DBMS > Apache IoTDB vs. Hyprcubd vs. OpenTSDB vs. Sequoiadb vs. Transwarp Hippo

System Properties Comparison Apache IoTDB vs. Hyprcubd vs. OpenTSDB vs. Sequoiadb vs. Transwarp Hippo

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

Serverless Time Series DBMS

Scalable Time Series DBMS based on HBase

NewSQL database with distributed OLTP and SQL

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Time Series DBMS

Document store
Relational DBMS

Vector DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	0.05
Rank	#386	Overall
	#15	Vector DBMS

Website

iotdb.apache.org

hyprcubd.com (offline)

opentsdb.net

www.sequoiadb.com

www.transwarp.cn/en/subproduct/hippo

Technical documentation

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

opentsdb.net/docs/build/html/index.html

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation

Hyprcubd, Inc.

currently maintained by Yahoo and other contributors

Sequoiadb Ltd.

Initial release

2018

2011

2013

2023

Current release

1.1.0, April 2023

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

LGPL

Open Source

Server: AGPL; Client: Apache V2

commercial

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++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
Windows

Linux

Linux
macOS

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

numeric data for metrics, strings for tags

yes

oid, date, timestamp, binary, regex

Vector, Numeric and 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

gRPC (https)

HTTP API
Telnet API

proprietary protocol using JSON

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Erlang
Go
Java
Python
R
Ruby

.Net
C++
Java
PHP
Python

C++
Java
Python

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

based on HBase

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

based on HBase

Source-replica replication

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

Immediate Consistency

based on HBase

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Document is locked during a transaction

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

token access

simple password-based access control

Role based access control and fine grained access rights

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

show all

Recent citations in the news

AMD EPYC 4364P & 4564P @ DDR5-4800 / DDR5-5200 vs. Intel Xeon E-2488 Review
6 June 2024, Phoronix

TsFile: A Standard Format for IoT Time Series Data
27 February 2024, The New Stack

Linux 6.5 With AMD P-State EPP Default Brings Performance & Power Efficiency Benefits For Ryzen Servers
21 September 2023, Phoronix

Apache Promotes IoT Database Project
25 September 2020, Datanami

AMD EPYC 8324P / 8324PN Siena 32-Core Siena Linux Server Performance Review
10 October 2023, Phoronix

provided by Google News

Comparing Different Time-Series Databases
10 February 2022, hackernoon.com

MapR to help admins peer into dense Hadoop clusters
28 June 2016, SiliconANGLE News

A real-time processing revival - O'Reilly Radar
2 April 2015, O'Reilly Radar

provided by Google News

Share this page