Kaव्य
@MaNiAcCoder3
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caffeinated coder by day, highly caffeinated coder by night. compsci grad @_PECChandigarh
India
Joined November 2021
Imagine how hard life would be if every fresher was cracked af and grinded LC. I thank MERN youtubers everyday for lobotomising these fresh grads. Every time I see someone struggle with reversing a linkedlist, I can’t stop smiling. one less guy I have to worry about. When I
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In Go, if you store every submitted goroutine in a global https://t.co/L5OFm8Vylx for tracking — you’re not monitoring tasks, you’re collecting them forever How do you: -remove task refs after execution? -avoid static maps in Go? -use errgroup or context for lifecycle tracking?
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Gonna start solving some Interesting System Design LLD HLD problems from now on just to work more on Golang
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>I can use a library written by someone else, without any worries that it will break my own code The poser-engineering reality of modern software development captured in a single sentence
Not learning Rust is a mistake. Name another language where I can use a library written by someone else, without any worries that it will break my own code? The headache of learning Rust is significantly less than the mental trauma caused by having to fix bugs in production.
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Let the task lifecycle define object lifetime , not global maps. In Go, global state is rarely needed. Prefer goroutines, contexts, and sync primitives for cleaner, leak-free design. #Golang #MemoryManagement #GoLangTips
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✅ Fix 4: Just use sync.WaitGroup or errgroup Group. They exist exactly to track task lifecycles safely: g.Go(func() error { return task() }) No global maps. No leaks. Cleaner concurrency.
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Go doesn’t have WeakHashMap, but you can mimic it. Run a cleanup goroutine that periodically prunes completed tasks: func CleanupCompletedTasks() { ... } This keeps memory under control when tracking tasks is unavoidable.
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✅ Fix 2: Use context.Context or channels. Instead of tracking tasks in a global map, let each task manage its own lifecycle. When the context is cancelled, the goroutine exits , no leaks, no global state.
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✅ Fix 1: Explicit cleanup defer submittedTasks.Delete(taskID) Right after your goroutine completes, remove its reference. Simple, reliable, and the easiest way to keep memory stable.
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This is Go’s version of a Java static Map memory leak. ➡️ Tasks complete ➡️ References never removed ➡️ Heap grows slowly ➡️ GC works harder each cycle Your service feels “fine” at first… until uptime hits weeks. 😬
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It starts innocently: var submittedTasks = https://t.co/L5OFm8Vylx{} func SubmitTask(taskID int64, task func()) { https://t.co/fB1beRX5RC(taskID, task) go task() } Every submitted task is stored forever even after it completes.GC can’t reclaim memory because references are live
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Ever seen a Go service slowly eat memory over time? 👀 You might be holding onto tasks long after they’re done — a classic static map leak, but in Go. Let’s break down how this happens (and fix it properly). 🧵 #Golang #MemoryLeak #Concurrency
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Go’s io.Reader/io.Writer interfaces make it perfect for streaming binary AV data. Pair that with FFmpeg bindings and you can build a concurrent AV analyzer with zero external daemons.
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In just a few hundred lines, this Go service handles: 🎥 Video fetching 🎧 Audio normalization 📦 HLS packaging 🖼️ Cover compression ☁️ Cloud upload That’s the beauty of Go — minimalism with real-world muscle 💪 #Golang #SystemDesign #BackendDev
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Even the cover image isn’t left out. It’s downloaded, compressed, and optimized for the web — keeping everything efficient in storage and delivery. #GoLang #ImageProcessing
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Next comes the magic: encoding 🎬 Some videos don’t even have audio, but HLS requires one — so it adds a silent track automatically before converting to HLS. Smart and resilient. #FFmpeg #MediaProcessing
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It all starts in a temporary workspace — every video gets its own isolated environment. When processing finishes, everything is cleaned up automatically. Simple. Predictable. Elegant. #GoLang #CleanCode
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Just made a Go module that processes videos — downloads, encodes to HLS, compresses cover images, and uploads to S3. Here’s a breakdown of how this works under the hood 👇 #Golang #VideoProcessing #AWS
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