Debugging Production Issue at 2 AM: API Timeouts and Index Optimization

String validation in C++ is fast, until you have to do it millions of times per second across massive datasets. While engineering a correctness fix for a silent truncation bug in Apache Arrow’s base64_decode utility, an automated review flagged a bottleneck: the function was using a linear search (std::string::find) to validate every single byte. For a 1MB payload, that meant potentially tens of millions of redundant CPU operations. Rather than bloating the initial correctness PR, I scoped the performance upgrade into a separate architectural follow-up. I replaced the linear scan with a static 256-entry lookup table (a direct-addressed array). This shifted the character validation from an O(N) linear search to an O(1) constant-time memory lookup via pointer arithmetic. The benchmarks on a 1MB payload: 🔴 Before (Unsafe): ~2832 ms 🟡 Intermediate (Strict Validation, but linear): ~4302 ms 🟢 Final (Strict Validation + O(1) Lookup): ~1126 ms Massive thanks to Kouhei Sutou and Dmitry C. for the feedbacks and for helping me get this optimization across the finish line. PR Link : https://lnkd.in/gjpM5ey9 #Cpp #Apache #ApacheArrow #SystemsEngineering #DataInfrastructure #OpenSource

How to debug kernel crash: 1️⃣ First capture exact crash signature Immediately collect: dmesg or serial console log if system hangs before filesystem flush. Look for: Oops, Panic, stack trace, faulting address, instruction pointer Typical output gives: faulting function, CPU number, process name, register dump 2️⃣ Read panic type carefully Typical categories: NULL pointer dereference Example: Unable to handle kernel NULL pointer dereference Usually: invalid pointer, freed structure, missing init Page fault: Means invalid memory access. Need to inspect: virtual address, permission bits, call stack 3️⃣ Read stack trace top-down Important: Top frame is not always root cause. Read: Call Trace: funcA funcB funcC Need: Identify first suspicious function you own Not just kernel generic wrapper. 4️⃣ Check if crash is deterministic or random Deterministic: same place every boot / action Likely: logic bug, init issue Random: different locations Likely: race, memory corruption DMA overwrite 5️⃣ If memory corruption suspected Use: KASAN Kernel Address Sanitizer. Detects: out-of-bounds, use-after-free 6️⃣ If race suspected Use: lockdep, KCSAN Symptoms: bug disappears with printk, hard to reproduce, random call traces 7️⃣ If interrupt-related crash Check: interrupt context stack whether sleeping inside ISR wrong lock usage Look for: BUG: sleeping function called from invalid context 8️⃣ Use crash dump if available Enable: kdump Then analyze vmcore using: crash vmlinux vmcore #kernel #debug #systemdesign #embedded #learning

Most developers debug like this ❌ “CPU spike? Let’s optimize code.” But a better approach is 👇 1. Reproduce under load 2. Break problem into layers: - CPU (goroutines, GC, computation) - DB (queries, indexes) - External APIs (latency) 3. Debug step-by-step 4. Connect the dots 💡 Real lesson: Don’t guess. Narrow down. Because: High CPU ≠ always CPU problem Slow response ≠ always DB problem 💬 What’s your debugging approach in production?

Our service was dropping requests under load. CPU was at 30%. Everything looked "fine." It wasn't a capacity problem. It was thread-pool starvation. Here's how I diagnosed and fixed it: 📌 SYMPTOM 503 errors and timeouts spiking. CPU looked idle. DB looked healthy. Restarting temporarily fixed it. 🔍 DIAGNOSIS Pulled a thread dump → every thread was in WAITING state. Pool was saturated. Queue depth was at max. 🕵️ ROOT CAUSE One upstream service had no timeout set. Each request grabbed a thread and held it for 8–15 seconds. 200 threads × 10s = 2,000 thread-seconds of capacity gone. 🛠 FIX 1. Set timeouts on all external calls (non-negotiable) 2. Moved slow tasks to a separate pool 3. Added circuit breakers to fail fast 📐 SIZING (the math that saved us) I/O-bound pool size = cores × (1 + wait_time / compute_time) We had 8 cores and 90% wait time → pool needed ~80 threads, not 20. The lesson: When CPU is low but requests drop, look at your thread pool, not your servers. Have you ever debugged thread-pool starvation? Drop your experience below👇 #SystemDesign #Backend #SoftwareEngineering #Java #DistributedSystems

Recently, I started using the #Codex extension by OpenAI in #VSCode as part of my daily workflow. Not long after, I noticed my laptop heating up, with “Code Helper (Renderer)” consistently spiking CPU and RAM usage. At first, I assumed it was a general VS Code issue. It turned out to be something more specific. After examining extension logs and webview behavior, I traced it to a retry loop: 1. The open-in-targets handler throws in extension mode 2. Instead of failing gracefully, the webview keeps retrying 3. Resulting in sustained CPU and RAM usage and wasted resources, a clear #Performance concern The solution is straightforward: return an empty response instead of throwing, so the retry loop stops. This kind of issue is a good example of how small edge cases in #DeveloperTools can escalate quickly if not handled properly. I also included a small script in the issue to make it easy to patch locally while waiting for an official fix. Since sharing it, several users have confirmed that the script resolves the issue on their end. I opened a #GitHub issue to document the findings and share the fix: https://lnkd.in/d6a763s4 This was a good reminder of how digging into logs and system behavior is critical in #Debugging, especially when working with modern #AIDev tools. It may seem like a small edge case, but it can easily slip into production and impact performance.

A function was 3 microseconds slower than it should have been. The code was clean. The logic was right. The algorithm was optimal. So you stop looking at the code and start looking at the CPU. The data wasn’t aligned to cache line boundaries. Every access was crossing a 64-byte line, forcing the prefetcher to pull twice the memory it needed. One alignas(64). That’s all it took. 3 microseconds back. In a function that runs millions of times before 9:15 AM, that’s not a micro-optimization. That’s a completely different system. This is what C++ in market data infrastructure gives you. Not just control over your logic but control over how your logic meets the hardware. Most bugs live in code. The interesting ones live in the space between code and silicon.

Someone just open-sourced a tool that converts pdfs to markdown at 100 pages per second. It's called OpenDataLoader. It runs entirely on CPU and handles complex layouts, tables, and nested structures like a senior dev 100% Free.

Advanced developers, let's talk microarchitecture. A common, yet overlooked, performance bottleneck lies within the CPU's instruction decode unit. Modern CISC processors dynamically translate complex machine instructions into simpler micro-operations (μops) for execution. However, intricate or poorly ordered instruction streams can lead to decode stalls, effectively limiting the front-end's throughput. Optimizing for CPU-friendly instruction patterns—favoring simpler instructions that typically translate to fewer μops and ensuring these hot code paths benefit from the micro-op cache (Decoded Stream Buffer)—can significantly enhance Instruction Per Cycle (IPC). We've seen 5-10% IPC gains in critical workloads by consciously addressing this decode-stage efficiency. This granular optimization is crucial for maximizing performance in high-throughput applications. #PerformanceEngineering #CPUOptimization #Microarchitecture #LowLevelProgramming #DeveloperInsights #TechLeadership

I ran a simple Go experiment to understand: 👉 concurrency vs parallelism Same code. Same machine. Just changing GOMAXPROCS. Here’s the result: CPU-bound procs=1 → 4.80s procs=2 → 2.26s procs=4 → 1.19s procs=8 → 0.59s IO-bound procs=1 → ~101ms procs=2 → ~101ms procs=4 → ~101ms procs=8 → ~101ms Mixed procs=1 → 478ms procs=2 → 271ms procs=4 → 166ms procs=8 → 111ms 🧠 CPU-bound → real parallelism 🌐 IO-bound → no change (nothing is actually “running”) ⚡ Mixed → both Full benchmark + code: 👉 https://lnkd.in/d3-t3FwA #golang #concurrency #parallelism #performance #backend #softwareengineering #benchmark

Continuous profiling usually means CPU profiles, especially in the #ebpf world. But for many incidents, memory profiling is just as useful. When an application is under memory pressure, infrastructure metrics alone are not enough. You need to dig into the code level and understand which functions allocate memory and why. Coroot can now collect heap profiles for Go applications with zero configuration. No code changes, no redeploys, and surprisingly, no eBPF involved :) I described how we implemented it in the Coroot blog: https://lnkd.in/dxwXjxmV