Software Alternatives, Accelerators & Startups

OrbitDB VS Dragonfly DB

Compare OrbitDB VS Dragonfly DB and see what are their differences

OrbitDB logo OrbitDB

OrbitDB is a serverless, distributed, peer-to-peer database.

Dragonfly DB logo Dragonfly DB

Dragonfly - Scalable in-memory datastore made simple
  • OrbitDB Landing page
    Landing page //
    2023-09-26
  • Dragonfly DB Landing page
    Landing page //
    2023-09-19

OrbitDB features and specs

  • Decentralization
    OrbitDB is a peer-to-peer database system built on top of IPFS, meaning it does not rely on a central server. This enhances data availability and resilience against censorship.
  • Offline-first Operation
    Since OrbitDB is built on IPFS, it can operate without a constant connection to the internet, allowing data access and changes even when offline.
  • Versioning and Immutability
    It provides built-in support for versioning and immutability of data, ensuring that historical records can be maintained and easily accessed.
  • Eventual Consistency
    OrbitDB uses eventual consistency, which means all nodes will eventually reach the same data state. This is suitable for applications tolerant to eventual consistency rather than immediate data consistency.
  • Community and Open Source
    As an open-source project, OrbitDB benefits from community contributions and transparency, allowing for improved security, auditing, and flexibility in modifications.

Possible disadvantages of OrbitDB

  • Complex Synchronization
    Handling data consistency and synchronization in a decentralized manner can be complex, which may result in increased development and maintenance efforts.
  • Performance Overhead
    Running on a peer-to-peer network can introduce performance overhead due to factors like data propagation delays and potential network instability.
  • Limited Ecosystem Support
    Compared to more established databases, OrbitDB has a more limited ecosystem of tools and integrations, potentially requiring more custom development work.
  • Learning Curve
    Developers may face a steeper learning curve due to the decentralized nature and unique characteristics of OrbitDB and IPFS.
  • Eventual Consistency Trade-offs
    While eventual consistency can be a pro, it also means that applications requiring strict immediate consistency could face challenges when using OrbitDB.

Dragonfly DB features and specs

No features have been listed yet.

OrbitDB videos

On Telemetry for InterPlanetary Systems (OrbitDB + IPFS)

Dragonfly DB videos

No Dragonfly DB videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to OrbitDB and Dragonfly DB)
Databases
76 76%
24% 24
Relational Databases
100 100%
0% 0
NoSQL Databases
59 59%
41% 41
Network & Admin
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare OrbitDB and Dragonfly DB

OrbitDB Reviews

We have no reviews of OrbitDB yet.
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Dragonfly DB Reviews

Redis vs. KeyDB vs. Dragonfly vs. Skytable | Hacker News
In my opinion, when it comes to these types of multi-threaded benchmarks it's much better to separate the "baseline, one-process performance" from "how it scales with number of processes". E.g. if you first pin Dragonfly to only run on a single core you can find the baseline performance compared to Redis, and then you can run different benchmarks of Dragonfly with increasing...

Social recommendations and mentions

Based on our record, OrbitDB seems to be more popular. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

OrbitDB mentions (7)

  • Show HN: TeaTime – static distributed library powered by SQLite, IPFS and GitHub
    > (I'm looking into using Helia so that users are also contributing nodes in the network) I had to look that term up I was reminded of https://github.com/orbitdb/orbitdb#readme which seems like it may be much less rolling your own parts. - Source: Hacker News / 5 months ago
  • How to make a crowdsourced distributed metadata database?
    Both use OrbitDB: Peer-to-Peer Databases for the Decentralized Web. JavaScript. MIT license. repo. Source: almost 3 years ago
  • Release: New features for Nalli
    I think a wallet-agnostic memo solution is definitely the way. Having wallets that end up (partly) incompatible is only gonna hurt the UX. Maybe a decentralised DB solution like OrbitDB or GunDB can be the best way forward, although I haven't dove deeply into the docs yet. Source: about 3 years ago
  • Building a decentralized database
    Checkout this https://github.com/orbitdb/orbit-db peer-to-peer database for the decentralized Web. Source: about 3 years ago
  • Any suggestions to build a decentralized web app?
    I want to build a decentralized social media web app for a personal project, and I'm thinking on using IPFS. What tool, API, or library can get me set up without writing smart contracts or using blockchain solutions? I've heard that GunDB or OrbitDB are useful libraries- is that true? What are your thoughts and suggestions? Source: over 3 years ago
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Dragonfly DB mentions (0)

We have not tracked any mentions of Dragonfly DB yet. Tracking of Dragonfly DB recommendations started around Jun 2022.

What are some alternatives?

When comparing OrbitDB and Dragonfly DB, you can also consider the following products

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

KeyDB - KeyDB is fast NoSQL database with full compatibility for Redis APIs, clients, and modules.

Datahike - A durable datalog database adaptable for distribution.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Datomic - The fully transactional, cloud-ready, distributed database

Skytable - Skytable is a free and open-source realtime NoSQL database that aims to provide flexible data modelling at scale.