DBMS > Apache IoTDB vs. BigchainDB vs. Hyprcubd

System Properties Comparison Apache IoTDB vs. BigchainDB vs. Hyprcubd

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Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded 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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Serverless Time Series DBMS

Primary database model

Time Series DBMS

Document store

Time Series DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.79
Rank	#212	Overall
	#36	Document stores

Website

iotdb.apache.org

www.bigchaindb.com

hyprcubd.com (offline)

Technical documentation

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

bigchaindb.readthedocs.io/en/latest

Developer

Apache Software Foundation

Hyprcubd, Inc.

Initial release

2018

2016

Current release

1.1.0, April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL v3

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Python

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JDBC
Native API

CLI Client
RESTful HTTP API

gRPC (https)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Go
Haskell
Java
JavaScript
Python
Ruby

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)

Sharding

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

selectable replication factor

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

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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

token access

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