DBMS > Google Cloud Spanner vs. Microsoft Azure Cosmos DB vs. MonetDB vs. RisingWave

System Properties Comparison Google Cloud Spanner vs. Microsoft Azure Cosmos DB vs. MonetDB vs. RisingWave

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Editorial information provided by DB-Engines

Name

Description

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

A relational database management system that stores data in columns

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

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

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.58
Rank	#242	Overall
	#111	Relational DBMS

Website

cloud.google.com/spanner

azure.microsoft.com/services/cosmos-db

www.monetdb.org

www.risingwave.com/database

Technical documentation

cloud.google.com/spanner/docs

learn.microsoft.com/azure/cosmos-db

www.monetdb.org/Documentation

docs.risingwave.com/docs/current/intro

Developer

Google

Microsoft

MonetDB BV

RisingWave Labs

Initial release

2017

2014

2004

2022

Current release

Dec2023 (11.49), December 2023

1.2, September 2023

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 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

Rust

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

Standard SQL-types and JSON

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

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

SQL-like query language

yes

SQL 2003 with some extensions

yes

APIs and other access methods

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
PostgreSQL wire protocol

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

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

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

JavaScript

yes, in SQL, C, R

UDFs in Python or Java

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

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

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

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

Users and Roles

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