DBMS > Firebolt vs. Kinetica vs. SQLite vs. TimesTen vs. Typesense

System Properties Comparison Firebolt vs. Kinetica vs. SQLite vs. TimesTen vs. Typesense

Editorial information provided by DB-Engines

Name

Description

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Fully vectorized database across both GPUs and CPUs

Widely used embeddable, in-process RDBMS

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

Primary database model

Relational DBMS

Search engine

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.49
Rank	#148	Overall
	#68	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Score	0.80
Rank	#209	Overall
	#14	Search engines

Website

www.firebolt.io

www.kinetica.com

www.sqlite.org

www.oracle.com/database/technologies/related/timesten.html

typesense.org

Technical documentation

docs.firebolt.io

docs.kinetica.com

www.sqlite.org/docs.html

docs.oracle.com/en/database/other-databases/timesten/index.html

typesense.org/docs

Developer

Firebolt Analytics Inc.

Kinetica

Dwayne Richard Hipp

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2020

2012

2000

1998

2015

Current release

7.1, August 2021

3.46.1 (13 August 2024), August 2024

Release 22.1

License

Commercial or Open Source

commercial

Open Source

Public Domain

commercial

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, C++

C++

Server operating systems

hosted

Linux

server-less

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Linux

Data scheme

yes

dynamic column types

yes

schema-free

pre-defined schema optional

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 DML and DDL statements

yes

SQL-92 is not fully supported

yes

APIs and other access methods

.Net
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

RESTful HTTP API

Supported programming languages

Go
JavaScript (Node.js)
Python

C++
Java
JavaScript (Node.js)
Python

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

C
C++
Java
Node.js
PL/SQL
Python

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Server-side scripts

Stored procedures

user defined functions

PL/SQL

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

Source-replica replication

none

Multi-source replication
Source-replica replication

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

User concepts

Access control

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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