DBMS > DolphinDB vs. Google BigQuery vs. HugeGraph vs. Newts

System Properties Comparison DolphinDB vs. Google BigQuery vs. HugeGraph vs. Newts

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Editorial information provided by DB-Engines

Name

Description

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.

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

Time Series DBMS based on Cassandra

Primary database model

Secondary database models

Relational DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Website

www.dolphindb.com

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

opennms.github.io/newts

Technical documentation

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

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

github.com/OpenNMS/newts/wiki

Developer

DolphinDB, Inc

Google

Baidu

OpenNMS Group

Initial release

2018

2010

2018

2014

Current release

v2.00.4, January 2022

0.9

License

Commercial or Open Source

commercial

free community version available

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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

Server operating systems

Linux
Windows

hosted

Linux
macOS
Unix

Linux
OS X
Windows

Data scheme

yes

schema-free

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

also supports composite index and range index

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

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

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP REST
Java API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

Java

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

asynchronous Gremlin script jobs

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

yes

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

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

Administrators, Users, Groups

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

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