DBMS > HEAVY.AI vs. Heroic vs. IRONdb vs. mSQL vs. Snowflake

System Properties Comparison HEAVY.AI vs. Heroic vs. IRONdb vs. mSQL vs. Snowflake

Editorial information provided by DB-Engines

Name

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

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

github.com/spotify/heroic

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

hughestech.com.au/products/msql

www.snowflake.com

Technical documentation

docs.heavy.ai

spotify.github.io/heroic

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

docs.snowflake.net/manuals/index.html

Developer

HEAVY.AI, Inc.

Spotify

Circonus LLC.

Hughes Technologies

Snowflake Computing Inc.

Initial release

2016

2014

2017

1994

2014

Current release

5.10, January 2022

V0.10.20, January 2018

4.4, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

free licenses can be provided

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

C++ and CUDA

Java

C and C++

Server operating systems

Linux

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

hosted

Data scheme

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

via Elasticsearch

yes

SQL

Support of SQL

yes

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

A subset of ANSI SQL is implemented

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

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

HQL (Heroic Query Language, a JSON-based language)
HTTP API

HTTP API

JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

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

C
C++
Delphi
Java
Perl
PHP
Tcl

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes, in Lua

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Automatic, metric affinity per node

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

configurable replication factor, datacenter aware

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

none

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

fine grained access rights according to SQL-standard

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

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