DBMS > IRONdb vs. Microsoft Azure Cosmos DB vs. ScyllaDB vs. SQream DB vs. Trino

System Properties Comparison IRONdb vs. Microsoft Azure Cosmos DB vs. ScyllaDB vs. SQream DB vs. Trino

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

Cassandra and DynamoDB compatible wide column store

a GPU-based, columnar RDBMS for big data analytics workloads

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Key-value store

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.08
Rank	#76	Overall
	#5	Wide column stores

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Score	4.99
Rank	#65	Overall
	#36	Relational DBMS

Website

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

azure.microsoft.com/services/cosmos-db

www.scylladb.com

sqream.com

trino.io

Technical documentation

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

learn.microsoft.com/azure/cosmos-db

docs.scylladb.com

docs.sqream.com

trino.io/broadcast
trino.io/docs/current

Social network pages

Developer

Circonus LLC.

Microsoft

ScyllaDB

SQream Technologies

Trino Software Foundation

Initial release

2017

2014

2015

2017

2012

2020 rebranded from PrestoSQL

Current release

V0.10.20, January 2018

ScyllaDB Open Source 5.4.1, January 2024

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

Open Source (AGPL), commercial license available

commercial

Open Source

Apache Version 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.

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Implementation language

C and C++

C++

C++, CUDA, Haskell, Java, Scala

Java

Server operating systems

Linux

hosted

Linux

Linux
macOS

for devlopment

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

JSON types

yes

yes, ANSI Standard SQL 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

cluster global secondary indices

depending on connected data-source

SQL

Support of SQL

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

SQL-like query language

SQL-like DML and DDL statements (CQL)

yes

APIs and other access methods

HTTP API

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

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

.Net
JDBC
ODBC

JDBC
RESTful HTTP API
Trino CLI

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

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

C++
Java
JavaScript (Node.js)
Python

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes, in Lua

JavaScript

yes, Lua

user defined functions in Python

yes, depending on connected data-source

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

Implicit feature of the cloud service

Sharding

horizontal and vertical partitioning

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

yes

Implicit feature of the cloud service

selectable replication factor

Representation of geographical distribution of servers is possible

none

depending on connected data-source

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

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

Atomicity and isolation are supported for single operations

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

in-memory tables

User concepts

Access control

Access rights can be defined down to the item level

Access rights for users can be defined per object

SQL standard access control

More information provided by the system vendor

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Competitive advantages

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