Markus Breuer
@mbreuer.bsky.social
🚀 Software Architect | Open Source | IT Blogger
📖 Tech challenges & software stories
📝 Blog: bytefusion.de | ✍️ Medium: medium.com/@msbreuer
📷 Pixelfed: @mbreuer@pixelfed | 🌍 Mastodon: @[email protected]
📖 Tech challenges & software stories
📝 Blog: bytefusion.de | ✍️ Medium: medium.com/@msbreuer
📷 Pixelfed: @mbreuer@pixelfed | 🌍 Mastodon: @[email protected]
𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗱𝗼𝗰𝘀 𝗼𝗳𝘁𝗲𝗻 𝗳𝗮𝗶𝗹 𝗹𝗼𝗻𝗴 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗼𝗻𝘁𝗲𝗻𝘁: 𝘁𝗵𝗲𝘆 𝗴𝗲𝘁 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝘁𝗵𝗲 𝗺𝗲𝗱𝗶𝘂𝗺.
📒 Wiki → easy but a graveyard without rules
📂 SharePoint → versioning, but weak vs. SCM
📝 Git Markdown → great for devs, tough for PMs
📄 PDF/Word → shareable, but outdated fast
📊 Diagram tools → powerful, but niche
No pe
📒 Wiki → easy but a graveyard without rules
📂 SharePoint → versioning, but weak vs. SCM
📝 Git Markdown → great for devs, tough for PMs
📄 PDF/Word → shareable, but outdated fast
📊 Diagram tools → powerful, but niche
No pe
November 11, 2025 at 10:31 AM
𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗱𝗼𝗰𝘀 𝗼𝗳𝘁𝗲𝗻 𝗳𝗮𝗶𝗹 𝗹𝗼𝗻𝗴 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗼𝗻𝘁𝗲𝗻𝘁: 𝘁𝗵𝗲𝘆 𝗴𝗲𝘁 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝘁𝗵𝗲 𝗺𝗲𝗱𝗶𝘂𝗺.
📒 Wiki → easy but a graveyard without rules
📂 SharePoint → versioning, but weak vs. SCM
📝 Git Markdown → great for devs, tough for PMs
📄 PDF/Word → shareable, but outdated fast
📊 Diagram tools → powerful, but niche
No pe
📒 Wiki → easy but a graveyard without rules
📂 SharePoint → versioning, but weak vs. SCM
📝 Git Markdown → great for devs, tough for PMs
📄 PDF/Word → shareable, but outdated fast
📊 Diagram tools → powerful, but niche
No pe
𝗜𝗦𝗢 𝟮𝟱𝟬𝟭𝟬 𝗮𝗽𝗽𝗹𝗶𝗲𝘀 𝘁𝗼 𝗹𝗼𝗴 𝘀𝗵𝗶𝗽𝗽𝗲𝗿𝘀 𝘁𝗼𝗼 🚢
– 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: use less CPU so business logic isn’t slowed down
– 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: logs can arrive later, e.g. after traffic peaks
– 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: encrypt transport, protect sensitive data
– 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: easy config, painless upgrades
Quality isn’t just for product
– 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: use less CPU so business logic isn’t slowed down
– 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: logs can arrive later, e.g. after traffic peaks
– 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: encrypt transport, protect sensitive data
– 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: easy config, painless upgrades
Quality isn’t just for product
November 6, 2025 at 12:01 PM
𝗜𝗦𝗢 𝟮𝟱𝟬𝟭𝟬 𝗮𝗽𝗽𝗹𝗶𝗲𝘀 𝘁𝗼 𝗹𝗼𝗴 𝘀𝗵𝗶𝗽𝗽𝗲𝗿𝘀 𝘁𝗼𝗼 🚢
– 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: use less CPU so business logic isn’t slowed down
– 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: logs can arrive later, e.g. after traffic peaks
– 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: encrypt transport, protect sensitive data
– 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: easy config, painless upgrades
Quality isn’t just for product
– 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: use less CPU so business logic isn’t slowed down
– 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: logs can arrive later, e.g. after traffic peaks
– 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: encrypt transport, protect sensitive data
– 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: easy config, painless upgrades
Quality isn’t just for product
Kubernetes requests/limits decide pod placement.
𝗧𝗼𝗼 𝘀𝗺𝗮𝗹𝗹 → overbooked nodes.
𝗧𝗼𝗼 𝗯𝗶𝗴 → wasted resources.
𝗡𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 = tiny defaults, risking instability.
Right-sizing ensures fair scheduling & efficient clusters.
𝗧𝗼𝗼 𝘀𝗺𝗮𝗹𝗹 → overbooked nodes.
𝗧𝗼𝗼 𝗯𝗶𝗴 → wasted resources.
𝗡𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 = tiny defaults, risking instability.
Right-sizing ensures fair scheduling & efficient clusters.
November 4, 2025 at 10:30 AM
Kubernetes requests/limits decide pod placement.
𝗧𝗼𝗼 𝘀𝗺𝗮𝗹𝗹 → overbooked nodes.
𝗧𝗼𝗼 𝗯𝗶𝗴 → wasted resources.
𝗡𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 = tiny defaults, risking instability.
Right-sizing ensures fair scheduling & efficient clusters.
𝗧𝗼𝗼 𝘀𝗺𝗮𝗹𝗹 → overbooked nodes.
𝗧𝗼𝗼 𝗯𝗶𝗴 → wasted resources.
𝗡𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 = tiny defaults, risking instability.
Right-sizing ensures fair scheduling & efficient clusters.
🚀 𝗧𝗮𝗶𝗹𝗶𝗻𝗴 𝗹𝗼𝗴𝗳𝗶𝗹𝗲𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗹𝗼𝗴𝗴𝗶𝗻𝗴 𝘀𝘁𝗮𝗰𝗸 = 𝗯𝗶𝗴 𝘄𝗶𝗻𝘀:
🗂 acts like a queue (resume later),
⚡ bulk writes > single updates,
🔄 data can be re-processed,
🛡️ resilient to outages.
Simple, robust, efficient logging.
🗂 acts like a queue (resume later),
⚡ bulk writes > single updates,
🔄 data can be re-processed,
🛡️ resilient to outages.
Simple, robust, efficient logging.
October 30, 2025 at 12:00 PM
🚀 𝗧𝗮𝗶𝗹𝗶𝗻𝗴 𝗹𝗼𝗴𝗳𝗶𝗹𝗲𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗹𝗼𝗴𝗴𝗶𝗻𝗴 𝘀𝘁𝗮𝗰𝗸 = 𝗯𝗶𝗴 𝘄𝗶𝗻𝘀:
🗂 acts like a queue (resume later),
⚡ bulk writes > single updates,
🔄 data can be re-processed,
🛡️ resilient to outages.
Simple, robust, efficient logging.
🗂 acts like a queue (resume later),
⚡ bulk writes > single updates,
🔄 data can be re-processed,
🛡️ resilient to outages.
Simple, robust, efficient logging.
🧵 𝗝𝗮𝘃𝗮 𝗢𝗢𝗠 𝗵𝗮𝘀 𝗳𝗹𝗮𝘃𝗼𝗿𝘀:
Heap space → objects don’t fit.
Non-heap → stacks, threads, metaspace, direct buffers.
OS OOM → kernel kills JVM when RAM is gone.
👉 Not all OOMs are equal.
Heap space → objects don’t fit.
Non-heap → stacks, threads, metaspace, direct buffers.
OS OOM → kernel kills JVM when RAM is gone.
👉 Not all OOMs are equal.
October 28, 2025 at 10:30 AM
🧵 𝗝𝗮𝘃𝗮 𝗢𝗢𝗠 𝗵𝗮𝘀 𝗳𝗹𝗮𝘃𝗼𝗿𝘀:
Heap space → objects don’t fit.
Non-heap → stacks, threads, metaspace, direct buffers.
OS OOM → kernel kills JVM when RAM is gone.
👉 Not all OOMs are equal.
Heap space → objects don’t fit.
Non-heap → stacks, threads, metaspace, direct buffers.
OS OOM → kernel kills JVM when RAM is gone.
👉 Not all OOMs are equal.
🚦 Dreyfus model:
👉 Beginners need rules — everything feels equally important.
🎯 Experts act intuitively — they focus on what matters and ignore the rest.
From rules to pattern recognition: that’s the path to real expertise. ✨
👉 Beginners need rules — everything feels equally important.
🎯 Experts act intuitively — they focus on what matters and ignore the rest.
From rules to pattern recognition: that’s the path to real expertise. ✨
October 23, 2025 at 11:00 AM
🚦 Dreyfus model:
👉 Beginners need rules — everything feels equally important.
🎯 Experts act intuitively — they focus on what matters and ignore the rest.
From rules to pattern recognition: that’s the path to real expertise. ✨
👉 Beginners need rules — everything feels equally important.
🎯 Experts act intuitively — they focus on what matters and ignore the rest.
From rules to pattern recognition: that’s the path to real expertise. ✨
⏱️ 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝘁𝗶𝗺𝗲𝗼𝘂𝘁 = 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗶𝘀𝘀𝘂𝗲?
Not always! Timeouts are often just a symptom:
- overprovisioned hosts 🖥️
- Kubernetes limits ⚙️
- Java garbage collection ♻️
…or all of them combined.
The root cause usually lies deeper — not just “the network.” 🚨
Not always! Timeouts are often just a symptom:
- overprovisioned hosts 🖥️
- Kubernetes limits ⚙️
- Java garbage collection ♻️
…or all of them combined.
The root cause usually lies deeper — not just “the network.” 🚨
October 21, 2025 at 9:30 AM
⏱️ 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝘁𝗶𝗺𝗲𝗼𝘂𝘁 = 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗶𝘀𝘀𝘂𝗲?
Not always! Timeouts are often just a symptom:
- overprovisioned hosts 🖥️
- Kubernetes limits ⚙️
- Java garbage collection ♻️
…or all of them combined.
The root cause usually lies deeper — not just “the network.” 🚨
Not always! Timeouts are often just a symptom:
- overprovisioned hosts 🖥️
- Kubernetes limits ⚙️
- Java garbage collection ♻️
…or all of them combined.
The root cause usually lies deeper — not just “the network.” 🚨
⏱️ 𝗧𝗶𝗺𝗲𝗼𝘂𝘁? 𝗗𝗼𝗻’𝘁 𝗽𝗮𝗻𝗶𝗰.
👉 Troubleshooting steps:
1️⃣ Check network (logs, policies, TCP)
2️⃣ Check platform (K8s limits, node metrics)
3️⃣ Check app (GC logs, thread dumps)
4️⃣ Correlate everything for the big picture
Only then you’ll uncover the real cause.
👉 Troubleshooting steps:
1️⃣ Check network (logs, policies, TCP)
2️⃣ Check platform (K8s limits, node metrics)
3️⃣ Check app (GC logs, thread dumps)
4️⃣ Correlate everything for the big picture
Only then you’ll uncover the real cause.
October 16, 2025 at 11:01 AM
⏱️ 𝗧𝗶𝗺𝗲𝗼𝘂𝘁? 𝗗𝗼𝗻’𝘁 𝗽𝗮𝗻𝗶𝗰.
👉 Troubleshooting steps:
1️⃣ Check network (logs, policies, TCP)
2️⃣ Check platform (K8s limits, node metrics)
3️⃣ Check app (GC logs, thread dumps)
4️⃣ Correlate everything for the big picture
Only then you’ll uncover the real cause.
👉 Troubleshooting steps:
1️⃣ Check network (logs, policies, TCP)
2️⃣ Check platform (K8s limits, node metrics)
3️⃣ Check app (GC logs, thread dumps)
4️⃣ Correlate everything for the big picture
Only then you’ll uncover the real cause.
𝗜𝗦𝗢 𝟮𝟱𝟬𝟭𝟬 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗹𝗼𝗴 𝘀𝗵𝗶𝗽𝗽𝗲𝗿𝘀 𝘁𝗼𝗼 🚢
– Efficiency: low CPU → business logic stays fast
– Reliability: delay logs after peaks
– Security: encrypt sensitive data
– Maintainability: simple config & upgrades
– Efficiency: low CPU → business logic stays fast
– Reliability: delay logs after peaks
– Security: encrypt sensitive data
– Maintainability: simple config & upgrades
October 9, 2025 at 11:01 AM
𝗜𝗦𝗢 𝟮𝟱𝟬𝟭𝟬 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗹𝗼𝗴 𝘀𝗵𝗶𝗽𝗽𝗲𝗿𝘀 𝘁𝗼𝗼 🚢
– Efficiency: low CPU → business logic stays fast
– Reliability: delay logs after peaks
– Security: encrypt sensitive data
– Maintainability: simple config & upgrades
– Efficiency: low CPU → business logic stays fast
– Reliability: delay logs after peaks
– Security: encrypt sensitive data
– Maintainability: simple config & upgrades
𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲.
With AI we can code texts the way we build programs: break complex documents into small, consistent units and assemble them into a whole. Like scenes in a novel → chapters → a book. Tools like Cursor.AI make this modular writing workflow smooth and powerful.
With AI we can code texts the way we build programs: break complex documents into small, consistent units and assemble them into a whole. Like scenes in a novel → chapters → a book. Tools like Cursor.AI make this modular writing workflow smooth and powerful.
October 7, 2025 at 9:30 AM
𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲.
With AI we can code texts the way we build programs: break complex documents into small, consistent units and assemble them into a whole. Like scenes in a novel → chapters → a book. Tools like Cursor.AI make this modular writing workflow smooth and powerful.
With AI we can code texts the way we build programs: break complex documents into small, consistent units and assemble them into a whole. Like scenes in a novel → chapters → a book. Tools like Cursor.AI make this modular writing workflow smooth and powerful.
𝗧𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗼𝗻𝗹𝘆 𝘁𝘄𝗼 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗶𝗻 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀:
2. Exactly-once delivery
1. Guaranteed order of messages
2. Exactly-once delivery
2. Exactly-once delivery
1. Guaranteed order of messages
2. Exactly-once delivery
October 2, 2025 at 11:00 AM
𝗧𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗼𝗻𝗹𝘆 𝘁𝘄𝗼 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗶𝗻 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀:
2. Exactly-once delivery
1. Guaranteed order of messages
2. Exactly-once delivery
2. Exactly-once delivery
1. Guaranteed order of messages
2. Exactly-once delivery
Vibe Coding isn’t just for software. With AI we can model texts like code: break down complex docs into small, consistent units, then stitch them together. Tools like Cursor.AI make this modular writing workflow smooth and powerful.
September 30, 2025 at 9:30 AM
Vibe Coding isn’t just for software. With AI we can model texts like code: break down complex docs into small, consistent units, then stitch them together. Tools like Cursor.AI make this modular writing workflow smooth and powerful.
𝗜𝗦𝗢 𝟮𝟱𝟬𝟭𝟬 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗹𝗼𝗴 𝘀𝗵𝗶𝗽𝗽𝗲𝗿𝘀 𝘁𝗼𝗼 🚢
– Efficiency: low CPU → business logic stays fast
– Reliability: delay logs after peaks
– Security: encrypt sensitive data
– Maintainability: simple config & upgrades
– Efficiency: low CPU → business logic stays fast
– Reliability: delay logs after peaks
– Security: encrypt sensitive data
– Maintainability: simple config & upgrades
September 25, 2025 at 11:00 AM
𝗜𝗦𝗢 𝟮𝟱𝟬𝟭𝟬 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗹𝗼𝗴 𝘀𝗵𝗶𝗽𝗽𝗲𝗿𝘀 𝘁𝗼𝗼 🚢
– Efficiency: low CPU → business logic stays fast
– Reliability: delay logs after peaks
– Security: encrypt sensitive data
– Maintainability: simple config & upgrades
– Efficiency: low CPU → business logic stays fast
– Reliability: delay logs after peaks
– Security: encrypt sensitive data
– Maintainability: simple config & upgrades
September 24, 2025 at 9:00 AM
⏱️ 𝗧𝗶𝗺𝗲𝗼𝘂𝘁? 𝗗𝗼𝗻’𝘁 𝗽𝗮𝗻𝗶𝗰.
👉 Troubleshooting steps:
1️⃣ Check network (logs, policies, TCP)
2️⃣ Check platform (K8s limits, node metrics)
3️⃣ Check app (GC logs, thread dumps)
4️⃣ Correlate everything for the big picture
Only then you’ll uncover the real cause.
👉 Troubleshooting steps:
1️⃣ Check network (logs, policies, TCP)
2️⃣ Check platform (K8s limits, node metrics)
3️⃣ Check app (GC logs, thread dumps)
4️⃣ Correlate everything for the big picture
Only then you’ll uncover the real cause.
September 23, 2025 at 9:30 AM
⏱️ 𝗧𝗶𝗺𝗲𝗼𝘂𝘁? 𝗗𝗼𝗻’𝘁 𝗽𝗮𝗻𝗶𝗰.
👉 Troubleshooting steps:
1️⃣ Check network (logs, policies, TCP)
2️⃣ Check platform (K8s limits, node metrics)
3️⃣ Check app (GC logs, thread dumps)
4️⃣ Correlate everything for the big picture
Only then you’ll uncover the real cause.
👉 Troubleshooting steps:
1️⃣ Check network (logs, policies, TCP)
2️⃣ Check platform (K8s limits, node metrics)
3️⃣ Check app (GC logs, thread dumps)
4️⃣ Correlate everything for the big picture
Only then you’ll uncover the real cause.
⏱️ 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝘁𝗶𝗺𝗲𝗼𝘂𝘁 = 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗶𝘀𝘀𝘂𝗲?
Not always! Timeouts are often just a symptom:
- overprovisioned hosts 🖥️
- Kubernetes limits ⚙️
- Java garbage collection ♻️
…or all of them combined.
The root cause usually lies deeper — not just “the network.” 🚨
Not always! Timeouts are often just a symptom:
- overprovisioned hosts 🖥️
- Kubernetes limits ⚙️
- Java garbage collection ♻️
…or all of them combined.
The root cause usually lies deeper — not just “the network.” 🚨
September 18, 2025 at 11:00 AM
⏱️ 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝘁𝗶𝗺𝗲𝗼𝘂𝘁 = 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗶𝘀𝘀𝘂𝗲?
Not always! Timeouts are often just a symptom:
- overprovisioned hosts 🖥️
- Kubernetes limits ⚙️
- Java garbage collection ♻️
…or all of them combined.
The root cause usually lies deeper — not just “the network.” 🚨
Not always! Timeouts are often just a symptom:
- overprovisioned hosts 🖥️
- Kubernetes limits ⚙️
- Java garbage collection ♻️
…or all of them combined.
The root cause usually lies deeper — not just “the network.” 🚨
🚦 Dreyfus model:
👉 Beginners need rules — everything feels equally important.
🎯 Experts act intuitively — they focus on what matters and ignore the rest.
From rules to pattern recognition: that’s the path to real expertise. ✨
👉 Beginners need rules — everything feels equally important.
🎯 Experts act intuitively — they focus on what matters and ignore the rest.
From rules to pattern recognition: that’s the path to real expertise. ✨
September 16, 2025 at 9:30 AM
🚦 Dreyfus model:
👉 Beginners need rules — everything feels equally important.
🎯 Experts act intuitively — they focus on what matters and ignore the rest.
From rules to pattern recognition: that’s the path to real expertise. ✨
👉 Beginners need rules — everything feels equally important.
🎯 Experts act intuitively — they focus on what matters and ignore the rest.
From rules to pattern recognition: that’s the path to real expertise. ✨
🧵 𝗝𝗮𝘃𝗮 𝗢𝗢𝗠 𝗵𝗮𝘀 𝗳𝗹𝗮𝘃𝗼𝗿𝘀:
Heap space → objects don’t fit.
Non-heap → stacks, threads, metaspace, direct buffers.
OS OOM → kernel kills JVM when RAM is gone.
👉 Not all OOMs are equal.
Heap space → objects don’t fit.
Non-heap → stacks, threads, metaspace, direct buffers.
OS OOM → kernel kills JVM when RAM is gone.
👉 Not all OOMs are equal.
September 11, 2025 at 11:01 AM
🧵 𝗝𝗮𝘃𝗮 𝗢𝗢𝗠 𝗵𝗮𝘀 𝗳𝗹𝗮𝘃𝗼𝗿𝘀:
Heap space → objects don’t fit.
Non-heap → stacks, threads, metaspace, direct buffers.
OS OOM → kernel kills JVM when RAM is gone.
👉 Not all OOMs are equal.
Heap space → objects don’t fit.
Non-heap → stacks, threads, metaspace, direct buffers.
OS OOM → kernel kills JVM when RAM is gone.
👉 Not all OOMs are equal.
🚀 𝗧𝗮𝗶𝗹𝗶𝗻𝗴 𝗹𝗼𝗴𝗳𝗶𝗹𝗲𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗹𝗼𝗴𝗴𝗶𝗻𝗴 𝘀𝘁𝗮𝗰𝗸 = 𝗯𝗶𝗴 𝘄𝗶𝗻𝘀:
🗂 acts like a queue (resume later),
⚡ bulk writes > single updates,
🔄 data can be re-processed,
🛡️ resilient to outages.
Simple, robust, efficient logging.
🗂 acts like a queue (resume later),
⚡ bulk writes > single updates,
🔄 data can be re-processed,
🛡️ resilient to outages.
Simple, robust, efficient logging.
September 9, 2025 at 9:30 AM
🚀 𝗧𝗮𝗶𝗹𝗶𝗻𝗴 𝗹𝗼𝗴𝗳𝗶𝗹𝗲𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗹𝗼𝗴𝗴𝗶𝗻𝗴 𝘀𝘁𝗮𝗰𝗸 = 𝗯𝗶𝗴 𝘄𝗶𝗻𝘀:
🗂 acts like a queue (resume later),
⚡ bulk writes > single updates,
🔄 data can be re-processed,
🛡️ resilient to outages.
Simple, robust, efficient logging.
🗂 acts like a queue (resume later),
⚡ bulk writes > single updates,
🔄 data can be re-processed,
🛡️ resilient to outages.
Simple, robust, efficient logging.
Kubernetes requests/limits decide pod placement.
𝗧𝗼𝗼 𝘀𝗺𝗮𝗹𝗹 → overbooked nodes.
𝗧𝗼𝗼 𝗯𝗶𝗴 → wasted resources.
𝗡𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 = tiny defaults, risking instability.
Right-sizing ensures fair scheduling & efficient clusters.
𝗧𝗼𝗼 𝘀𝗺𝗮𝗹𝗹 → overbooked nodes.
𝗧𝗼𝗼 𝗯𝗶𝗴 → wasted resources.
𝗡𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 = tiny defaults, risking instability.
Right-sizing ensures fair scheduling & efficient clusters.
September 4, 2025 at 11:01 AM
Kubernetes requests/limits decide pod placement.
𝗧𝗼𝗼 𝘀𝗺𝗮𝗹𝗹 → overbooked nodes.
𝗧𝗼𝗼 𝗯𝗶𝗴 → wasted resources.
𝗡𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 = tiny defaults, risking instability.
Right-sizing ensures fair scheduling & efficient clusters.
𝗧𝗼𝗼 𝘀𝗺𝗮𝗹𝗹 → overbooked nodes.
𝗧𝗼𝗼 𝗯𝗶𝗴 → wasted resources.
𝗡𝗼 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 = tiny defaults, risking instability.
Right-sizing ensures fair scheduling & efficient clusters.
Linux “Steal Time” = CPU time your VM wanted but the hypervisor gave to another VM.
On bare metal it’s ~0%.
On VMs, high values mean your host is overloaded.
Check with top (st) or mpstat.
On bare metal it’s ~0%.
On VMs, high values mean your host is overloaded.
Check with top (st) or mpstat.
September 2, 2025 at 9:31 AM
Linux “Steal Time” = CPU time your VM wanted but the hypervisor gave to another VM.
On bare metal it’s ~0%.
On VMs, high values mean your host is overloaded.
Check with top (st) or mpstat.
On bare metal it’s ~0%.
On VMs, high values mean your host is overloaded.
Check with top (st) or mpstat.
Container platforms 𝗱𝗼𝗻’𝘁 𝗯𝗲𝗹𝗼𝗻𝗴 𝗶𝗻 𝗹𝗮𝗯 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀. Labs are often overprovisioned: one heavy job from a neighbor and the host CPU is gone. 𝗩𝗠 𝗴𝘂𝗲𝘀𝘁𝘀 𝘀𝘁𝗮𝗿𝘃𝗲, timeouts pile up — and containers are especially brittle under those conditions.
August 28, 2025 at 11:01 AM
Container platforms 𝗱𝗼𝗻’𝘁 𝗯𝗲𝗹𝗼𝗻𝗴 𝗶𝗻 𝗹𝗮𝗯 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀. Labs are often overprovisioned: one heavy job from a neighbor and the host CPU is gone. 𝗩𝗠 𝗴𝘂𝗲𝘀𝘁𝘀 𝘀𝘁𝗮𝗿𝘃𝗲, timeouts pile up — and containers are especially brittle under those conditions.
𝗞𝟴𝘀 𝗗𝗼𝗺𝗶𝗻𝗼 𝗘𝗳𝗳𝗲𝗰𝘁
Incident timeline 🕒
1️⃣ No requests/limits set 🚫
2️⃣ Too many pods on one worker 🐘
3️⃣ Load ↑ → kubelet timeouts ⏳
4️⃣ Node drops ❌
5️⃣ Pods reschedule… next node dies 🔄
Repeat until chaos complete 💥
Fix: set sane limits & protect your cluster 🛠️
Incident timeline 🕒
1️⃣ No requests/limits set 🚫
2️⃣ Too many pods on one worker 🐘
3️⃣ Load ↑ → kubelet timeouts ⏳
4️⃣ Node drops ❌
5️⃣ Pods reschedule… next node dies 🔄
Repeat until chaos complete 💥
Fix: set sane limits & protect your cluster 🛠️
August 26, 2025 at 9:31 AM
𝗞𝟴𝘀 𝗗𝗼𝗺𝗶𝗻𝗼 𝗘𝗳𝗳𝗲𝗰𝘁
Incident timeline 🕒
1️⃣ No requests/limits set 🚫
2️⃣ Too many pods on one worker 🐘
3️⃣ Load ↑ → kubelet timeouts ⏳
4️⃣ Node drops ❌
5️⃣ Pods reschedule… next node dies 🔄
Repeat until chaos complete 💥
Fix: set sane limits & protect your cluster 🛠️
Incident timeline 🕒
1️⃣ No requests/limits set 🚫
2️⃣ Too many pods on one worker 🐘
3️⃣ Load ↑ → kubelet timeouts ⏳
4️⃣ Node drops ❌
5️⃣ Pods reschedule… next node dies 🔄
Repeat until chaos complete 💥
Fix: set sane limits & protect your cluster 🛠️