DBMS > HEAVY.AI vs. OpenMLDB vs. Oracle vs. Tkrzw

System Properties Comparison HEAVY.AI vs. OpenMLDB vs. Oracle vs. Tkrzw

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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

An open-source machine learning database that provides a feature platform for training and inference

Widely used RDBMS

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

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

openmldb.ai

www.oracle.com/database

dbmx.net/tkrzw

Technical documentation

docs.heavy.ai

openmldb.ai/docs/zh/main

docs.oracle.com/en/database

Developer

HEAVY.AI, Inc.

4 Paradigm Inc.

Oracle

Mikio Hirabayashi

Initial release

2016

2020

1980

2020

Current release

5.10, January 2022

2024-2 February 2024

23c, September 2023

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

commercial

restricted free version is available

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

C++, Java, Scala

C and C++

C++

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
macOS

Data scheme

yes

Fixed schema

yes

Schemaless in JSON and XML columns

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
SQLAlchemy

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Go
Java
Python
Scala

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

horizontal partitioning

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

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

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

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