DBMS > 4D vs. Apache IoTDB vs. SAP HANA vs. TimesTen vs. Vertica

System Properties Comparison 4D vs. Apache IoTDB vs. SAP HANA vs. TimesTen vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

In-Memory RDBMS compatible to Oracle

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Column oriented

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

www.4d.com

iotdb.apache.org

www.sap.com/products/hana.html

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

www.vertica.com

Technical documentation

developer.4d.com

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

help.sap.com/hana

docs.oracle.com/database/timesten-18.1

vertica.com/documentation

Developer

4D, Inc

Apache Software Foundation

SAP

Oracle, TimesTen Performance Software, HP

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

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1984

2018

2010

1998

2005

Current release

v20, April 2023

1.1.0, April 2023

2.0 SPS07 (April 4, 2023), April 2023

11 Release 2 (11.2.2.8.0)

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

also available as a cloud based service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

OS X
Windows

All OS with a Java VM (>= 1.8)

Appliance or cloud-service

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

Linux

Data scheme

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
Native API

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Go
Java
Python
Scala

C
C++
Java
PL/SQL

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

Server-side scripts

Stored procedures

yes

SQLScript, R

PL/SQL

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

yes

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

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

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

Users and groups

yes

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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