DBMS > HEAVY.AI vs. Lovefield vs. RethinkDB vs. SQLite vs. VictoriaMetrics

System Properties Comparison HEAVY.AI vs. Lovefield vs. RethinkDB vs. SQLite vs. VictoriaMetrics

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Widely used embeddable, in-process RDBMS

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Secondary database models

Spatial DBMS

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	2.74
Rank	#105	Overall
	#19	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

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

google.github.io/lovefield

rethinkdb.com

www.sqlite.org

victoriametrics.com

Technical documentation

docs.heavy.ai

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

rethinkdb.com/docs

www.sqlite.org/docs.html

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

HEAVY.AI, Inc.

Google

The Linux Foundation

since July 2017

Dwayne Richard Hipp

VictoriaMetrics

Initial release

2016

2014

2009

2000

2018

Current release

5.10, January 2022

2.1.12, February 2017

2.4.1, August 2020

3.45.3 (15 April 2024), April 2024

v1.91, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

Public Domain

Open Source

Apache Version 2.0

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

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

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Windows

server-less

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

not rigid because of 'dynamic typing' concept.

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

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

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

Server-side scripts

Stored procedures

Triggers

Using read-only observers

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Source-replica replication

none

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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 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

yes

users and table-level permissions

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