DBMS > Google BigQuery vs. HugeGraph vs. NSDb vs. OrigoDB

System Properties Comparison Google BigQuery vs. HugeGraph vs. NSDb vs. OrigoDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Graph DBMS

Time Series DBMS

Document store
Object oriented DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

nsdb.io

origodb.com

Technical documentation

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

nsdb.io/Architecture

origodb.com/docs

Developer

Google

Baidu

Robert Friberg et al

Initial release

2010

2018

2017

2009

under the name LiveDB

Current release

0.9

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Scala

Server operating systems

hosted

Linux
macOS
Unix

Linux
macOS

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

also supports composite index and range index

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

gRPC
HTTP REST
WebSocket

.NET Client API
HTTP API
LINQ

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

Java
Scala

.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

asynchronous Gremlin script jobs

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

yes

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

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

depending on model

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

Write ahead log

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

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

Role based authorization

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