DBMS > Elasticsearch vs. EsgynDB vs. Milvus vs. ObjectBox vs. RocksDB

System Properties Comparison Elasticsearch vs. EsgynDB vs. Milvus vs. ObjectBox vs. RocksDB

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Extremely fast embedded database for small devices, IoT and Mobile

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Time Series DBMS

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

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	2.78
Rank	#103	Overall
	#3	Vector DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

www.elastic.co/elasticsearch

Technical documentation

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

milvus.io/docs/overview.md

docs.objectbox.io

github.com/facebook/rocksdb/wiki

Developer

Elastic

Esgyn

ObjectBox Limited

Facebook, Inc.

Initial release

2010

2015

2019

2017

2013

Current release

8.6, January 2023

2.3.4, January 2024

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

Open Source

BSD

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++, Java

C++, Go

C and C++

C++

Server operating systems

All OS with a Java VM

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Android
iOS
Linux
macOS
Windows

Linux

Data scheme

schema-free

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

yes

schema-free

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

RESTful HTTP API

Proprietary native API

C++ API
Java API

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

online/offline synchronization between client and server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

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

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

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...
» 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

News

The on-device Vector Database for Android and Java
29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

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.