DBMS > Amazon Redshift vs. InfinityDB vs. Informix vs. JanusGraph vs. TDSQL for MySQL

System Properties Comparison Amazon Redshift vs. InfinityDB vs. Informix vs. JanusGraph vs. TDSQL for MySQL

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A Java embedded Key-Value Store which extends the Java Map interface

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Graph DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS

with Informix TimeSeries Extension

Document store
Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	17.12
Rank	#34	Overall
	#21	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	0.85
Rank	#207	Overall
	#97	Relational DBMS

Website

aws.amazon.com/redshift

boilerbay.com

www.ibm.com/products/informix

janusgraph.org

www.tencentcloud.com/products/dcdb

Technical documentation

docs.aws.amazon.com/redshift

boilerbay.com/infinitydb/manual

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

docs.janusgraph.org

www.tencentcloud.com/document/product/1042

Developer

Amazon (based on PostgreSQL)

Boiler Bay Inc.

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.

Linux Foundation; originally developed as Titan by Aurelius

Tencent

Initial release

2012

2002

1984

2017

2013

Current release

4.0

14.10.FC5, November 2020

0.6.3, February 2023

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache 2.0

commercial

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

Java

C, C++ and Java

Java

Server operating systems

hosted

All OS with a Java VM

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux
OS X
Unix
Windows

hosted

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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.

Secondary indexes

restricted

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
JSON API

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Java

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

Clojure
Java
Python

C
C#
C++
Java
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

manual creation possible, using inversions based on multi-value capability

yes

Relationships in graphs

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

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

User authentification and security via Rexster Graph Server

Users with fine-grained authorization concept

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