DBMS > Infobright vs. Kinetica vs. LevelDB vs. PostGIS vs. Sadas Engine

System Properties Comparison Infobright vs. Kinetica vs. LevelDB vs. PostGIS vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Fully vectorized database across both GPUs and CPUs

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Spatial extension of PostgreSQL

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

ignitetech.com/softwarelibrary/infobrightdb

www.kinetica.com

github.com/google/leveldb

postgis.net

www.sadasengine.com

Technical documentation

docs.kinetica.com

github.com/google/leveldb/blob/main/doc/index.md

postgis.net/documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Ignite Technologies Inc.; formerly InfoBright Inc.

Kinetica

Google

SADAS s.r.l.

Initial release

2005

2012

2011

2005

2006

Current release

7.1, August 2021

1.23, February 2021

3.4.2, February 2024

8.0

License

Commercial or Open Source

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

Open Source

BSD

Open Source

GPL v2.0

commercial

free trial version available

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, C++

C++

Server operating systems

Linux
Windows

Linux

Illumos
Linux
OS X
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Proprietary protocol

Supported programming languages

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

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

none

Sharding

none

yes

based on PostgreSQL

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

based on PostgreSQL

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Durability

Support for making data persistent

yes

with automatic compression on writes

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

Access rights for users and roles on table level

yes

based on PostgreSQL

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

show all

Recent citations in the news

Kinetica Elevates RAG with Fast Access to Real-Time Data
26 March 2024, Datanami

Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica Launches Generative AI Solution for Real-Time Inferencing Powered by NVIDIA AI Enterprise
18 March 2024, GlobeNewswire

Kinetica Delivers Real-Time Vector Similarity Search
20 March 2024, Datanami

Transforming spatiotemporal data analysis with GPUs and generative AI
30 October 2023, InfoWorld

provided by Google News

LevelDB in Ruby — SitePoint
22 October 2014, SitePoint

Pliops unveils XDP-Rocks for RocksDB – Blocks and Files
19 October 2022, Blocks & Files

Microsoft Teams stores auth tokens as cleartext in Windows, Linux, Macs
14 September 2022, BleepingComputer

XanMod, Liquorix Kernels Offer Some Advantages On AMD Ryzen 5 Notebook
26 July 2021, Phoronix

RocksDB - Facebook's Database Now Open Source
21 November 2013, iProgrammer

provided by Google News

Share this page