FaunaDB vs OrbitDB

Overview

Fauna

Stacks112

Followers153

Votes27

OrbitDB

Stacks10

Followers60

Votes0

FaunaDB vs OrbitDB: What are the differences?

Introduction

FaunaDB and OrbitDB are both decentralized databases that aim to provide secure and scalable solutions for storing and querying data. While they share some similarities, there are several key differences between the two.

Data Consistency: One major difference between FaunaDB and OrbitDB is the approach to data consistency. FaunaDB provides strong consistency, ensuring that all replicas of the database are always in sync. On the other hand, OrbitDB uses eventual consistency, which means that replicas may temporarily have different versions of the data until they synchronize.
Data Storage: FaunaDB uses a distributed global index to store and retrieve data efficiently. It employs a document model, allowing for structured data with relationships. In contrast, OrbitDB uses a distributed hash table (DHT) to store data in a peer-to-peer network. It provides a key-value store and supports JSON data structures.
Query Language: FaunaDB utilizes its own query language called FQL (Fauna Query Language). FQL offers a powerful and expressive syntax for querying and manipulating data within the database. On the other hand, OrbitDB does not have a dedicated query language. Instead, it provides an API for retrieving data by key or using range queries.
Data Replication: FaunaDB provides built-in multi-region and multi-cloud replication capabilities. This means that data can be efficiently replicated across different geographical regions or cloud providers for high availability and disaster recovery. In contrast, OrbitDB relies on the IPFS (InterPlanetary File System) network for replication. It utilizes the IPFS swarm to distribute data and ensure redundancy.
Cryptography: Both FaunaDB and OrbitDB prioritize data security, but they differ in their cryptographic approaches. FaunaDB incorporates end-to-end encryption, which means that data is encrypted both during transit and at rest. In contrast, OrbitDB utilizes content addressing and a distributed hash table to ensure data integrity but does not provide built-in encryption.
Community and Ecosystem: FaunaDB has a larger and more mature community compared to OrbitDB. It has a wide range of integrations and supports various programming languages, making it easier to adopt and integrate into existing applications. OrbitDB, being built on top of IPFS, benefits from the wider IPFS ecosystem, but it has a smaller community and fewer integrations compared to FaunaDB.

In Summary, FaunaDB and OrbitDB have key differences in data consistency, storage mechanisms, query languages, data replication, cryptography, and community support. These differences make them suitable for different use cases and environments.

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CLI (Node.js)

Manual

Detailed Comparison

Fauna	OrbitDB
Escape the boundaries imposed by legacy databases with a data API that is simple to adopt, highly productive to use, and offers the capabilities that your business needs, without the operational pain typically associated with databases.	It is a serverless, distributed, peer-to-peer database. It uses IPFS as its data storage and IPFS Pubsub to automatically sync databases with peers. It’s an eventually consistent database that uses CRDTs for conflict-free database merges making it an excellent choice for decentralized apps (dApps), blockchain applications and offline-first web applications.
Native support for GraphQL and others. Easily access any data with any API. No middleware necessary.; Access all data via a data model that best suits your needs - relational, document, graph or composite.; A unique approach to indexing makes it simpler to write efficient queries that scale with your application.; Build SaaS apps more easily with native multi-tenancy and query-level QoS controls to prevent workload collisions.; Eliminate data anomalies with multi-region ACID transactions that don't limit number of keys or documents.; Data-driven RBAC that combines with SSL to offers reliable protection, and yet is simple to understand and codify.; Travel back in time with temporal querying. Run queries at a point-in-time or as change feeds. Track how your data evolved.; Dynamically replicates your data to global locations, so that your queries run fast no matter where your users are.; Easily deploy a FaunaDB cluster on your workstation accompanied by a powerful shell and tools to simplify your workflow.;	Peer-to-Peer Database; Serverless; Automatically sync databases with peers
Statistics
Stacks 112	Stacks 10
Followers 153	Followers 60
Votes 27	Votes 0
Pros & Cons
Pros 5 100% ACID 4 Generous free tier 4 Removes server provisioning or maintenance 3 No more n+1 problems (+ GraphQL) 3 Low latency global CDN's Cons 1 Log stack traces to avoid improper exception handling 1 Must keep app secrets encrypted 1 Susceptible to DDoS (& others) use timeouts throttling	No community feedback yet
Integrations
No integrations available	Node.js

What are some alternatives to Fauna, OrbitDB?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

FaunaDB vs OrbitDB: What are the differences?

Introduction

Data Consistency: One major difference between FaunaDB and OrbitDB is the approach to data consistency. FaunaDB provides strong consistency, ensuring that all replicas of the database are always in sync. On the other hand, OrbitDB uses eventual consistency, which means that replicas may temporarily have different versions of the data until they synchronize.
Data Storage: FaunaDB uses a distributed global index to store and retrieve data efficiently. It employs a document model, allowing for structured data with relationships. In contrast, OrbitDB uses a distributed hash table (DHT) to store data in a peer-to-peer network. It provides a key-value store and supports JSON data structures.
Query Language: FaunaDB utilizes its own query language called FQL (Fauna Query Language). FQL offers a powerful and expressive syntax for querying and manipulating data within the database. On the other hand, OrbitDB does not have a dedicated query language. Instead, it provides an API for retrieving data by key or using range queries.
Data Replication: FaunaDB provides built-in multi-region and multi-cloud replication capabilities. This means that data can be efficiently replicated across different geographical regions or cloud providers for high availability and disaster recovery. In contrast, OrbitDB relies on the IPFS (InterPlanetary File System) network for replication. It utilizes the IPFS swarm to distribute data and ensure redundancy.
Cryptography: Both FaunaDB and OrbitDB prioritize data security, but they differ in their cryptographic approaches. FaunaDB incorporates end-to-end encryption, which means that data is encrypted both during transit and at rest. In contrast, OrbitDB utilizes content addressing and a distributed hash table to ensure data integrity but does not provide built-in encryption.
Community and Ecosystem: FaunaDB has a larger and more mature community compared to OrbitDB. It has a wide range of integrations and supports various programming languages, making it easier to adopt and integrate into existing applications. OrbitDB, being built on top of IPFS, benefits from the wider IPFS ecosystem, but it has a smaller community and fewer integrations compared to FaunaDB.

FaunaDB vs OrbitDB

Overview