DBMS > Dgraph vs. Google BigQuery vs. HugeGraph vs. Kinetica vs. Lovefield

System Properties Comparison Dgraph vs. Google BigQuery vs. HugeGraph vs. Kinetica vs. Lovefield

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

Fully vectorized database across both GPUs and CPUs

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

dgraph.io

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

www.kinetica.com

google.github.io/lovefield

Technical documentation

dgraph.io/docs

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

docs.kinetica.com

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

Developer

Dgraph Labs, Inc.

Google

Baidu

Kinetica

Google

Initial release

2016

2010

2018

2012

2014

Current release

0.9

7.1, August 2021

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

commercial

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

Java

C, C++

JavaScript

Server operating systems

Linux
OS X
Windows

hosted

Linux
macOS
Unix

Linux

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

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

C++
Java
JavaScript (Node.js)
Python

JavaScript

Server-side scripts

Stored procedures

user defined functions

in JavaScript

asynchronous Gremlin script jobs

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

yes

none

yes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes

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

Source-replica replication

none

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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

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

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

using MemoryDB

User concepts

Access control

Planned for future releases

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

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

Access rights for users and roles on table level

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