DBMS > Dgraph vs. HEAVY.AI vs. HugeGraph vs. Lovefield vs. Snowflake

System Properties Comparison Dgraph vs. HEAVY.AI vs. HugeGraph vs. Lovefield vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A fast-speed and highly-scalable Graph DBMS

Embeddable relational database for web apps written in pure JavaScript

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

dgraph.io

github.com/heavyai/heavydb
www.heavy.ai

github.com/hugegraph
hugegraph.apache.org

google.github.io/lovefield

www.snowflake.com

Technical documentation

dgraph.io/docs

docs.heavy.ai

hugegraph.apache.org/docs

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

docs.snowflake.net/manuals/index.html

Developer

Dgraph Labs, Inc.

HEAVY.AI, Inc.

Baidu

Google

Snowflake Computing Inc.

Initial release

2016

2018

2014

Current release

5.10, January 2022

0.9

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

Open Source

Apache 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

C++ and CUDA

Java

JavaScript

Server operating systems

Linux
OS X
Windows

Linux

Linux
macOS
Unix

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

tested with Chrome, Firefox, IE, Safari

hosted

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

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

JDBC
ODBC
Thrift
Vega

Java API
RESTful HTTP API
TinkerPop Gremlin

CLI Client
JDBC
ODBC

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

Groovy
Java
Python

JavaScript

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

user defined functions

Triggers

Using read-only observers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Round robin

yes

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

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication

yes

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

none

yes

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

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of 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

Planned for future releases

fine grained access rights according to SQL-standard

Users, roles and permissions

Users with fine-grained authorization concept, user roles and pluggable authentication

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