DBMS > Amazon DynamoDB vs. GBase vs. HarperDB vs. Newts vs. PlanetScale

System Properties Comparison Amazon DynamoDB vs. GBase vs. HarperDB vs. Newts vs. PlanetScale

Editorial information provided by DB-Engines

Name

Description

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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Time Series DBMS based on Cassandra

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Primary database model

Document store
Key-value store

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	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Website

aws.amazon.com/dynamodb

www.gbase.cn

www.harperdb.io

opennms.github.io/newts

planetscale.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.harperdb.io/docs

github.com/OpenNMS/newts/wiki

planetscale.com/docs

Developer

Amazon

General Data Technology Co., Ltd.

HarperDB

OpenNMS Group

PlanetScale

Initial release

2012

2004

2017

2014

2020

Current release

GBase 8a, GBase 8s, GBase 8c

3.1, August 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free community edition available

Open Source

Apache 2.0

commercial

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

C, Java, Python

Node.js

Java

Server operating systems

hosted

Linux

Linux
OS X

Linux
OS X
Windows

Docker
Linux
macOS

Data scheme

schema-free

yes

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

Standard with numerous extensions

SQL-like data manipulation statements

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP REST
Java API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

user defined functions

Custom Functions

since release 3.1

yes

proprietary syntax

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

A table resides as a whole on one (or more) nodes in a cluster

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

selectable replication factor

based on Cassandra

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)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

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

Atomic execution of specific operations

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

yes, using LMDB

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)

yes

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

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