DBMS > Apache IoTDB vs. InfinityDB vs. MonetDB vs. RavenDB vs. Snowflake

System Properties Comparison Apache IoTDB vs. InfinityDB vs. MonetDB vs. RavenDB vs. Snowflake

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

A Java embedded Key-Value Store which extends the Java Map interface

A relational database management system that stores data in columns

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Secondary database models

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
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	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

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

boilerbay.com/infinitydb/manual

www.monetdb.org/Documentation

ravendb.net/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Boiler Bay Inc.

MonetDB BV

Hibernating Rhinos

Snowflake Computing Inc.

Initial release

2018

2002

2004

2010

2014

Current release

1.1.0, April 2023

4.0

Dec2023 (11.49), December 2023

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla Public License 2.0

Open Source

AGPL version 3, commercial license available

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

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Raspberry Pi
Windows

hosted

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

SQL-like query language (RQL)

yes

APIs and other access methods

JDBC
Native API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

yes, in SQL, C, R

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding via remote tables

Sharding

yes

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 available in experimental status

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

READ-COMMITTED or SERIALIZED

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

ACID, Cluster-wide transaction available

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

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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

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.