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AnuDB – An experimental C++ document store using RocksDB

Hacker News

AnuDB – An experimental C++ document store using RocksDB

I have been working in the database field for a significant amount of time; however, I never had the opportunity to work on the core of a database—until now. I am excited to introduce AnuDB, a document-oriented database built using RocksDB for persistence. Given my background in embedded Linux platforms, I conceptualized running a database on embedded systems. While there are several databases available in the market, most high-quality solutions cater primarily to enterprise customers. Although some databases exist for embedded platforms, they come with various limitations. AnuDB aims to address this gap, specifically targeting the IoT domain, where frequent data streaming and storage are essential. AnuDB leverages RocksDB’s LSM tree-based architecture as its storage engine, ensuring efficient handling of high-throughput workloads. The project includes JSON-based APIs for CRUD operations, with enforced indexing for document retrieval. The indexing mechanism is implemented using prefix extractors in RocksDB—further details can be found in the Collection class of AnuDB. I invite you to explore the GitHub repository: https://github.com/hash-anu/AnuDB . Your insights and feedback would be invaluable in refining and improving the project. I look forward to hearing your thoughts!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, lua · Missing: https docs, just released, open source
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, efficient · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: using, apis · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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