DBMS > H2 vs. Milvus vs. SAP HANA vs. Solr vs. SQLite

System Properties Comparison H2 vs. Milvus vs. SAP HANA vs. Solr vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

In-memory, column based data store. Available as appliance or cloud service

A widely used distributed, scalable search engine based on Apache Lucene

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	2.78
Rank	#103	Overall
	#3	Vector DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

www.h2database.com

milvus.io

www.sap.com/products/hana.html

solr.apache.org

www.sqlite.org

Technical documentation

www.h2database.com/html/main.html

milvus.io/docs/overview.md

help.sap.com/hana

solr.apache.org/resources.html

www.sqlite.org/docs.html

Developer

Thomas Mueller

SAP

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2005

2019

2010

2006

2000

Current release

2.2.220, July 2023

2.4.4, May 2024

2.0 SPS07 (April 4, 2023), April 2023

9.6.1, May 2024

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

also available as a cloud based 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

Java

Server operating systems

All OS with a Java VM

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Appliance or cloud-service

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

server-less

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes

supports customizable data types and automatic typing

yes

not rigid because of 'dynamic typing' concept.

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC
ODBC

Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Java

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

SQLScript, R

Java plugins

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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

fine grained access rights according to SQL-standard

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...
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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...
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