DBMS > EventStoreDB vs. HEAVY.AI vs. SQLite vs. SurrealDB

System Properties Comparison EventStoreDB vs. HEAVY.AI vs. SQLite vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

Industrial-strength, open-source database solution built from the ground up for event sourcing.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Widely used embeddable, in-process RDBMS

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Event Store

Relational DBMS

Document store
Graph DBMS

Secondary database models

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

www.eventstore.com

github.com/heavyai/heavydb
www.heavy.ai

www.sqlite.org

surrealdb.com

Technical documentation

developers.eventstore.com

docs.heavy.ai

www.sqlite.org/docs.html

surrealdb.com/docs

Developer

Event Store Limited

HEAVY.AI, Inc.

Dwayne Richard Hipp

SurrealDB Ltd

Initial release

2012

2016

2000

2022

Current release

21.2, February 2021

5.10, January 2022

3.46.0 (23 May 2024), May 2024

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Public Domain

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++ and CUDA

Rust

Server operating systems

Linux
Windows

Linux

server-less

Linux
macOS
Windows

Data scheme

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

SQL-like query language

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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