DBMS > Amazon Redshift vs. Microsoft Azure SQL Database vs. PlanetScale vs. Titan vs. Tkrzw

System Properties Comparison Amazon Redshift vs. Microsoft Azure SQL Database vs. PlanetScale vs. Titan vs. Tkrzw

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Large scale data warehouse service for use with business intelligence tools

Database as a Service offering with high compatibility to Microsoft SQL Server

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Titan is a Graph DBMS optimized for distributed clusters.

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Graph DBMS

Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

aws.amazon.com/redshift

azure.microsoft.com/en-us/products/azure-sql/database

planetscale.com

github.com/thinkaurelius/titan

dbmx.net/tkrzw

Technical documentation

docs.aws.amazon.com/redshift

docs.microsoft.com/en-us/azure/azure-sql

planetscale.com/docs

github.com/thinkaurelius/titan/wiki

Developer

Amazon (based on PostgreSQL)

Microsoft

PlanetScale

Aurelius, owned by DataStax

Mikio Hirabayashi

Initial release

2012

2010

2020

2012

2020

Current release

V12

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache license, version 2.0

Open Source

Apache Version 2.0

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++

Java

C++

Server operating systems

hosted

Docker
Linux
macOS

Linux
OS X
Unix
Windows

Linux
macOS

Data scheme

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

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

All languages supporting JDBC/ODBC

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Clojure
Java
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

Transact SQL

yes

proprietary syntax

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

via pluggable storage backends

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, with always 3 replicas available

Multi-source replication
Source-replica replication

yes

none

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

Eventual Consistency across shards
Immediate Consistency within a shard

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

not for MyISAM storage engine

yes

Relationships in graph

Transaction concepts

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

ACID

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

User authentification and security via Rexster Graph Server

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