DBMS > Apache IoTDB vs. BigObject vs. EXASOL vs. Oracle Berkeley DB vs. SiriDB

System Properties Comparison Apache IoTDB vs. BigObject vs. EXASOL vs. Oracle Berkeley DB vs. SiriDB

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Widely used in-process key-value store

Open Source Time Series DBMS

Primary database model

Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	1.66
Rank	#138	Overall
	#64	Relational DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Website

iotdb.apache.org

bigobject.io

www.exasol.com

www.oracle.com/database/technologies/related/berkeleydb.html

siridb.com

Technical documentation

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

docs.bigobject.io

www.exasol.com/resources

docs.oracle.com/cd/E17076_05/html/index.html

docs.siridb.com

Developer

Apache Software Foundation

BigObject, Inc.

Exasol

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Cesbit

Initial release

2018

2015

2000

1994

2017

Current release

1.1.0, April 2023

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

Open Source

commercial license available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C, Java, C++ (depending on the Berkeley DB edition)

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)

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
Native API

fluentd
ODBC
RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
Lua
Python
R

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

Lua

user defined functions

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

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

Access rights for users, groups and roles according to SQL-standard

simple rights management via user accounts

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