ArangoDB vs EdgeDB

Overview

ArangoDB

Stacks273

Followers442

Votes192

EdgeDB

Stacks17

Followers52

Votes0

ArangoDB vs EdgeDB: What are the differences?

Introduction

ArangoDB and EdgeDB are two different database management systems with distinct features and functionalities. Here are the key differences between them:

Query Language: ArangoDB uses a query language called AQL (ArangoDB Query Language), which is similar to SQL and allows users to perform complex queries and aggregations. On the other hand, EdgeDB uses a different query language called EdgeQL, which is specifically designed for working with graph and relational data. EdgeQL allows for powerful navigation and manipulation of connected data.
Data Modeling: ArangoDB is a flexible multi-model database that allows users to store and query different types of data, including documents, key-value pairs, and graphs, within a single database instance. EdgeDB, on the other hand, is primarily focused on graph data modeling and provides strong support for creating complex graph schemas and querying graph data efficiently.
Consistency Model: ArangoDB offers multiple consistency models, including strong consistency, causal consistency, and eventual consistency, giving users the flexibility to choose the appropriate level of consistency for their applications. EdgeDB, on the other hand, provides strong consistency by default, ensuring that data is always in a consistent state across the database.
Schema Evolution: ArangoDB supports schema-less and schema-full modes, allowing users to dynamically define and modify data schema as and when needed. In schema-full mode, ArangoDB enforces a schema on the data, providing data validation and easy indexing. EdgeDB, on the other hand, uses a schema-first approach, where the schema is defined upfront and any changes to the schema require explicit migration steps.
Scaling: ArangoDB provides horizontal scaling through sharding and replication, allowing users to distribute data across multiple nodes and handle large amounts of data and traffic efficiently. EdgeDB, on the other hand, currently supports standalone deployments but is actively working on adding support for distributed deployments to enable horizontal scaling.
Community and Ecosystem: ArangoDB has a larger and more mature community and ecosystem, with a wide range of community-contributed tools, libraries, and integrations. It has been around for a longer time and has gained popularity among developers and organizations. EdgeDB, on the other hand, is a relatively newer database and is still growing its community and ecosystem.

In summary, ArangoDB and EdgeDB differ in their query languages, data modeling capabilities, consistency models, schema evolution approaches, scaling options, and community ecosystems.

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CLI (Node.js)

Manual

Detailed Comparison

ArangoDB	EdgeDB
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.	An object-relational database that stores and describes the data as strongly typed objects and relationships between them.
multi-model nosql db; acid; transactions; javascript; database; nosql; sharding; replication; query language; joins; aql; documents; graphs; key-values; graphdb	Strict, strongly typed schema; Powerful and clean query language; Ability to easily work with complex hierarchical data; Built-in support for schema migrations
Statistics
Stacks 273	Stacks 17
Followers 442	Followers 52
Votes 192	Votes 0
Pros & Cons
Pros 37 Grahps and documents in one DB 26 Intuitive and rich query language 25 Good documentation 25 Open source 21 Joins for collections Cons 3 Web ui has still room for improvement 2 No support for blueprints standard, using custom AQL	No community feedback yet
Integrations
No integrations available	GraphQL Python

What are some alternatives to ArangoDB, 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.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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ArangoDB vs EdgeDB: What are the differences?

Introduction

ArangoDB and EdgeDB are two different database management systems with distinct features and functionalities. Here are the key differences between them:

Query Language: ArangoDB uses a query language called AQL (ArangoDB Query Language), which is similar to SQL and allows users to perform complex queries and aggregations. On the other hand, EdgeDB uses a different query language called EdgeQL, which is specifically designed for working with graph and relational data. EdgeQL allows for powerful navigation and manipulation of connected data.
Data Modeling: ArangoDB is a flexible multi-model database that allows users to store and query different types of data, including documents, key-value pairs, and graphs, within a single database instance. EdgeDB, on the other hand, is primarily focused on graph data modeling and provides strong support for creating complex graph schemas and querying graph data efficiently.
Consistency Model: ArangoDB offers multiple consistency models, including strong consistency, causal consistency, and eventual consistency, giving users the flexibility to choose the appropriate level of consistency for their applications. EdgeDB, on the other hand, provides strong consistency by default, ensuring that data is always in a consistent state across the database.
Schema Evolution: ArangoDB supports schema-less and schema-full modes, allowing users to dynamically define and modify data schema as and when needed. In schema-full mode, ArangoDB enforces a schema on the data, providing data validation and easy indexing. EdgeDB, on the other hand, uses a schema-first approach, where the schema is defined upfront and any changes to the schema require explicit migration steps.
Scaling: ArangoDB provides horizontal scaling through sharding and replication, allowing users to distribute data across multiple nodes and handle large amounts of data and traffic efficiently. EdgeDB, on the other hand, currently supports standalone deployments but is actively working on adding support for distributed deployments to enable horizontal scaling.
Community and Ecosystem: ArangoDB has a larger and more mature community and ecosystem, with a wide range of community-contributed tools, libraries, and integrations. It has been around for a longer time and has gained popularity among developers and organizations. EdgeDB, on the other hand, is a relatively newer database and is still growing its community and ecosystem.

In summary, ArangoDB and EdgeDB differ in their query languages, data modeling capabilities, consistency models, schema evolution approaches, scaling options, and community ecosystems.