CouchDB vs EdgeDB

Overview

CouchDB

Stacks529

Followers584

Votes139

GitHub Stars6.7K

Forks1.1K

EdgeDB

Stacks17

Followers52

Votes0

CouchDB vs EdgeDB: What are the differences?

Data Model: CouchDB is a NoSQL database that uses a document-oriented data model, storing data in JSON documents. On the other hand, EdgeDB is a relational database that uses a schema-based data model, enforcing data integrity through relationships and constraints.
Query Language: CouchDB uses MapReduce for querying data, which can sometimes be complex to write and understand. In contrast, EdgeDB uses a more intuitive and SQL-like query language that allows for easier data retrieval and manipulation.
Scalability: CouchDB follows a distributed design, making it suitable for horizontal scalability and high availability. EdgeDB, on the other hand, is designed for vertical scalability, emphasizing performance through efficient data storage and retrieval.
Consistency: CouchDB offers eventual consistency by default, where updates may take time to propagate across replicas. In EdgeDB, strong consistency is maintained, ensuring that all queries see the same data at any given time.
Language Support: While CouchDB can be accessed through various programming languages using HTTP REST APIs, EdgeDB offers native support for Python, allowing for seamless integration and data manipulation within Python applications.
Schema Evolution: In CouchDB, schema evolution is more flexible as documents can have different structures. In EdgeDB, schema changes require migrations to maintain data integrity, making it more strict and controlled in terms of schema evolution.

In Summary, CouchDB and EdgeDB differ in terms of their data models, query languages, scalability approaches, consistency levels, language support, and schema evolution strategies.

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Advice on CouchDB, EdgeDB

Gabriel

CEO at Naologic

Jan 2, 2020

Decidedon

CouchDB

Couchbase

Memcached

We implemented our first large scale EPR application from naologic.com using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

592k views592k

Comments

Detailed Comparison

CouchDB	EdgeDB
Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.	An object-relational database that stores and describes the data as strongly typed objects and relationships between them.
Terrific single-node database; Clustered database ; HTTP/JSON; Offline first data sync	Strict, strongly typed schema; Powerful and clean query language; Ability to easily work with complex hierarchical data; Built-in support for schema migrations
Statistics
GitHub Stars 6.7K	GitHub Stars -
GitHub Forks 1.1K	GitHub Forks -
Stacks 529	Stacks 17
Followers 584	Followers 52
Votes 139	Votes 0
Pros & Cons
Pros 43 JSON 30 Open source 18 Highly available 12 Partition tolerant 11 Eventual consistency	No community feedback yet
Integrations
No integrations available	GraphQL Python

What are some alternatives to CouchDB, EdgeDB?

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

CouchDB vs EdgeDB: What are the differences?

Data Model: CouchDB is a NoSQL database that uses a document-oriented data model, storing data in JSON documents. On the other hand, EdgeDB is a relational database that uses a schema-based data model, enforcing data integrity through relationships and constraints.
Query Language: CouchDB uses MapReduce for querying data, which can sometimes be complex to write and understand. In contrast, EdgeDB uses a more intuitive and SQL-like query language that allows for easier data retrieval and manipulation.
Scalability: CouchDB follows a distributed design, making it suitable for horizontal scalability and high availability. EdgeDB, on the other hand, is designed for vertical scalability, emphasizing performance through efficient data storage and retrieval.
Consistency: CouchDB offers eventual consistency by default, where updates may take time to propagate across replicas. In EdgeDB, strong consistency is maintained, ensuring that all queries see the same data at any given time.
Language Support: While CouchDB can be accessed through various programming languages using HTTP REST APIs, EdgeDB offers native support for Python, allowing for seamless integration and data manipulation within Python applications.
Schema Evolution: In CouchDB, schema evolution is more flexible as documents can have different structures. In EdgeDB, schema changes require migrations to maintain data integrity, making it more strict and controlled in terms of schema evolution.

In Summary, CouchDB and EdgeDB differ in terms of their data models, query languages, scalability approaches, consistency levels, language support, and schema evolution strategies.

CouchDB vs EdgeDB

Overview