DBMS > Apache IoTDB vs. Oracle Berkeley DB vs. SiriDB vs. Snowflake vs. Yaacomo

System Properties Comparison Apache IoTDB vs. Oracle Berkeley DB vs. SiriDB vs. Snowflake vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed 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

Widely used in-process key-value store

Open Source Time Series DBMS

Cloud-based data warehousing service for structured and semi-structured data

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

Relational DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

iotdb.apache.org

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

siridb.com

www.snowflake.com

yaacomo.com

Technical documentation

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

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

docs.siridb.com

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Cesbit

Snowflake Computing Inc.

Q2WEB GmbH

Initial release

2018

1994

2017

2014

2009

Current release

1.1.0, April 2023

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial license available

Open Source

MIT License

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

Server operating systems

All OS with a Java VM (>= 1.8)

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

hosted

Android
Linux
Windows

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

JDBC
Native API

HTTP API

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

only for the SQL API

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

yes

horizontal partitioning

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

Source-replica replication

yes

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

simple rights management via user accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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