DBMS > Amazon SimpleDB vs. EXASOL vs. Google Cloud Spanner vs. GreptimeDB vs. MySQL

System Properties Comparison Amazon SimpleDB vs. EXASOL vs. Google Cloud Spanner vs. GreptimeDB vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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.

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	0.12
Rank	#351	Overall
	#34	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/simpledb

www.exasol.com

cloud.google.com/spanner

greptime.com

www.mysql.com

Technical documentation

docs.aws.amazon.com/simpledb

www.exasol.com/resources

cloud.google.com/spanner/docs

docs.greptime.com

dev.mysql.com/doc

Developer

Amazon

Exasol

Google

Greptime Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2007

2000

2017

2022

1995

Current release

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Rust

C and C++

Server operating systems

hosted

Android
Docker
FreeBSD
Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free, schema definition possible

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

All columns are indexed automatically

yes

SQL

Support of SQL

yes

Query statements complying to ANSI 2011

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

gRPC
HTTP API
JDBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

Java
Lua
Python
R

Go
Java
JavaScript (Node.js)
Python

C++
Erlang
Go
Java
JavaScript

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

user defined functions

Python

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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

Concurrent data updates can be detected by the application

ACID

Strict serializable isolation

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

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 and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users, groups and roles according to SQL-standard

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

Simple rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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