DBMS > Bangdb vs. Cachelot.io vs. EJDB vs. Milvus vs. OpenEdge

System Properties Comparison Bangdb vs. Cachelot.io vs. EJDB vs. Milvus vs. OpenEdge

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

In-memory caching system

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

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

Application development environment with integrated database management system

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Website

bangdb.com

cachelot.io

github.com/Softmotions/ejdb

milvus.io

www.progress.com/openedge

Technical documentation

docs.bangdb.com

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

milvus.io/docs/overview.md

documentation.progress.com/output/ua/OpenEdge_latest

Developer

Sachin Sinha, BangDB

Softmotions

Progress Software Corporation

Initial release

2012

2015

2012

2019

1984

Current release

BangDB 2.0, October 2021

2.4.4, May 2024

OpenEdge 12.2, March 2020

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Simplified BSD License

Open Source

GPLv2

Open Source

Apache Version 2.0

commercial

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

C, C++

C++

C++, Go

Server operating systems

Linux

FreeBSD
Linux
OS X

server-less

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

string, integer, double, bool, date, object_id

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

close to SQL 92

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Memcached protocol

in-process shared library

RESTful HTTP API

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

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

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

yes

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

none

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Role based access control and fine grained access rights

Users and groups

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

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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