DBMS > JaguarDB vs. Lovefield vs. Quasardb vs. SQream DB

System Properties Comparison JaguarDB vs. Lovefield vs. Quasardb vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

Embeddable relational database for web apps written in pure JavaScript

Distributed, high-performance timeseries database

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Key-value store
Vector DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.00
Rank	#385	Overall
	#61	Key-value stores
	#16	Vector DBMS

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	0.62
Rank	#234	Overall
	#109	Relational DBMS

Website

www.jaguardb.com

google.github.io/lovefield

quasar.ai

sqream.com

Technical documentation

www.jaguardb.com/support.html

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

doc.quasar.ai/master

docs.sqream.com

Developer

DataJaguar, Inc.

Google

quasardb

SQream Technologies

Initial release

2015

2014

2009

2017

Current release

3.3 July 2023

2.1.12, February 2017

3.14.1, January 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

GPL V3.0

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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++

the server part. Clients available in other languages

JavaScript

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

BSD
Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

yes, ANSI Standard SQL Types

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

with tags

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

via JavaScript builder pattern

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

HTTP API

.Net
JDBC
ODBC

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

JavaScript

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions in Python

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

consistent hashing

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

Durability

Support for making data persistent

yes

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

yes

by using LevelDB

yes

In-memory capabilities

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

yes

using MemoryDB

yes

Transient mode

User concepts

Access control

rights management via user accounts

Cryptographically strong user authentication and audit trail

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