DBMS > Amazon DynamoDB vs. MySQL vs. Postgres-XL vs. searchxml vs. Splice Machine

System Properties Comparison Amazon DynamoDB vs. MySQL vs. Postgres-XL vs. searchxml vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Widely used open source RDBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

DBMS for structured and unstructured content wrapped with an application server

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Key-value store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

aws.amazon.com/dynamodb

www.mysql.com

www.postgres-xl.org

www.searchxml.net/category/products

splicemachine.com

Technical documentation

docs.aws.amazon.com/dynamodb

dev.mysql.com/doc

www.postgres-xl.org/documentation

www.searchxml.net/support/handouts

splicemachine.com/how-it-works

Developer

Amazon

Oracle

since 2010, originally MySQL AB, then Sun

informationpartners gmbh

Splice Machine

Initial release

2012

1995

2014

since 2012, originally named StormDB

2015

2014

Current release

8.4.0, April 2024

10 R1, October 2018

1.0

3.1, March 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Mozilla public license

commercial

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

C++

Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

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

XML type, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

distributed, parallel query execution

yes

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C++

most other programming languages supported via APIs

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions

yes

on the application server

yes

Java

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

sychronisation to multiple collections

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Yes, via Full Spark Integration

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

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

not for MyISAM storage engine

ACID

MVCC

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

yes, multi-version concurrency control (MVCC)

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)

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

Domain, group and role-based access control at the document level and for application services

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

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