DBMS > HEAVY.AI vs. Postgres-XL vs. Qdrant vs. SiteWhere

System Properties Comparison HEAVY.AI vs. Postgres-XL vs. Qdrant vs. SiteWhere

Please select another system to include it in the comparison.

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

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

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

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Vector DBMS

Time Series DBMS

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	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

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

www.postgres-xl.org

github.com/qdrant/qdrant
qdrant.tech

github.com/sitewhere/sitewhere

Technical documentation

docs.heavy.ai

www.postgres-xl.org/documentation

qdrant.tech/documentation

sitewhere1.sitewhere.io/index.html

Developer

HEAVY.AI, Inc.

Qdrant

SiteWhere

Initial release

2016

2014

since 2012, originally named StormDB

2021

2010

Current release

5.10, January 2022

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Mozilla public license

Open Source

Apache Version 2.0

Open Source

Common Public Attribution License Version 1.0

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

Java

Server operating systems

Linux

Linux
macOS

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

predefined scheme

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

XML type, but no XML query functionality

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes

distributed, parallel query execution

APIs and other access methods

JDBC
ODBC
Thrift
Vega

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

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

.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

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Collection-level replication

selectable replication factor

based on HBase

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

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

User concepts

Access control

fine grained access rights according to SQL-standard

Key-based authentication

Users with fine-grained authorization concept

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

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
13 July 2023, Spiceworks News and Insights

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, Business Wire

Making the most of geospatial intelligence
14 April 2023, InfoWorld

The insideBIGDATA IMPACT 50 List for Q4 2023
11 October 2023, insideBIGDATA

provided by Google News

Open source vector database startup Qdrant raises $28M
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, businesswire.com

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

Qdrant launches first vector database as a managed hybrid cloud
16 April 2024, VentureBeat

provided by Google News

SiteWhere: An open platform for connected devices
11 July 2017, Open Source For You

11 Best Open source IoT Platforms To Develop Smart Projects
9 March 2023, H2S Media

provided by Google News

Share this page