DBMS > Amazon DynamoDB vs. CouchDB vs. DolphinDB vs. EsgynDB vs. Google Cloud Spanner

System Properties Comparison Amazon DynamoDB vs. CouchDB vs. DolphinDB vs. EsgynDB vs. Google Cloud Spanner

Editorial information provided by DB-Engines

Name

Description

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

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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.

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Relational 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	8.30
Rank	#47	Overall
	#7	Document stores

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Website

aws.amazon.com/dynamodb

couchdb.apache.org

www.dolphindb.com

www.esgyn.cn

cloud.google.com/spanner

Technical documentation

docs.aws.amazon.com/dynamodb

docs.couchdb.org/en/stable

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

cloud.google.com/spanner/docs

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

DolphinDB, Inc

Esgyn

Google

Initial release

2012

2005

2018

2015

2017

Current release

3.3.3, December 2023

v2.00.4, January 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache version 2

commercial

free community version available

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

Erlang

C++

C++, Java

Server operating systems

hosted

Android
BSD
Linux
OS X
Solaris
Windows

Linux
Windows

Linux

hosted

Data scheme

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

SQL-like query language

yes

Query statements complying to ANSI 2011

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

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

ADO.NET
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

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

All languages supporting JDBC/ODBC/ADO.Net

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

View functions in JavaScript

yes

Java Stored Procedures

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Multi-source replication between multi datacenters

Multi-source replication with 3 replicas for regional instances.

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

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

atomic operations within a single document possible

yes

ACID

Strict serializable isolation

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

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 can be defined per database

Administrators, Users, Groups

fine grained access rights according to SQL-standard

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

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