DBMS > Dgraph vs. Drizzle vs. Google BigQuery vs. NuoDB vs. Percona Server for MongoDB

System Properties Comparison Dgraph vs. Drizzle vs. Google BigQuery vs. NuoDB vs. Percona Server for MongoDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Distributed and scalable native Graph DBMS

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Large scale data warehouse service with append-only tables

NuoDB is a webscale distributed database that supports SQL and ACID transactions

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Primary database model

Graph DBMS

Relational DBMS

Document store

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	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Website

dgraph.io

cloud.google.com/bigquery

www.3ds.com/nuodb-distributed-sql-database

www.percona.com/mongodb/software/percona-server-for-mongodb

Technical documentation

dgraph.io/docs

cloud.google.com/bigquery/docs

doc.nuodb.com

docs.percona.com/percona-distribution-for-mongodb

Developer

Dgraph Labs, Inc.

Drizzle project, originally started by Brian Aker

Google

Dassault Systèmes

originally NuoDB, Inc.

Percona

Initial release

2016

2008

2010

2013

2015

Current release

7.2.4, September 2012

3.4.10-2.10, November 2017

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GNU GPL

commercial

limited edition free

Open Source

GPL Version 2

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++

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

schema-free

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

with proprietary extensions

yes

APIs and other access methods

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

JDBC

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

proprietary protocol using JSON

Supported programming languages

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

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, SQL

JavaScript

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

data is dynamically stored/cached on the nodes where it is read/written

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

Multi-source replication
Source-replica replication

yes

Managed transparently by NuoDB

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Temporary table

yes

via In-Memory Engine

User concepts

Access control

Planned for future releases

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

Google Cloud Identity & Access Management (IAM)

Standard SQL roles/ privileges, Administrative Users

Access rights for users and roles

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