DBMS > Bangdb vs. Cloudflare Workers KV vs. Memcached vs. Microsoft Azure Cosmos DB vs. SwayDB

System Properties Comparison Bangdb vs. Cloudflare Workers KV vs. Memcached vs. Microsoft Azure Cosmos DB vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

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

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

In-memory key-value store, originally intended for caching

Globally distributed, horizontally scalable, multi-model database service

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Graph DBMS
Time Series DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

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	0.39
Rank	#274	Overall
	#39	Key-value stores

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

bangdb.com

www.cloudflare.com/developer-platform/workers-kv

www.memcached.org

azure.microsoft.com/services/cosmos-db

swaydb.simer.au

Technical documentation

docs.bangdb.com

developers.cloudflare.com/kv/api

github.com/memcached/memcached/wiki

learn.microsoft.com/azure/cosmos-db

Developer

Sachin Sinha, BangDB

Cloudflare

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

Simer Plaha

Initial release

2012

2018

2003

2014

2018

Current release

BangDB 2.0, October 2021

1.6.27, May 2024

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

BSD license

commercial

Open Source

GNU Affero GPL V3.0

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.

Implementation language

C, C++

Scala

Server operating systems

Linux

hosted

FreeBSD
Linux
OS X
Unix
Windows

hosted

Data scheme

schema-free

Typing

predefined data types such as float or date

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

yes

JSON types

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

secondary, composite, nested, reverse, geospatial

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP REST
Proprietary protocol

Proprietary protocol

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

Supported programming languages

C
C#
C++
Java
Python

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

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

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

JavaScript

Triggers

yes, Notifications (with Streaming only)

JavaScript

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

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

none

Repcached, a Memcached patch, provides this functionallity

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

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)

yes

using SASL (Simple Authentication and Security Layer) protocol

Access rights can be defined down to the item level

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