DBMS > GeoMesa vs. HugeGraph vs. Milvus vs. Oracle Rdb vs. Splice Machine

System Properties Comparison GeoMesa vs. HugeGraph vs. Milvus vs. Oracle Rdb vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

A fast-speed and highly-scalable Graph DBMS

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.geomesa.org

github.com/hugegraph
hugegraph.apache.org

milvus.io

www.oracle.com/database/technologies/related/rdb.html

splicemachine.com

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

hugegraph.apache.org/docs

milvus.io/docs/overview.md

www.oracle.com/database/technologies/related/rdb-doc.html

splicemachine.com/how-it-works

Developer

CCRi and others

Baidu

Oracle, originally developed by Digital Equipment Corporation (DEC)

Splice Machine

Initial release

2014

2018

2019

1984

2014

Current release

4.0.5, February 2024

0.9

2.3.4, January 2024

7.4.1.1, 2021

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0, 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

Scala

Java

C++, Go

Java

Server operating systems

Linux
macOS
Unix

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

HP Open VMS

Linux
OS X
Solaris
Windows

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Groovy
Java
Python

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

yes

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

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Eventual Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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.

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

Users, roles and permissions

Role based access control and fine grained access rights

Access rights for users, groups and roles according to SQL-standard

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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Vector databases
2 June 2023, Matthias Gelbmann

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