DBMS > Amazon Redshift vs. Google Cloud Spanner vs. H2 vs. Linter vs. RRDtool

System Properties Comparison Amazon Redshift vs. Google Cloud Spanner vs. H2 vs. Linter vs. RRDtool

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.

RDBMS for high security requirements

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Relational DBMS

Time Series 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	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Website

aws.amazon.com/redshift

cloud.google.com/spanner

www.h2database.com

linter.ru

oss.oetiker.ch/rrdtool

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/spanner/docs

www.h2database.com/html/main.html

oss.oetiker.ch/rrdtool/doc

Developer

Amazon (based on PostgreSQL)

Google

Thomas Mueller

relex.ru

Tobias Oetiker

Initial release

2012

2017

2005

1990

1999

Current release

2.2.220, July 2023

1.8.0, 2022

License

Commercial or Open Source

commercial

Open Source

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

commercial

Open Source

GPL V2 and FLOSS

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++

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

hosted

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

HP-UX
Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Query statements complying to ANSI 2011

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
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

in-process shared library
Pipes

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript (Node.js)
Python

Java

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures and User-Defined Functions

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

Source-replica replication

none

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

none

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

by using the rrdcached daemon

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

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

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