DBMS > Apache IoTDB vs. BigObject vs. Sequoiadb

System Properties Comparison Apache IoTDB vs. BigObject vs. Sequoiadb

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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

Analytic DBMS for real-time computations and queries

NewSQL database with distributed OLTP and SQL

Primary database model

Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Relational DBMS

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Trend Chart

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	0.13
Rank	#342	Overall
	#150	Relational DBMS

Score	0.48
Rank	#260	Overall
	#41	Document stores
	#121	Relational DBMS

Website

iotdb.apache.org

bigobject.io

www.sequoiadb.com

Technical documentation

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

docs.bigobject.io

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

Developer

Apache Software Foundation

BigObject, Inc.

Sequoiadb Ltd.

Initial release

2018

2015

2013

Current release

1.1.0, April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
Native API

fluentd
ODBC
RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

Lua

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

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

none

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

none

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Read/write lock on objects (tables, trees)

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

simple password-based access control

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