DBMS > Apache IoTDB vs. mSQL vs. OrigoDB vs. Snowflake vs. Solr

System Properties Comparison Apache IoTDB vs. mSQL vs. OrigoDB vs. Snowflake vs. Solr

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Time Series DBMS

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

iotdb.apache.org

hughestech.com.au/products/msql

origodb.com

www.snowflake.com

solr.apache.org

Technical documentation

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

origodb.com/docs

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Apache Software Foundation

Hughes Technologies

Robert Friberg et al

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2018

1994

2009

under the name LiveDB

2014

2006

Current release

1.1.0, April 2023

4.4, October 2021

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

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

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

yes

supports customizable data types and automatic typing

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.

can be achieved using .NET

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
Native API

JDBC
ODBC

.NET Client API
HTTP API
LINQ

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions

Java plugins

Triggers

yes

Domain Events

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

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

none

horizontal partitioning

client side managed; servers are not synchronized

yes

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

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

depending on model

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

Role based authorization

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

yes

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