DBMS > Elasticsearch vs. Milvus vs. Qdrant vs. RocksDB

System Properties Comparison Elasticsearch vs. Milvus vs. Qdrant vs. RocksDB

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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

A high-performance vector database with neural network or semantic-based matching

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

Primary database model

Search engine

Vector DBMS

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	135.35
Rank	#7	Overall
	#1	Search engines

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

www.elastic.co/elasticsearch

milvus.io

github.com/qdrant/qdrant
qdrant.tech

rocksdb.org

Technical documentation

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

milvus.io/docs/overview.md

qdrant.tech/documentation

github.com/facebook/rocksdb/wiki

Developer

Elastic

Qdrant

Facebook, Inc.

Initial release

2010

2019

2021

2013

Current release

8.6, January 2023

2.3.4, January 2024

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache Version 2.0

Open Source

Apache Version 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++, Go

Rust

C++

Server operating systems

All OS with a Java VM

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Docker
Linux
macOS
Windows

Linux

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

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP/JSON API

RESTful HTTP API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

C++ API
Java API

Supported programming languages

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

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

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Collection-level replication

yes

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

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

Role based access control and fine grained access rights

Key-based authentication

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
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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....
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Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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