DBMS > Apache IoTDB vs. Databend vs. OpenQM vs. TerarkDB

System Properties Comparison Apache IoTDB vs. Databend vs. OpenQM vs. TerarkDB

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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

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

QpenQM is a high-performance, self-tuning, multi-value DBMS

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

Primary database model

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

iotdb.apache.org

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

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

github.com/bytedance/terarkdb

Technical documentation

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

docs.databend.com

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Apache Software Foundation

Databend Labs

Rocket Software, originally Martin Phillips

ByteDance, originally Terark

Initial release

2018

2021

1993

2016

Current release

1.1.0, April 2023

1.0.59, April 2023

3.4-12

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

GPLv2, extended commercial license available

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Rust

C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted
Linux
macOS

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Data scheme

yes

with some exceptions

schema-free

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

APIs and other access methods

JDBC
Native API

CLI Client
JDBC
RESTful HTTP API

C++ API
Java API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Basic
C
Java
Objective C
PHP
Python

C++
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

yes

none

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

yes

none

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

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Users with fine-grained authorization concept, user roles

Access rights can be defined down to the item level

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