DBMS > Apache Kylin vs. Infobright vs. LevelDB vs. PostgreSQL vs. SWC-DB

System Properties Comparison Apache Kylin vs. Infobright vs. LevelDB vs. PostgreSQL vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

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

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

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Key-value store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Wide column store

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

Time Series DBMS

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

kylin.apache.org

ignitetech.com/softwarelibrary/infobrightdb

github.com/google/leveldb

www.postgresql.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

kylin.apache.org/docs

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

www.postgresql.org/docs

Developer

Apache Software Foundation, originally contributed from eBay Inc

Ignite Technologies Inc.; formerly InfoBright Inc.

Google

PostgreSQL Global Development Group

www.postgresql.org/developer

Alex Kashirin

Initial release

2015

2005

2011

1989

1989: Postgres, 1996: PostgreSQL

2020

Current release

3.1.0, July 2020

1.23, February 2021

16.3, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

Open Source

BSD

Open Source

BSD

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

Linux

Linux
Windows

Illumos
Linux
OS X
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

standard with numerous extensions

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Proprietary protocol
Thrift

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C++

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

partitioning by range, list and (since PostgreSQL 11) by hash

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

User concepts

Access control

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

More information provided by the system vendor

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