Su

SuperMassive – Fast durable, in-memory, distributed key-value database

Hacker News

SuperMassive – Fast durable, in-memory, distributed key-value database

Hey hn! I hope you're all doing well. I’d like to share a new open-source database I’ve designed and written called SuperMassive. SuperMassive is a scalable, distributed, in-memory key-value database designed from the ground up to allow for high concurrency, fast writes, fault tolerance, and durability while remaining simple to use and efficiently scalable. The idea for SuperMassive comes from its name.. I wanted to build a key-value database that can scale infinitely, remain durable, be self-healing, consistent, and blazing fast. I also wanted to simplify sharding and replication compared to existing distributed KV stores and their protocols. SuperMassive is built to be simple yet powerful. It consists of just one multiplatform supported binary that can run in multiple modes(cluster, primary node, or replica). Nodes function as shards of your data. The design emphasizes minimal configuration, high performance, and automatic failover. While SuperMassive is still in its early stages (v1.0.2b), I’d love to hear your thoughts on the design, its architecture, and any feedback you may have :D Features~~ ~ Highly scalable Scale horizontally with ease. Simply add more nodes to the cluster. ~ Distributed Data is distributed across multiple nodes in a sharded fashion. ~ Robust Health Checking System Health checks are performed on all nodes, if any node is marked unhealthy we will try to recover it. ~ Smart Data Distribution uses a sequence-based round-robin approach for distributing writes across primary nodes. This ensures that all primary nodes get an equal share of writes. ~ Automatic Fail-over Automatic fail-over of primary nodes on write failure. If a primary node is unavailable for a write, we go to the next available primary node. ~ Parallel Read Operations Read operations are performed in parallel. ~ Consistency Management Timestamp-based version control to handle conflicts. The most recent value is always returned, the rest are deleted. ~ Fault-tolerant Replication and fail-over are supported. If a node goes down, the cluster will continue to function. ~ Self-healing Automatic data recovery. A node can recover from a journal. A node replica can recover from a primary node via a check point like algorithm. ~ Cluster Authentication with basic like auth. ~ Simple Protocol Simple protocol PUT, GET, DEL, INCR, DECR, REGX, STAT, RCNF, PING. ~ Async Node Journal Operations are written to a journal asynchronously. This allows for fast writes and recovery. ~ Multi-platform Linux, Windows, MacOS https://supermassivedb.com https://github.com/supermassivedb/supermassive - Alex

Share card

Actual performance

7points
2comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: para, efficiently · Missing: supports, reddit linkedin, podcasting
96%96% 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: mac, macos, new · Missing: agents, agent, cursor
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
75%75% 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 · Strong signals: platform, efficient · Missing: plus, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
38%38% 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
21%21% 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.

Correct prediction on native model

Similar products

Sk
Sklad, a key-value database in Zig60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sklad, a key-value database in Zig

Hacker News2
Ro
RonDB – fast key-value database in the cloud64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

RonDB – fast key-value database in the cloud

Hacker News112
Bu
Build8 Key-Value service is a simple key-value database59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Build8 Key-Value service is a simple key-value database

Hacker News1
Be
BerylDB – a small key-value database59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

BerylDB – a small key-value database

Hacker News57
Ol
Olivia, a distributed, in-memory, Key-value store in Go58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Olivia, a distributed, in-memory, Key-value store in Go

Hacker News6
Ki
Kiwi – a minimalistic, extendable, in-memory key value store44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Kiwi – a minimalistic, extendable, in-memory key value store

Hacker News23
Ke
Key-Value-Exists48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Key-Value-Exists

Hacker News2
Fr
Framing: Context-Based Value-Key Database47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Framing: Context-Based Value-Key Database

Hacker News1
Em
Embeddable distributed in-memory key/value data store and cache in Go61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Embeddable distributed in-memory key/value data store and cache in Go

Hacker News1
El
EloqKV – Scalable distributed ACID key-value database with Redis API69%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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

Hacker News44