DBMS > Apache Doris vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. Splice Machine vs. Titan

System Properties Comparison Apache Doris vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. Splice Machine vs. Titan

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

An MPP-based analytics DBMS embracing the MySQL protocol

Globally distributed, horizontally scalable, multi-model database service

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

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

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

azure.microsoft.com/services/cosmos-db

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

splicemachine.com

github.com/thinkaurelius/titan

Technical documentation

github.com/apache/doris/wiki

learn.microsoft.com/azure/cosmos-db

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

splicemachine.com/how-it-works

github.com/thinkaurelius/titan/wiki

Developer

Apache Software Foundation, originally contributed from Baidu

Microsoft

Splice Machine

Aurelius, owned by DataStax

Initial release

2017

2014

2010

2014

2012

Current release

1.2.2, February 2023

V12

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache license, 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

Java

C++

Java

Server operating systems

Linux

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

JDBC
MySQL client

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

ADO.NET
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

Java

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

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Clojure
Java
Python

Server-side scripts

Stored procedures

user defined functions

JavaScript

Transact SQL

yes

Java

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

Shared Nothhing Auto-Sharding, Columnar Partitioning

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

yes, with always 3 replicas available

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Yes, via Full Spark Integration

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

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

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

User authentification and security via Rexster Graph Server

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