DBMS > Dolt vs. HEAVY.AI vs. Lovefield vs. RocksDB

System Properties Comparison Dolt vs. HEAVY.AI vs. Lovefield vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

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

Embeddable relational database for web apps written in pure JavaScript

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store

Spatial DBMS

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

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

github.com/dolthub/dolt
www.dolthub.com

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

google.github.io/lovefield

rocksdb.org

Technical documentation

docs.dolthub.com

docs.heavy.ai

github.com/google/lovefield/blob/master/docs/spec_index.md

github.com/facebook/rocksdb/wiki

Developer

DoltHub Inc

HEAVY.AI, Inc.

Google

Facebook, Inc.

Initial release

2018

2016

2014

2013

Current release

5.10, January 2022

2.1.12, February 2017

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

BSD

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

JavaScript

C++

Server operating systems

Linux
macOS
Windows

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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-like query language

via JavaScript builder pattern

APIs and other access methods

CLI Client
HTTP REST

JDBC
ODBC
Thrift
Vega

C++ API
Java API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

currently in alpha release

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication

none

yes

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

Only one user is configurable, and must be specified in the config file at startup

fine grained access rights according to SQL-standard

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