DBMS > Bangdb vs. Brytlyt vs. Milvus vs. RavenDB vs. SWC-DB

System Properties Comparison Bangdb vs. Brytlyt vs. Milvus vs. RavenDB vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

Time Series DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

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

Technical documentation

docs.bangdb.com

docs.brytlyt.io

milvus.io/docs/overview.md

ravendb.net/docs

Developer

Sachin Sinha, BangDB

Brytlyt

Hibernating Rhinos

Alex Kashirin

Initial release

2012

2016

2019

2010

2020

Current release

BangDB 2.0, October 2021

5.0, August 2023

2.4.4, May 2024

5.4, July 2022

0.5, April 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C, C++

C, C++ and CUDA

C++, Go

C++

Server operating systems

Linux

Linux
OS X
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
macOS
Raspberry Pi
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like query language (RQL)

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Proprietary protocol
Thrift

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

Authorization levels configured per client per database

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.

Related products and services

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