DBMS > GeoMesa vs. HugeGraph vs. IRONdb vs. Milvus vs. WakandaDB

System Properties Comparison GeoMesa vs. HugeGraph vs. IRONdb vs. Milvus vs. WakandaDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A fast-speed and highly-scalable Graph DBMS

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

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	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.geomesa.org

github.com/hugegraph
hugegraph.apache.org

www.circonus.com/solutions/time-series-database/

milvus.io

wakanda.github.io

Technical documentation

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

hugegraph.apache.org/docs

docs.circonus.com/irondb/category/getting-started

milvus.io/docs/overview.md

wakanda.github.io/doc

Developer

CCRi and others

Baidu

Circonus LLC.

Wakanda SAS

Initial release

2014

2018

2017

2019

2012

Current release

4.0.5, February 2024

0.9

V0.10.20, January 2018

2.3.4, January 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

Open Source

AGPLv3, extended 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 and C++

C++, Go

C++, JavaScript

Server operating systems

Linux
macOS
Unix

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

RESTful HTTP API

Supported programming languages

Groovy
Java
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

JavaScript

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes, in Lua

yes

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

Automatic, metric affinity per node

Sharding

none

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

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

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.

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

yes

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

Spatial database management systems
6 April 2021, Matthias Gelbmann

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Vector databases
2 June 2023, Matthias Gelbmann

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