DBMS > HEAVY.AI vs. IBM Db2 warehouse vs. TerarkDB vs. TimesTen

System Properties Comparison HEAVY.AI vs. IBM Db2 warehouse vs. TerarkDB vs. TimesTen

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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

Cloud-based data warehousing service

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

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

www.ibm.com/products/db2/warehouse

github.com/bytedance/terarkdb

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.heavy.ai

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

docs.oracle.com/database/timesten-18.1

Developer

HEAVY.AI, Inc.

IBM

ByteDance, originally Terark

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2016

2014

2016

1998

Current release

5.10, January 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

restricted open source version available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++ and CUDA

C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

yes

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

.NET Client API
JDBC
ODBC
OLE DB

C++ API
Java API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C++
Java

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

PL/SQL, SQL PL

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

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More resources

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