DBMS > Apache IoTDB vs. STSdb vs. TerminusDB vs. Vertica

System Properties Comparison Apache IoTDB vs. STSdb vs. TerminusDB vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

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

Column oriented

Secondary database models

Document store
RDF store

Spatial DBMS
Time Series DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Score	0.19
Rank	#311	Overall
	#27	Graph DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

iotdb.apache.org

github.com/STSSoft/STSdb4

terminusdb.com

www.vertica.com

Technical documentation

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

terminusdb.github.io/terminusdb/#

vertica.com/documentation

Developer

Apache Software Foundation

STS Soft SC

DataChemist Ltd.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2018

2011

2018

2005

Current release

1.1.0, April 2023

4.0.8, September 2015

11.0.0, January 2023

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPLv2, commercial license available

Open Source

GPL V3

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

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

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Prolog, Rust

C++

Server operating systems

All OS with a Java VM (>= 1.8)

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

primitive types and user defined types (classes)

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.

Secondary indexes

yes

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

SQL

Support of SQL

SQL-like query language

SQL-like query language (WOQL)

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

APIs and other access methods

JDBC
Native API

.NET Client API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

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

character-based, interactive, front-end utility

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
Java

JavaScript
Python

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

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

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

none

Graph Partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Journaling Streams

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

Eventual Consistency
Strong Consistency with Raft

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

in-memory journaling

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

yes

Role-based access control

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

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