DBMS > HEAVY.AI vs. Postgres-XL vs. Qdrant vs. SAP HANA

System Properties Comparison HEAVY.AI vs. Postgres-XL vs. Qdrant vs. SAP HANA

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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

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

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

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
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	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

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

www.postgres-xl.org

github.com/qdrant/qdrant
qdrant.tech

www.sap.com/products/hana.html

Technical documentation

docs.heavy.ai

www.postgres-xl.org/documentation

qdrant.tech/documentation

help.sap.com/hana

Developer

HEAVY.AI, Inc.

Qdrant

SAP

Initial release

2016

2014

since 2012, originally named StormDB

2021

2010

Current release

5.10, January 2022

10 R1, October 2018

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Mozilla public license

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based 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

Docker
Linux
macOS
Windows

Appliance or cloud-service

Data scheme

yes

schema-free

yes

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

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes

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

JDBC
ODBC

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

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

horizontal partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

MVCC

ACID

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

yes

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