DBMS > Google Cloud Spanner vs. IBM Cloudant vs. JaguarDB vs. Lovefield vs. Microsoft SQL Server

System Properties Comparison Google Cloud Spanner vs. IBM Cloudant vs. JaguarDB vs. Lovefield vs. Microsoft SQL Server

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.

Database as a Service offering based on Apache CouchDB

Performant, highly scalable DBMS for AI and IoT applications

Embeddable relational database for web apps written in pure JavaScript

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Document store

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

cloud.google.com/spanner

www.ibm.com/products/cloudant

www.jaguardb.com

google.github.io/lovefield

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

Technical documentation

cloud.google.com/spanner/docs

cloud.ibm.com/docs/Cloudant

www.jaguardb.com/support.html

github.com/google/lovefield/blob/master/docs/spec_index.md

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

Developer

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

DataJaguar, Inc.

Google

Microsoft

Initial release

2017

2010

2015

2014

1989

Current release

3.3 July 2023

2.1.12, February 2017

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Open Source

Apache 2.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

C++

the server part. Clients available in other languages

JavaScript

C++

Server operating systems

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
Windows

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

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

via JavaScript builder pattern

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

RESTful HTTP/JSON API

JDBC
ODBC

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

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

JavaScript

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

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

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

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

Multi-source replication

none

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes

User concepts

Access control

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

Access rights for users can be defined per database

rights management via user accounts

fine grained access rights according to SQL-standard

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