DBMS > Elasticsearch vs. Milvus vs. Snowflake vs. SurrealDB

System Properties Comparison Elasticsearch vs. Milvus vs. Snowflake vs. SurrealDB

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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

Cloud-based data warehousing service for structured and semi-structured data

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Search engine

Vector DBMS

Relational DBMS

Document store
Graph DBMS

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	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

www.elastic.co/elasticsearch

milvus.io

www.snowflake.com

surrealdb.com

Technical documentation

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

milvus.io/docs/overview.md

docs.snowflake.net/manuals/index.html

surrealdb.com/docs

Developer

Elastic

Snowflake Computing Inc.

SurrealDB Ltd

Initial release

2010

2019

2014

2022

Current release

8.6, January 2023

2.3.4, January 2024

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache Version 2.0

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

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

Server operating systems

All OS with a Java VM

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

hosted

Linux
macOS
Windows

Data scheme

schema-free

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

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP/JSON API

RESTful HTTP API

CLI Client
JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

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

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

JavaScript (Node.js)
Python

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

by using the 'percolation' feature

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

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

Immediate Consistency

Foreign keys

Referential integrity

yes

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

User concepts

Access control

Role based access control and fine grained access rights

Users with fine-grained authorization concept, user roles and pluggable authentication

yes, based on authentication and database rules

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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