DBMS > AgensGraph vs. DolphinDB vs. Google Cloud Datastore vs. Graph Engine vs. ToroDB

System Properties Comparison AgensGraph vs. DolphinDB vs. Google Cloud Datastore vs. Graph Engine vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Graph DBMS
Relational DBMS

Time Series DBMS

Document store

Graph DBMS
Key-value store

Document store

Secondary database models

Relational DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Website

bitnine.net/agensgraph

www.dolphindb.com

cloud.google.com/datastore

www.graphengine.io

github.com/torodb/server

Technical documentation

bitnine.net/documentation

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

cloud.google.com/datastore/docs

www.graphengine.io/docs/manual

Developer

Bitnine Global Inc.

DolphinDB, Inc

Google

Microsoft

8Kdata

Initial release

2016

2018

2008

2010

2016

Current release

2.1, December 2018

v2.00.4, January 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free community version available

commercial

Open Source

MIT License

Open Source

AGPL-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++

.NET and C

Java

Server operating systems

Linux
OS X
Windows

Linux
Windows

hosted

.NET

All OS with a Java 7 VM

Data scheme

depending on used data model

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

string, integer, double, boolean, date, object_id

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

yes

SQL-like query language

SQL-like query language (GQL)

APIs and other access methods

Cypher Query Language
JDBC

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

RESTful HTTP API

Supported programming languages

C
Java
JavaScript
Python

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

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

C#
C++
F#
Visual Basic

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

horizontal partitioning

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication using Paxos

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

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.

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

fine grained access rights according to SQL-standard

Administrators, Users, Groups

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

Access rights for users and roles

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