DBMS > HEAVY.AI vs. Machbase Neo vs. Postgres-XL vs. Qdrant

System Properties Comparison HEAVY.AI vs. Machbase Neo vs. Postgres-XL vs. Qdrant

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Editorial information provided by DB-Engines

Name

Description

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

TimeSeries DBMS for AIoT and BigData

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

Primary database model

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Website

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

machbase.com

www.postgres-xl.org

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

docs.heavy.ai

machbase.com/dbms

www.postgres-xl.org/documentation

qdrant.tech/documentation

Developer

HEAVY.AI, Inc.

Machbase

Qdrant

Initial release

2016

2013

2014

since 2012, originally named StormDB

2021

Current release

5.10, January 2022

V8.0, August 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

free test version available

Open Source

Mozilla public license

Open Source

Apache Version 2.0

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

C++ and CUDA

Rust

Server operating systems

Linux

Linux
macOS
Windows

Linux
macOS

Docker
Linux
macOS
Windows

Data scheme

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JDBC
ODBC
Thrift
Vega

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

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

MVCC

Concurrency

Support for concurrent manipulation of data

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

volatile and lookup table

yes

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control

fine grained access rights according to SQL-standard

Key-based authentication

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More resources

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HEAVY.AI Introduces HeavyIQ, Delivering Powerful Conversational Analytics Focused on Location and Time Data
19 March 2024, Datanami

Big Data Analytics: A Game Changer for Infrastructure
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HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, Business Wire

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23 January 2024, TechCrunch

Qdrant offers managed vector database for hybrid clouds
16 April 2024, InfoWorld

Qdrant Announces an Industry-First Hybrid Cloud Offering For Enterprise AI Applications
16 April 2024, Business Wire

Qdrant Raises $28M to Advance Massive-Scale AI Applications
26 January 2024, Datanami

Qdrant launches first vector database as a managed hybrid cloud
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provided by Google News

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