DBMS > HEAVY.AI vs. Lovefield vs. PlanetScale vs. SQLite vs. TerminusDB

System Properties Comparison HEAVY.AI vs. Lovefield vs. PlanetScale vs. SQLite vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

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

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Widely used embeddable, in-process RDBMS

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

Document store
RDF store

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

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

google.github.io/lovefield

planetscale.com

www.sqlite.org

terminusdb.com

Technical documentation

docs.heavy.ai

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

planetscale.com/docs

www.sqlite.org/docs.html

terminusdb.github.io/terminusdb/#

Developer

HEAVY.AI, Inc.

Google

PlanetScale

Dwayne Richard Hipp

DataChemist Ltd.

Initial release

2016

2014

2020

2000

2018

Current release

5.10, January 2022

2.1.12, February 2017

3.45.3 (15 April 2024), April 2024

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

Open Source

Public Domain

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++ and CUDA

JavaScript

Prolog, Rust

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

Docker
Linux
macOS

server-less

Linux

Data scheme

yes

dynamic column types

yes

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-like query language

via JavaScript builder pattern

yes

with proprietary extensions

yes

SQL-92 is not fully supported

SQL-like query language (WOQL)

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
MySQL protocol
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

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

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

JavaScript
Python

Server-side scripts

Stored procedures

yes

proprietary syntax

yes

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Multi-source replication
Source-replica replication

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

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

yes

in-memory journaling

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Role-based access control

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