DBMS > Galaxybase vs. Google Cloud Bigtable vs. Milvus vs. Riak TS vs. SWC-DB

System Properties Comparison Galaxybase vs. Google Cloud Bigtable vs. Milvus vs. Riak TS vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Scalable, ACID-compliant native distributed parallel graph platform

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

Graph DBMS

Key-value store
Wide column store

Vector DBMS

Time Series DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Website

galaxybase.com

cloud.google.com/bigtable

milvus.io

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

Technical documentation

cloud.google.com/bigtable/docs

milvus.io/docs/overview.md

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

Developer

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Google

Open Source, formerly Basho Technologies

Alex Kashirin

Initial release

2017

2015

2019

2015

2020

Current release

Nov 20, November 2021

2.4.4, May 2024

3.0.0, September 2022

0.5, April 2021

License

Commercial or Open Source

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 and Java

C++, Go

Erlang

C++

Server operating systems

Linux

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X

Linux

Data scheme

Strong typed schema

schema-free

Typing

predefined data types such as float or date

yes

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

yes

restricted

SQL

Support of SQL

yes, limited

SQL-like query language

APIs and other access methods

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

RESTful HTTP API

HTTP API
Native Erlang Interface

Proprietary protocol
Thrift

Supported programming languages

Go
Java
Python

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

user defined procedures and functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

links between datasets can be stored

Transaction concepts

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

ACID

Atomic single-row operations

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

Role-based access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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