DBMS > Apache IoTDB vs. Dolt vs. HEAVY.AI vs. Snowflake vs. TerminusDB

System Properties Comparison Apache IoTDB vs. Dolt vs. HEAVY.AI vs. Snowflake vs. TerminusDB

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

A MySQL compatible DBMS with Git-like versioning of data and schema

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Time Series DBMS

Relational DBMS

Graph DBMS

Secondary database models

Document store

Spatial DBMS

Document store
RDF store

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

iotdb.apache.org

github.com/dolthub/dolt
www.dolthub.com

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

www.snowflake.com

terminusdb.com

Technical documentation

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

docs.dolthub.com

docs.heavy.ai

docs.snowflake.net/manuals/index.html

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

DoltHub Inc

HEAVY.AI, Inc.

Snowflake Computing Inc.

DataChemist Ltd.

Initial release

2018

2016

2014

2018

Current release

1.1.0, April 2023

5.10, January 2022

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

GPL V3

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

Java

C++ and CUDA

Prolog, Rust

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
macOS
Windows

Linux

hosted

Linux

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language (WOQL)

APIs and other access methods

JDBC
Native API

CLI Client
HTTP REST

JDBC
ODBC
Thrift
Vega

CLI Client
JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript (Node.js)
Python

JavaScript
Python

Server-side scripts

Stored procedures

yes

currently in alpha release

user defined functions

yes

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

Round robin

yes

Graph 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

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

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

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

Only one user is configurable, and must be specified in the config file at startup

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

Role-based access control

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