DBMS > Amazon DynamoDB vs. DolphinDB vs. Google Cloud Datastore vs. TerminusDB

System Properties Comparison Amazon DynamoDB vs. DolphinDB vs. Google Cloud Datastore vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

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

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Document store
Key-value store

Time Series DBMS

Document store

Graph DBMS

Secondary database models

Relational DBMS

Document store
RDF store

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

aws.amazon.com/dynamodb

www.dolphindb.com

cloud.google.com/datastore

terminusdb.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.dolphindb.cn/en/help200/index.html

cloud.google.com/datastore/docs

terminusdb.github.io/terminusdb/#

Developer

Amazon

DolphinDB, Inc

Google

DataChemist Ltd.

Initial release

2012

2018

2008

2018

Current release

v2.00.4, January 2022

11.0.0, January 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free community version available

commercial

Open Source

GPL V3

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++

Prolog, Rust

Server operating systems

hosted

Linux
Windows

hosted

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (GQL)

SQL-like query language (WOQL)

APIs and other access methods

RESTful HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

JavaScript
Python

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

Triggers

yes

by integration with AWS Lambda

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

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)

Administrators, Users, Groups

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

Role-based access control

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