EloqKV – Scalable distributed ACID key-value database with Redis API

Hacker News

We're thrilled to unveil EloqKV, a lightning-fast distributed key-value store with a Redis-compatible API. Built on a new database architecture called the Data Substrate, EloqKV brings significant innovations to database design. Here’s the unique features that makes it stand out: - Flexible Deployment: Run it as a single-node in-memory KV cache, a larger-than-memory database or scale to a highly available, distributed transactional database with ease. - High Performance: Achieves performance levels comparable to top in-memory databases like Redis and DragonflyDB, while significantly outperforming durable KV stores like KVRocks. - Full ACID Transactions: Ensures complete transactional integrity, even in distributed environments. - Independent Resource Scaling: Scale CPU, memory, storage, and logging resources independently to meet your needs. We’d love to hear your thoughts and feedback!

Visit product View on Hacker NewsSep 19, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, compatible · 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.

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

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

73%73% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

22%22% 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.

Correct prediction on native model

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