DBMS > Apache Doris vs. Cassandra vs. JanusGraph vs. Microsoft SQL Server vs. Trino

System Properties Comparison Apache Doris vs. Cassandra vs. JanusGraph vs. Microsoft SQL Server vs. Trino

Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

A Graph DBMS optimized for distributed clusters

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

Microsofts flagship relational DBMS

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Secondary database models

Vector DBMS

starting with release V5

Document store
Graph DBMS
Spatial DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	5.00
Rank	#66	Overall
	#36	Relational DBMS

Website

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

cassandra.apache.org

janusgraph.org

www.microsoft.com/en-us/sql-server

trino.io

Technical documentation

github.com/apache/doris/wiki

cassandra.apache.org/doc/latest

docs.janusgraph.org

learn.microsoft.com/en-US/sql/sql-server

trino.io/broadcast
trino.io/docs/current

Social network pages

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation

Apache top level project, originally developped by Facebook

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Trino Software Foundation

Initial release

2017

2008

2017

1989

2012

2020 rebranded from PrestoSQL

Current release

1.2.2, February 2023

4.1.3, July 2023

0.6.3, February 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Java

Server operating systems

Linux

BSD
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
Windows

Linux
macOS

for devlopment

Data scheme

yes

schema-free

yes

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

yes

restricted

only equality queries, not always the best performing solution

yes

depending on connected data-source

SQL

Support of SQL

yes

SQL-like SELECT, DML and DDL statements (CQL)

yes

APIs and other access methods

JDBC
MySQL client

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

Java

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Clojure
Java
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes, depending on connected data-source

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

no "single point of failure"

yes

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

Representation of geographical distribution of servers is possible

yes

yes, but depending on the SQL-Server Edition

depending on connected data-source

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
Immediate Consistency

can be individually decided for each write operation

Eventual Consistency
Immediate Consistency

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

depending on connected data-source

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

depending on connected data-source

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 for users can be defined per object

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

SQL standard access control

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Market metrics

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Licensing and pricing models

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