DBMS > IRONdb vs. Microsoft Azure Cosmos DB vs. OpenMLDB vs. Sadas Engine vs. Spark SQL

System Properties Comparison IRONdb vs. Microsoft Azure Cosmos DB vs. OpenMLDB vs. Sadas Engine vs. Spark SQL

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Globally distributed, horizontally scalable, multi-model database service

An open-source machine learning database that provides a feature platform for training and inference

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.02
Rank	#367	Overall
	#37	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.circonus.com/solutions/time-series-database/

azure.microsoft.com/services/cosmos-db

openmldb.ai

www.sadasengine.com

spark.apache.org/sql

Technical documentation

docs.circonus.com/irondb/category/getting-started

learn.microsoft.com/azure/cosmos-db

openmldb.ai/docs/zh/main

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Circonus LLC.

Microsoft

4 Paradigm Inc.

SADAS s.r.l.

Apache Software Foundation

Initial release

2017

2014

2020

2006

2014

Current release

V0.10.20, January 2018

2024-2 February 2024

8.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

commercial

free trial version available

Open Source

Apache 2.0

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

C and C++

C++, Java, Scala

C++

Scala

Server operating systems

Linux

hosted

Linux

AIX
Linux
Windows

Linux
OS X
Windows

Data scheme

schema-free

Fixed schema

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
SQLAlchemy

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++
Go
Java
Python
Scala

.Net
C
C#
C++
Groovy
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes, in Lua

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

Implicit feature of the cloud service

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

yes

Implicit feature of the cloud service

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

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

managed by 'Learn by Usage'

User concepts

Access control

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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

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