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ReJSON – a JSON data type for Redis

Hacker News

ReJSON – a JSON data type for Redis

Redis v4 is planned for GA in a few weeks and it comes with a new modules API. Over the last months we've been putting together a few of these modules, and yesterday we've published the preview for ReJSON. ReJSON is a Redis module that implements a data type for storing, fetching and manipulating JSON values stored as binary objects. The API is faithful to the Redis spirit with a new set of commands (https://redislabsmodules.github.io/rejson/commands), and the same spirit is maintained in terms of performance as our our early benchmarks suggest (https://redislabsmodules.github.io/rejson/performance). The project is currently under active development and we still have improvements planned before calling it an MVP (mainly reducing memory overhead and performance optimizations). That said, anything you say can and may be used to make ReJSON better, so feel free to drop us a line here or anywhere else :) Source code: https://github.com/RedisLabsModules/rejson Docs: https://redislabsmodules.github.io/rejson

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
20%20% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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