, everyone has to wait while I clean every single table.”

“In Java code, you’d ‘hire’ this cleaner like this:”

XX:+UseSerialGC # "I want the solo, methodical cleaner"

“When would you want this?”

• Small office (applications under 100MB)

• Home offices (desktop apps)

• When you only have one person working (single-core machines)

The Cleaning Team

Suddenly, a whole crew of cleaners appears, working in coordination to clean multiple areas simultaneously.

“And THAT,” Jimmy says excitedly, “is the Parallel Garbage Collector in action!”

I watch as four cleaners efficiently divide the work — one handles tables, another manages the food area, while two others handle trash and sanitizing.

“See how they work together? Same concept:

• Still stops all eating when they clean

• But multiple cleaners work simultaneously

• Much faster than the solo guy

• Great for busy cafeterias with lots of throughput”

Jimmy imitates a team leader: “Alright team, everyone stop eating! We’ll clean this place in 30 seconds instead of 2 minutes!”

“The Java configuration:”

-XX:+UseParallelGC
-XX:ParallelGCThreads=4 # "Hire 4 cleaners to work together"
-XX:MaxGCPauseMillis=200 # "Don't stop eating for more than 200ms"

“Perfect for:”

• Medium to large cafeterias (2–32GB applications)

• Corporate dining halls focused on serving lots of people (batch processing)

• When you have multiple staff available (multi-core servers)

The Zone Manager

“But wait,” I interrupt, “what about that supervisor over there who seems to be cleaning specific sections while people are still eating in other areas?”

Jimmy practically jumps out of his seat. “YES! You’ve spotted the G1 Garbage Collector in action!”

I look more carefully and notice that while some tables are being cleaned, people in other sections are still eating undisturbed.

“G1 is brilliant,” Jimmy explains. “Instead of treating the cafeteria as fixed areas, it divides everything into small, flexible zones.”

He redraws the cafeteria layout:

Press enter or click to view image in full size

“Each zone can change purpose as needed:

• Eating Zones: Active dining areas (Eden regions)

• Cleaning Zones: Being sanitized right now (collection in progress)

• Storage Zones: Long-term reserved tables (Old regions)

• Big Table: For large groups that need extra space (Humongous objects)

• Free Zones: Available for any purpose”

“The genius part? The zone manager can say: ‘I see zones 3 and 8 are really messy. I’ll clean just those two while everyone else keeps eating.’”

“Configuration:”

-XX:+UseG1GC
-XX:MaxGCPauseMillis=10 # "Never stop eating for more than 10ms"
-XX:G1HeapRegionSize=16m # "Make each zone this big

“Best for:”

• Large cafeterias (>4GB applications)

• Restaurants where service can’t stop (web servers)

• When consistency matters more than raw speed

The Ultimate Cleaning Ninjas

“But what about,” I ask, getting into the spirit of things, “that person who seems to clean so efficiently that I barely notice any interruption at all?”

Jimmy looks around and spots a cleaner who’s somehow managing to sanitize tables while people are still using them, with incredible precision and speed.

“THAT,” he says with reverence, “is like ZGC — the Ultra-Fast Specialist!”

The Lightning-Fast Ninja (ZGC)

“Watch closely,” Jimmy whispers as we observe this almost magical cleaner who seems to clean around people without them even noticing.

“ZGC is like having a cleaning ninja:

• Cleans while people are still eating

• Pauses are so short you don’t even notice

• Can handle massive cafeterias (up to 16TB!)

• Uses advanced techniques that seem like magic”

I watch in amazement as this person somehow manages to sanitize a table while someone is still sitting there, without interrupting their meal.

“In our cafeteria scenario, it’s like they have special tools and techniques:

• Spray that dries instantly

• Ability to clean sections of a table while you’re using other parts

• So fast and efficient that eating never really stops”

“Java setup:”

-XX:+UseZGC
-XX:+UnlockExperimentalVMOptions # "I want the ninja-level cleaning tech"

“Perfect for:”

• Massive corporate campuses (>32GB applications)

• High-end restaurants where any delay loses customers

• Trading floor cafeterias where microseconds matter (financial systems)

• Gaming company lunches where lag kills morale

The Multitasking Master (Shenandoah)

“And see that other person?” Jimmy points to another cleaner who’s somehow managing to work continuously around diners, never really stopping anyone from eating.

I squint at both cleaners. “Wait, they both look like they’re doing the same thing. What’s the difference?”

Jimmy grins. “Ah! Great eye! They’re both ninja-level, but watch more carefully…”

The ZGC Ninja Approach: “ZGC is like a cleaner with magic cleaning solution. See how they spray the table and it’s instantly clean? They use special ‘colored pointers’ — imagine they have a magic marker that can instantly tell them ‘this plate is dirty’ or ‘this plate is clean’ without even touching it.”

The Shenandoah Approach: “Shenandoah is like a cleaner with incredible coordination skills. Watch — they’re using ‘forwarding notes.’ When they need to move your plate to clean under it, they leave a little note saying ‘your plate is now over there’ so you can keep eating while they clean your original spot.”

“Here’s the key difference:

ZGC:

• Uses advanced ‘memory coloring’ (like magic markers on objects)

• Can handle MASSIVE cafeterias (up to 16TB!)

• Slightly higher resource usage but incredible speed

• Better for enormous applications

Shenandoah:

• Uses ‘forwarding pointers’ (like leaving notes where things moved)

• Excellent for large cafeterias but not as massive as ZGC

• More memory-efficient than ZGC

• Better for containerized environments (like food trucks vs full restaurants)”

I nod, starting to see it. “So ZGC is for when you have unlimited resources and need maximum performance, while Shenandoah is for when you want great performance but need to be more resource-conscious?”

“Exactly!” Jimmy says. “Think of it this way:

• ZGC: The premium cleaning service with all the latest tech — costs more but handles anything

• Shenandoah: The highly skilled artisan cleaner — incredibly efficient and works great in most situations”

“Configuration:”

-XX:+UseShenandoahGC
-XX:+UnlockExperimentalVMOptions
# ZGC for comparison:
-XX:+UseZGC
-XX:+UnlockExperimentalVMOptions

ZGC vs Shenandoah: The Ninja Showdown

“Wait,” I interrupt, “I’m still confused about when I’d choose the magic marker ninja (ZGC) versus the coordination master (Shenandoah).”

Jimmy pulls out his napkin again and draws a comparison:

Press enter or click to view image in full size

“Think of it like this:

Choose ZGC when:

• You’re running a massive corporate campus cafeteria (very large heaps)

• Budget isn’t a concern, you want the absolute best

• You’re dealing with financial trading floors where nanoseconds matter

• You have dedicated servers with lots of resources

Choose Shenandoah when:

• You’re running multiple smaller restaurant locations (containerized apps)

• You want great performance but need to watch resource costs

• You’re in a cloud environment where you pay for what you use

• You want consistent, predictable performance across different workloads”

“The technical difference:

• ZGC: Uses colored pointers — marks objects with special bits to track their state

• Shenandoah: Uses forwarding pointers — leaves ‘breadcrumbs’ when moving objects around”

The Decision Framework

“Okay, now I’m getting it,” I say. “So how would I choose which ‘cleaner’ to hire for my Java application?”

Jimmy pulls out his phone and sketches a decision tree:

What size is your cafeteria (application)?

├── Small Cafe (<2GB)

│ └── Solo Cleaner (Serial GC) ✅

├── Medium Restaurant (2–32GB)

│ ├── Need maximum serving speed? → Cleaning Team (Parallel GC) ✅

│ └── Need consistent service? → Zone Manager (G1GC) ✅

├── Large Food Court (>32GB)

│ ├── Ultra-fast service required? → Cleaning Ninja (ZGC) ✅

│ ├── Consistent low interruptions? → Multitasker (Shenandoah) ✅

│ └── Good balance of everything? → Zone Manager (G1GC) ✅

Understanding the Cleaning Reports

Just then, I notice a supervisor with a clipboard making notes about the cleaning process.

“Perfect timing!” Jimmy says. “That’s like garbage collection logging — the cleaning reports that tell you how well your ‘cleaner’ is performing.”

“You can enable these reports in Java:”

# Modern Java (9+): "Please document all cleaning activities"
-Xlog:gc:gc.log:time,tags
# Older Java: "Keep detailed cleaning records"
-XX:+PrintGC
-XX:+PrintGCDetails
-XX:+PrintGCTimeStamps
-Xloggc:gc.log

Reading the Cleaning Reports

Jimmy shows me his phone screen with a sample log entry:

[2023–08–28T12:30:15.123+0000][gc] GC(12) Pause Young (Normal)

(G1 Evacuation Pause) 245M->89M(512M) 4.123ms

“Let me translate this cleaning report:

12:30:15*: ‘At exactly 12:30 and 15 seconds…’*

GC(12)*: ‘This was the 12th cleaning session today’*

Pause Young*: ‘We focused on cleaning the food counter area’*

245M->89M*: ‘Cafeteria went from 245MB worth of stuff to 89MB (cleared out 156MB of trash!)’*

(512M)*: ‘Total cafeteria capacity is 512MB’*

4.123ms*: ‘We only stopped food service for 4.123 milliseconds’”*

What Makes Good vs. Bad Reports

“Here’s how to read the signs,” Jimmy continues:

🟢 Excellent Performance

“Cafeteria went from 80% full to 20% full in 5ms”

↳ Amazing! Lots of trash cleared super quickly

🟡 Concerning Signs

“Cafeteria went from 85% full to 82% full in 50ms”

↳ Warning! Not much trash found, but took a while to clean

🔴 Major Problems

“Cafeteria went from 95% full to 94% full in 500ms”

↳ Crisis! Almost no space freed, very long service interruption

Troubleshooting Common Problems

“What if my cleaning service isn’t working well?” I ask.

Problem 1: “Cleaning Takes Forever!”

Jimmy puts on his troubleshooting hat.

Symptoms:

• Long service interruptions (>100ms)

• Customers complaining about wait times

• Food getting cold while waiting”

Solutions:

# Option 1: Hire a faster cleaning service

-XX:+UseG1GC # Switch from team cleaning to zone management
-XX:MaxGCPauseMillis=50 # 'Never interrupt service for more than 50ms'
# Option 2: Get the ninja cleaner
-XX:+UseZGC # Ultimate speed, barely noticeable interruptions

Problem 2: “Cafeteria Always Full!”

Symptoms:

• Space usage stays above 80% even after cleaning

• Frequent full-cafeteria deep cleans

• Eventually run out of space completely

Solutions:

# Option 1: Get a bigger cafeteria
-Xmx8g # Increase from 4GB to 8GB
# Option 2: Clean the food counter more often
-XX:NewRatio=1 # Give more space to the food prep area
# Option 3: Fix the root cause
# Maybe people are leaving too much stuff behind (memory leaks)

Problem 3: “Too Much Trash Generated!”

Symptoms

• Cleaning crew working constantly

• High mess-generation rate

• Cleaners can barely keep up”

Solutions:

# Give more space for quick dining (short-lived objects)
-Xmn2g # 2GB for food counter area
-XX:SurvivorRatio=8 # Bigger serving area
# Consider changing dining habits:
# - Encourage people to clean up after themselves (object pooling)
# - Use reusable plates instead of disposables (StringBuilder vs String)
# - Don't grab extra napkins you won't use (avoid unnecessary objects)

The Real-World Impact

As we’re packing up our lunch, I ask, “But does this really matter that much in the real world?”

Jimmy’s expression gets serious. “Oh, absolutely”. Let me tell you some stories:

The $440 Million Lunch Break

In 2012, a trading company had a system with poorly tuned garbage collection. The GC pauses caused delays in their trading algorithms, and in just 45 minutes, they lost $440 million. Imagine if our cafeteria cleaning delays caused the company to lose that much money!”

The Frustrated Gamer’s Lunch

“Ever had lag spikes in online games right when you’re about to win? Often, that’s garbage collection pauses. It’s like being in the middle of an important phone call and having the cafeteria cleaning crew interrupt you mid-sentence.”

The Slow Shopping Experience

“Amazon discovered that every 100ms of delay costs them 1% in sales. If our cafeteria took an extra 100ms to serve each customer, we’d lose business. Same principle applies to web applications.”

The Success Story

As we head back to our desks, Jimmy summarizes: “The right garbage collection strategy is like having the perfect cleaning service for your cafeteria. Get it right, and:

• Happy customers (users don’t experience delays)

• Efficient operations (your application runs smoothly)

• Lower costs (better use of resources)

• Peaceful sleep (fewer 3 AM production alerts)”

“The journey from ‘What’s garbage collection?’ to ‘I optimized our GC for 20% better performance’ is like going from someone who just eats in the cafeteria to someone who understands how to make the whole dining experience better for everyone ✨.”

Conclusion

As I sit down at my computer, I can’t help but smile. What started as an annoying cafeteria cleaning delay turned into the most practical programming lesson I’ve had in months.

And now, every time I see our office cleaning crew in action, I’ll think about Java garbage collectors. Who knew a Tuesday lunch break could be so educational?

“Thanks, Jimmy,” I think to myself, “for showing me that the best way to understand complex technical concepts is to see them in the everyday world around us.”

Resources

https://sematext.com/blog/java-garbage-collection-logs

https://www.infoq.com/articles/Visualizing-Java-Garbage-Collection