DBMS > Databend vs. Heroic vs. MonetDB vs. SWC-DB

System Properties Comparison Databend vs. Heroic vs. MonetDB vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

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

A relational database management system that stores data in columns

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

github.com/datafuselabs/databend
www.databend.com

github.com/spotify/heroic

www.monetdb.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.databend.com

spotify.github.io/heroic

www.monetdb.org/Documentation

Developer

Databend Labs

Spotify

MonetDB BV

Alex Kashirin

Initial release

2021

2014

2004

2020

Current release

1.0.59, April 2023

Dec2023 (11.49), December 2023

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Rust

Java

C++

Server operating systems

hosted
Linux
macOS

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

via Elasticsearch

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

SQL-like query language

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Proprietary protocol
Thrift

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

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

C++

Server-side scripts

Stored procedures

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

Source-replica replication available in experimental status

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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.

User concepts

Access control

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

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