DBMS > AnzoGraph DB vs. Bangdb vs. Milvus vs. Newts

System Properties Comparison AnzoGraph DB vs. Bangdb vs. Milvus vs. Newts

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Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

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

Time Series DBMS based on Cassandra

Primary database model

Graph DBMS
RDF store

Document store
Graph DBMS
Time Series DBMS

Vector DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.07
Rank	#346	Overall
	#47	Document stores
	#35	Graph DBMS
	#32	Time Series DBMS

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Website

cambridgesemantics.com/anzograph

bangdb.com

milvus.io

opennms.github.io/newts

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.bangdb.com

milvus.io/docs/overview.md

github.com/OpenNMS/newts/wiki

Developer

Cambridge Semantics

Sachin Sinha, BangDB

OpenNMS Group

Initial release

2018

2012

2019

2014

Current release

2.3, January 2021

BangDB 2.0, October 2021

2.4.4, May 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

BSD 3

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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++, Go

Java

Server operating systems

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

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

Vector, Numeric and String

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

secondary, composite, nested, reverse, geospatial

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL like support with command line tool

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

Proprietary protocol
RESTful HTTP API

RESTful HTTP API

HTTP REST
Java API

Supported programming languages

C++
Java
Python

C
C#
C++
Java
Python

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

Java

Server-side scripts

Stored procedures

user defined functions and aggregates

Triggers

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

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

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

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

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Tunable consistency, set CAP knob accordingly

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

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

yes, run db with in-memory only mode

yes

User concepts

Access control

Access rights for users and roles

yes (enterprise version only)

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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More resources

DB-Engines blog posts

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