DBMS > Infobright vs. Kinetica vs. LevelDB vs. Microsoft Azure Cosmos DB vs. Sphinx

System Properties Comparison Infobright vs. Kinetica vs. LevelDB vs. Microsoft Azure Cosmos DB vs. Sphinx

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

Globally distributed, horizontally scalable, multi-model database service

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial 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	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

ignitetech.com/softwarelibrary/infobrightdb

www.kinetica.com

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

sphinxsearch.com

Technical documentation

docs.kinetica.com

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

learn.microsoft.com/azure/cosmos-db

sphinxsearch.com/docs

Developer

Ignite Technologies Inc.; formerly InfoBright Inc.

Kinetica

Google

Microsoft

Sphinx Technologies Inc.

Initial release

2005

2012

2011

2014

2001

Current release

7.1, August 2021

1.23, February 2021

3.5.1, February 2023

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

commercial

Open Source

GPL version 2, commercial licence available

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

Linux
Windows

Linux

Illumos
Linux
OS X
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON 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

Knowledge Grid Technology used instead

yes

All properties auto-indexed by default

yes

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

JavaScript

Triggers

yes

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

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

The original contents of fields are not stored in the Sphinx index.

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

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

Access rights can be defined down to the item level

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