DBMS > BaseX vs. Google BigQuery vs. Oracle NoSQL vs. TinkerGraph

System Properties Comparison BaseX vs. Google BigQuery vs. Oracle NoSQL vs. TinkerGraph

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Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Large scale data warehouse service with append-only tables

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Native XML DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS

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

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

basex.org

cloud.google.com/bigquery

www.oracle.com/database/nosql/technologies/nosql

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

docs.basex.org

cloud.google.com/bigquery/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

BaseX GmbH

Google

Oracle

Initial release

2007

2010

2011

2009

Current release

10.7, August 2023

23.3, December 2023

License

Commercial or Open Source

Open Source

BSD license

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Apache 2.0

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

Server operating systems

Linux
OS X
Windows

hosted

Linux
Solaris SPARC/x86

Data scheme

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

XQuery supports types

yes

optional

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

yes

SQL-like DML and DDL statements

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

RESTful HTTP/JSON API

RESTful HTTP API

TinkerPop 3

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
Go
Java
JavaScript (Node.js)
Python

Groovy
Java

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Triggers

yes

via events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

multiple readers, single writer

Since BigQuery is designed for querying data

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

off heap cache

yes

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

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