DBMS > Milvus vs. OrigoDB vs. SpatiaLite vs. Splice Machine vs. XTDB

System Properties Comparison Milvus vs. OrigoDB vs. SpatiaLite vs. Splice Machine vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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

A fully ACID in-memory object graph database

Spatial extension of SQLite

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Vector DBMS

Document store
Object oriented DBMS

Spatial DBMS

Relational DBMS

Document store

Secondary database models

Relational DBMS

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

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#19	Object oriented DBMS

Score	1.63
Rank	#146	Overall
	#3	Spatial DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

milvus.io

origodb.com

www.gaia-gis.it/fossil/libspatialite/index

splicemachine.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

milvus.io/docs/overview.md

origodb.com/docs

www.gaia-gis.it/gaia-sins/spatialite_topics.html

splicemachine.com/how-it-works

www.xtdb.com/docs

Developer

Robert Friberg et al

Alessandro Furieri

Splice Machine

Juxt Ltd.

Initial release

2019

2009

under the name LiveDB

2008

2014

2019

Current release

2.4.4, May 2024

5.0.0, August 2020

3.1, March 2021

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Open Source

AGPL 3.0, commercial license available

Open Source

MIT License

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++, Go

C++

Java

Clojure

Server operating systems

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
Windows

server-less

Linux
OS X
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

Vector, Numeric and String

User defined using .NET types and collections

yes

yes, extensible-data-notation format

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

.NET Client API
HTTP API
LINQ

JDBC
Native Spark Datasource
ODBC

HTTP REST
JDBC

Supported programming languages

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

.Net

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

Clojure
Java

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

Write ahead log

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Role based access control and fine grained access rights

Role based authorization

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

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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

Vector databases
2 June 2023, Matthias Gelbmann

show all

Spatial database management systems
6 April 2021, Matthias Gelbmann

show all

Recent citations in the news

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