DBMS > Elasticsearch vs. Milvus vs. Oracle Berkeley DB vs. Tkrzw

System Properties Comparison Elasticsearch vs. Milvus vs. Oracle Berkeley DB vs. Tkrzw

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

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

Widely used in-process key-value store

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Search engine

Vector DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	128.79
Rank	#8	Overall
	#1	Search engines

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Website

www.elastic.co/elasticsearch

milvus.io

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

dbmx.net/tkrzw

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

milvus.io/docs/overview.md

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Elastic

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Mikio Hirabayashi

Initial release

2010

2019

1994

2020

Current release

8.6, January 2023

2.4.4, May 2024

18.1.40, May 2020

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache Version 2.0

Open Source

commercial license available

Open Source

Apache Version 2.0

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

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

All OS with a Java VM

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

only with the Berkeley DB XML edition

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Java API
RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

by using the 'percolation' feature

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

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.

Memcached and Redis integration

yes

using specific database classes

User concepts

Access control

Role based access control and fine grained access rights

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

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2017
2 January 2018, Paul Andlinger, Matthias Gelbmann

Elasticsearch moved into the top 10 most popular database management systems
3 July 2017, Matthias Gelbmann

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

show all

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

Unsecured Elasticsearch cluster exposes over 3M vehicle records
18 September 2024, SC Media

AWS hands OpenSearch to Linux Foundation – is this why Elasticsearch was made open source again?
16 September 2024, DevClass

The Elasticsearch cloud now subject to open-source licence
3 September 2024, TechHQ

Elasticsearch Open Inference API Extends Support for Hugging Face Models with Semantic Text
12 September 2024, Yahoo Finance

Elastic Announces Open Source License for Elasticsearch and Kibana Source Code
29 August 2024, Business Wire

provided by Google News

AI-Powered Search Engine With Milvus Vector Database on Vultr - SitePoint
30 August 2024, SitePoint

How NVIDIA GPU Acceleration Supercharged Milvus Vector Database
26 March 2024, The New Stack

IBM watsonx.data’s integrated vector database: unify, prepare, and deliver your data for AI
9 April 2024, IBM

10 top vector database options for similarity searches
8 August 2024, TechTarget

Milvus 2.4 Unveils Game-Changing Features for Enhanced Vector Search
20 March 2024, GlobeNewswire

provided by Google News

What is NoSQL (Not Only SQL database)?
28 February 2022, TechTarget

Margo I. Seltzer
18 August 2020, Berkman Klein Center

Oracle acquires Sleepycat for code
17 August 2016, East Bay Times

Database Trends Report: SQL Beats NoSQL, MySQL Most Popular
5 March 2019, ADT Magazine

How to store financial market data for backtesting
26 January 2019, Towards Data Science

provided by Google News

Share this page