DBMS > Apache IoTDB vs. Kinetica vs. Lovefield vs. Quasardb vs. SQLite

System Properties Comparison Apache IoTDB vs. Kinetica vs. Lovefield vs. Quasardb vs. SQLite

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Fully vectorized database across both GPUs and CPUs

Embeddable relational database for web apps written in pure JavaScript

Distributed, high-performance timeseries database

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

iotdb.apache.org

www.kinetica.com

google.github.io/lovefield

quasar.ai

www.sqlite.org

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.kinetica.com

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

doc.quasar.ai/master

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Kinetica

Google

quasardb

Dwayne Richard Hipp

Initial release

2018

2012

2014

2009

2000

Current release

1.1.0, April 2023

7.1, August 2021

2.1.12, February 2017

3.14.1, January 2024

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

commercial

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

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

JavaScript

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

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

tested with Chrome, Firefox, IE, Safari

BSD
Linux
OS X
Windows

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
Native API

JDBC
ODBC
RESTful HTTP API

HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C++
Java
JavaScript (Node.js)
Python

JavaScript

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

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

yes

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

none

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

none

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on configuration

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

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

GPU vRAM or System RAM

yes

using MemoryDB

yes

Transient mode

yes

User concepts

Access control

yes

Access rights for users and roles on table level

Cryptographically strong user authentication and audit trail

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