DBMS > Blazegraph vs. HugeGraph vs. Milvus vs. SQLite vs. YDB

System Properties Comparison Blazegraph vs. HugeGraph vs. Milvus vs. SQLite vs. YDB

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

A fast-speed and highly-scalable Graph DBMS

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

Widely used embeddable, in-process RDBMS

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Document store
Relational DBMS

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

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.35
Rank	#278	Overall
	#43	Document stores
	#127	Relational DBMS

Website

blazegraph.com

github.com/hugegraph
hugegraph.apache.org

milvus.io

www.sqlite.org

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

wiki.blazegraph.com

hugegraph.apache.org/docs

milvus.io/docs/overview.md

www.sqlite.org/docs.html

ydb.tech/en/docs

Developer

Blazegraph

Baidu

Dwayne Richard Hipp

Yandex

Initial release

2006

2018

2019

2000

2019

Current release

2.1.5, March 2019

0.9

2.3.4, January 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Public Domain

Open Source

Apache 2.0; commercial license available

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

Java

C++, Go

Server operating systems

Linux
OS X
Windows

Linux
macOS
Unix

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

server-less

Linux

Data scheme

schema-free

yes

dynamic column types

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

RDF literal types

yes

Vector, Numeric and String

yes

not rigid because of 'dynamic typing' concept.

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

also supports composite index and range index

yes

SQL

Support of SQL

SPARQL is used as query language

yes

SQL-92 is not fully supported

SQL-like query language (YQL)

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

Groovy
Java
Python

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

edges in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

Security and Authentication via Web Application Container (Tomcat, Jetty)

Users, roles and permissions

Role based access control and fine grained access rights

Access rights defined for Yandex Cloud users

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Licensing and pricing models

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