DBMS > Dgraph vs. Google BigQuery vs. HEAVY.AI vs. NuoDB vs. Qdrant

System Properties Comparison Dgraph vs. Google BigQuery vs. HEAVY.AI vs. NuoDB vs. Qdrant

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Large scale data warehouse service with append-only tables

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

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

A high-performance vector database with neural network or semantic-based matching

Primary database model

Graph DBMS

Relational DBMS

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	1.39
Rank	#155	Overall
	#15	Graph DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.87
Rank	#201	Overall
	#94	Relational DBMS

Score	1.53
Rank	#145	Overall
	#7	Vector DBMS

Website

dgraph.io

cloud.google.com/bigquery

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

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

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

dgraph.io/docs

cloud.google.com/bigquery/docs

docs.heavy.ai

doc.nuodb.com

qdrant.tech/documentation

Developer

Dgraph Labs, Inc.

Google

HEAVY.AI, Inc.

Dassault Systèmes

originally NuoDB, Inc.

Qdrant

Initial release

2016

2010

2016

2013

2021

Current release

5.10, January 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

limited edition free

Open Source

Apache Version 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

C++ and CUDA

C++

Rust

Server operating systems

Linux
OS X
Windows

hosted

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Docker
Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes

APIs and other access methods

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

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

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

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

none

Sharding

Round robin

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

yes

Managed transparently by NuoDB

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency, tunable 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

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)

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

Key-based authentication

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