DBMS > Heroic vs. Informix vs. Linter vs. OushuDB vs. Postgres-XL

System Properties Comparison Heroic vs. Informix vs. Linter vs. OushuDB vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A secure embeddable database from IBM, positioned besides IBM Db2 as a relatively low-cost product optimized for OLTP and Internet of Things data

RDBMS for high security requirements

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Time Series DBMS

Relational DBMS

Since Version 12.10 support for JSON/BSON datatypes compatible with MongoDB

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS

with Informix TimeSeries Extension

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	17.12
Rank	#34	Overall
	#21	Relational DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

github.com/spotify/heroic

www.ibm.com/products/informix

linter.ru

www.oushu.com/product/oushuDB

www.postgres-xl.org

Technical documentation

spotify.github.io/heroic

informix.hcldoc.com
www.ibm.com/support/knowledgecenter/SSGU8G/welcomeIfxServers.html

www.oushu.com/documentation

www.postgres-xl.org/documentation

Developer

Spotify

IBM, HCL Technologies

Effective May 1st, 2017, HCL took on development, technical support, and product management teams, and works jointly with IBM on product strategy, marketing, and sales.

relex.ru

Oushu

Initial release

2014

1984

1990

2014

since 2012, originally named StormDB

Current release

14.10.FC5, November 2020

4.0.1, August 2020

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free developer edition available

commercial

Open Source

Mozilla public license

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

Java

C, C++ and Java

C and C++

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux

Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Since Version 12.10 support for JSON/BSON datatypes

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

XML type, but no XML query functionality

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

yes

distributed, parallel query execution

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
JSON API

MongoDB compatible
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

JDBC
ODBC

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

Supported programming languages

.Net
C
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
C++

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Users with fine-grained authentication, authorization, and auditing controls

fine grained access rights according to SQL-standard

Kerberos, SSL and role based access

fine grained access rights according to SQL-standard

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