DBMS > AntDB vs. HEAVY.AI vs. NuoDB vs. Qdrant vs. Snowflake

System Properties Comparison AntDB vs. HEAVY.AI vs. NuoDB vs. Qdrant vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A scalable, multi-tenant, MPP-architectured RDBMS for OLTP and OLAP operations, highly compatible with Oracle

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

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

Primary database model

Relational DBMS

Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.09
Rank	#342	Overall
	#149	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

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

www.asiainfo.com/en_us/product_aisware_antdb_detail.html

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

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

github.com/qdrant/qdrant
qdrant.tech

www.snowflake.com

Technical documentation

docs.heavy.ai

doc.nuodb.com

qdrant.tech/documentation

docs.snowflake.net/manuals/index.html

Developer

AsiaInfo Technologies Limited

HEAVY.AI, Inc.

Dassault Systèmes

originally NuoDB, Inc.

Qdrant

Snowflake Computing Inc.

Initial release

2016

2013

2021

2014

Current release

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

limited edition free

Open Source

Apache Version 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

C++

Rust

Server operating systems

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Docker
Linux
macOS
Windows

hosted

Data scheme

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

SQL2016 compliant

yes

APIs and other access methods

JDBC
MySQL protocol compliant
ODBC

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

CLI Client
JDBC
ODBC

Supported programming languages

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java, SQL

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

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

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Managed transparently by NuoDB

Collection-level replication

yes

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable commit protocol

ACID

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

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

Key-based authentication

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

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