DBMS > EsgynDB vs. HEAVY.AI vs. Postgres-XL vs. Quasardb

System Properties Comparison EsgynDB vs. HEAVY.AI vs. Postgres-XL vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Distributed, high-performance timeseries database

Primary database model

Relational 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	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.esgyn.cn

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

www.postgres-xl.org

quasar.ai

Technical documentation

docs.heavy.ai

www.postgres-xl.org/documentation

doc.quasar.ai/master

Developer

Esgyn

HEAVY.AI, Inc.

quasardb

Initial release

2015

2016

2014

since 2012, originally named StormDB

2009

Current release

5.10, January 2022

10 R1, October 2018

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Mozilla public license

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++, Java

C++ and CUDA

C++

Server operating systems

Linux

Linux
macOS

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

distributed, parallel query execution

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC
Thrift
Vega

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

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

All languages supporting JDBC/ODBC/Thrift
Python

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

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

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

horizontal partitioning

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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