DBMS > Google BigQuery vs. Lovefield vs. ObjectBox vs. TigerGraph

System Properties Comparison Google BigQuery vs. Lovefield vs. ObjectBox vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Embeddable relational database for web apps written in pure JavaScript

Extremely fast embedded database for small devices, IoT and Mobile

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS

Object oriented DBMS

Graph DBMS

Secondary database models

Time Series DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

cloud.google.com/bigquery

google.github.io/lovefield

objectbox.io

www.tigergraph.com

Technical documentation

cloud.google.com/bigquery/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.objectbox.io

docs.tigergraph.com

Developer

Google

ObjectBox Limited

Initial release

2010

2014

2017

Current release

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache License 2.0

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

JavaScript

C and C++

C++

Server operating systems

hosted

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Android
iOS
Linux
macOS
Windows

Linux

Data scheme

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

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP/JSON API

Proprietary native API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

online/offline synchronization between client and server

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

Role-based access control

More information provided by the system vendor

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