DBMS > Google Cloud Spanner vs. GridDB vs. H2 vs. MySQL

System Properties Comparison Google Cloud Spanner vs. GridDB vs. H2 vs. MySQL

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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.

Scalable in-memory time series database optimized for IoT and Big Data

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Widely used open source RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Key-value store
Relational DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	2.89
Rank	#105	Overall
	#53	Relational DBMS

Score	2.02
Rank	#132	Overall
	#11	Time Series DBMS

Score	8.22
Rank	#50	Overall
	#32	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Website

cloud.google.com/spanner

griddb.net

www.h2database.com

www.mysql.com

Technical documentation

cloud.google.com/spanner/docs

docs.griddb.net

www.h2database.com/html/main.html

dev.mysql.com/doc

Developer

Google

Toshiba Corporation

Thomas Mueller

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2017

2013

2005

1995

Current release

5.1, August 2022

2.2.220, July 2023

8.3.0, January 2024

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

GPL version 2. Commercial licenses with extended functionallity are 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

C++

Java

C and C++

Server operating systems

hosted

Linux

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL92, SQL-like TQL (Toshiba Query Language)

yes

with proprietary extensions

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

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

Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication with 3 replicas for regional instances.

Source-replica replication

With clustering: 2 database servers on different computers operate on identical copies of a database

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

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

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Strict serializable isolation

ACID at container level

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

table locks or row locks depending on storage engine

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 for users can be defined per database

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.