DBMS > Dgraph vs. Google Cloud Spanner vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server vs. VelocityDB

System Properties Comparison Dgraph vs. Google Cloud Spanner vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Globally distributed, horizontally scalable, multi-model database service

Microsofts flagship relational DBMS

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Graph DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS
Object oriented 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	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

dgraph.io

cloud.google.com/spanner

azure.microsoft.com/services/cosmos-db

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

velocitydb.com

Technical documentation

dgraph.io/docs

cloud.google.com/spanner/docs

learn.microsoft.com/azure/cosmos-db

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

velocitydb.com/UserGuide

Developer

Dgraph Labs, Inc.

Google

Microsoft

VelocityDB Inc

Initial release

2016

2017

2014

1989

2011

Current release

SQL Server 2022, November 2022

7.x

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
Windows

Any that supports .NET

Data scheme

schema-free

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

Query statements complying to ANSI 2011

SQL-like query language

yes

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

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

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

.Net

Supported programming languages

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

Go
Java
JavaScript (Node.js)
Python

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

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

.Net

Server-side scripts

Stored procedures

JavaScript

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

Triggers

JavaScript

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Implicit feature of the cloud service

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication with 3 replicas for regional instances.

yes

Implicit feature of the cloud service

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

Multi-item ACID transactions with snapshot isolation within a partition

ACID

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

Planned for future releases

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Based on Windows Authentication

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