DBMS > Dgraph vs. GridGain vs. Lovefield vs. Yaacomo

System Properties Comparison Dgraph vs. GridGain vs. Lovefield vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Distributed and scalable native Graph DBMS

GridGain is an in-memory computing platform, built on Apache Ignite

Embeddable relational database for web apps written in pure JavaScript

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Graph DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

dgraph.io

www.gridgain.com

google.github.io/lovefield

yaacomo.com

Technical documentation

dgraph.io/docs

www.gridgain.com/docs/index.html

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

Developer

Dgraph Labs, Inc.

GridGain Systems, Inc.

Google

Q2WEB GmbH

Initial release

2016

2007

2014

2009

Current release

GridGain 8.5.1

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

JavaScript

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

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

tested with Chrome, Firefox, IE, Safari

Android
Linux
Windows

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

JavaScript

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Triggers

yes (cache interceptors and events)

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes (replicated cache)

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

yes

User concepts

Access control

Planned for future releases

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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