DBMS > Amazon Redshift vs. Google Cloud Spanner vs. H2 vs. IBM Db2 Event Store vs. NuoDB

System Properties Comparison Amazon Redshift vs. Google Cloud Spanner vs. H2 vs. IBM Db2 Event Store vs. NuoDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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.

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

Distributed Event Store optimized for Internet of Things use cases

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Website

aws.amazon.com/redshift

cloud.google.com/spanner

www.h2database.com

www.ibm.com/products/db2-event-store

www.3ds.com/nuodb-distributed-sql-database

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/spanner/docs

www.h2database.com/html/main.html

www.ibm.com/docs/en/db2-event-store

doc.nuodb.com

Developer

Amazon (based on PostgreSQL)

Google

Thomas Mueller

IBM

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2012

2017

2005

2017

2013

Current release

2.2.220, July 2023

2.0

License

Commercial or Open Source

commercial

Open Source

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

commercial

free developer edition available

commercial

limited edition free

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

Java

C and C++

C++

Server operating systems

hosted

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Query statements complying to ANSI 2011

yes

through the embedded Spark runtime

yes

APIs and other access methods

JDBC
ODBC

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

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript (Node.js)
Python

Java

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

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

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures and User-Defined Functions

yes

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

data is dynamically stored/cached on the nodes where it is read/written

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication with 3 replicas for regional instances.

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

Active-active shard replication

yes

Managed transparently by NuoDB

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

No - written data is immutable

yes

MVCC

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

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

yes

Temporary table

User concepts

Access control

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

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