Samchika – A Java Library for Fast, Multithreaded File Processing

Hacker News

Hi HN, I built a Java library called SmartFileProcessor to make high-performance, multi-threaded file processing simpler and more maintainable. Most Java file processing solutions either involve a lot of boilerplate or don’t handle concurrency, backpressure, or metrics well out of the box. I needed something fast, clean, and production-friendly — so I built this. Key features: Multi-threaded line/batch processing using a configurable thread pool Producer/consumer model with built-in backpressure Buffered, asynchronous writing with optional auto-flush Live metrics: memory usage, throughput, thread times, queue stats Simple builder API — minimal setup to get going Output metrics to JSON, CSV, or human-readable format Use cases: Large CSV or log file parsing ETL pre-processing Line-by-line filtering and transformation Batch preparation before ingestion I’d really appreciate your feedback — feature ideas, performance improvements, critiques, or whether this solves a real problem for others. Thanks for checking it out!

Visit product View on Hacker NewsMay 23, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: friendly, builder · Missing: plus, platform, intuitive

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, using · Missing: mac, agents, macos

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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