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BlitzDB – Document-oriented database written purely in Python

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BlitzDB – Document-oriented database written purely in Python

BlitzDB, or just Blitz, is a document-based, object-oriented, transactional database written purely in Python. Among other things, it provides a powerful querying language, deep indexing of documents, compressed data storage and automatic referencing of embedded documents. It is reasonably fast, can be easily embedded in any Python application and does not have any external dependencies (except when using a third-party backend). In addition, you can use it as a frontend to other database engines such as MongoDB in case you should need more power.

Visit productView on Hacker NewsMar 13, 2014
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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
56%56% 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 · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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