DBMS > Apache IoTDB vs. Databend vs. Datomic vs. Hypertable vs. MySQL

System Properties Comparison Apache IoTDB vs. Databend vs. Datomic vs. Hypertable vs. MySQL

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

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

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

An open source BigTable implementation based on distributed file systems such as Hadoop

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

iotdb.apache.org

github.com/datafuselabs/databend
www.databend.com

www.datomic.com

www.mysql.com

Technical documentation

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

docs.databend.com

docs.datomic.com

dev.mysql.com/doc

Developer

Apache Software Foundation

Databend Labs

Cognitect

Hypertable Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2018

2021

2012

2009

1995

Current release

1.1.0, April 2023

1.0.59, April 2023

1.0.7075, December 2023

0.9.8.11, March 2016

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

limited edition free

Open Source

GNU version 3. Commercial license available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

Rust

Java, Clojure

C++

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted
Linux
macOS

All OS with a Java VM

Linux
OS X
Windows

an inofficial Windows port is available

FreeBSD
Linux
OS X
Solaris
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.

yes

Secondary indexes

yes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP API

C++ API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Java
JavaScript (Node.js)
Python
Rust

Clojure
Java

C++
Java
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

proprietary syntax

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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

But extensive use of caching in the application peers

selectable replication factor on file system level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

yes

Users with fine-grained authorization concept, user roles

Users with fine-grained authorization concept

no user groups or roles

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