DBMS > Hypertable vs. Microsoft Azure Cosmos DB vs. Milvus vs. Riak TS vs. SWC-DB

System Properties Comparison Hypertable vs. Microsoft Azure Cosmos DB vs. Milvus vs. Riak TS vs. SWC-DB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

An open source BigTable implementation based on distributed file systems such as Hadoop

Globally distributed, horizontally scalable, multi-model database service

A DBMS designed for efficient storage of vector data and vector similarity searches

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A high performance, scalable Wide Column DBMS

Primary database model

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Time Series DBMS

Wide column store

Secondary database models

Spatial DBMS

Time Series DBMS

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	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

azure.microsoft.com/services/cosmos-db

milvus.io

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

Technical documentation

learn.microsoft.com/azure/cosmos-db

milvus.io/docs/overview.md

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Hypertable Inc.

Microsoft

Open Source, formerly Basho Technologies

Alex Kashirin

Initial release

2009

2014

2019

2015

2020

Current release

0.9.8.11, March 2016

2.4.4, May 2024

3.0.0, September 2022

0.5, April 2021

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

commercial

Open Source

Apache Version 2.0

Open Source

GPL V3

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

Erlang

C++

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X

Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

Vector, Numeric and String

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

restricted

only exact value or prefix value scans

yes

All properties auto-indexed by default

restricted

SQL

Support of SQL

SQL-like query language

yes, limited

SQL-like query language

APIs and other access methods

C++ API
Thrift

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

RESTful HTTP API

HTTP API
Native Erlang Interface

Proprietary protocol
Thrift

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

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

C++
Go
Java
JavaScript (Node.js)
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C++

Server-side scripts

Stored procedures

JavaScript

Erlang

Triggers

JavaScript

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

yes

Implicit feature of the cloud service

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

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

User concepts

Access control

Access rights can be defined down to the item level

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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