DBMS > EJDB vs. EsgynDB vs. Microsoft Azure Cosmos DB vs. Milvus vs. TigerGraph

System Properties Comparison EJDB vs. EsgynDB vs. Microsoft Azure Cosmos DB vs. Milvus vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Globally distributed, horizontally scalable, multi-model database service

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

github.com/Softmotions/ejdb

www.esgyn.cn

azure.microsoft.com/services/cosmos-db

milvus.io

www.tigergraph.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

learn.microsoft.com/azure/cosmos-db

milvus.io/docs/overview.md

docs.tigergraph.com

Developer

Softmotions

Esgyn

Microsoft

Initial release

2012

2015

2014

2019

2017

Current release

2.3.4, January 2024

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

Apache Version 2.0

commercial

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

C++, Java

C++, Go

C++

Server operating systems

server-less

Linux

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

JSON types

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 properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

SQL-like query language (GSQL)

APIs and other access methods

in-process shared library

ADO.NET
JDBC
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

C++
Java

Server-side scripts

Stored procedures

Java Stored Procedures

JavaScript

yes

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Relationships in graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

Role based access control and fine grained access rights

Role-based access control

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....
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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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