DBMS > Cloudflare Workers KV vs. etcd vs. Hypertable vs. LevelDB vs. Microsoft Azure Cosmos DB

System Properties Comparison Cloudflare Workers KV vs. etcd vs. Hypertable vs. LevelDB vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

A distributed reliable key-value store

An open source BigTable implementation based on distributed file systems such as Hadoop

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Key-value store

Wide column store

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.32
Rank	#283	Overall
	#40	Key-value stores

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

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

Website

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

etcd.io
github.com/etcd-io/etcd

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

Technical documentation

developers.cloudflare.com/kv/api

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

github.com/google/leveldb/blob/main/doc/index.md

learn.microsoft.com/azure/cosmos-db

Developer

Cloudflare

Hypertable Inc.

Google

Microsoft

Initial release

2018

2009

2011

2014

Current release

3.4, August 2019

0.9.8.11, March 2016

1.23, February 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GNU version 3. Commercial license available

Open Source

BSD

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.

Implementation language

C++

Server operating systems

hosted

FreeBSD
Linux
Windows

experimental

Linux
OS X
Windows

an inofficial Windows port is available

Illumos
Linux
OS X
Windows

hosted

Data scheme

schema-free

Typing

predefined data types such as float or date

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

restricted

only exact value or prefix value scans

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

APIs and other access methods

HTTP REST
Proprietary protocol

gRPC
JSON over HTTP

C++ API
Thrift

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

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

C++
Java
Perl
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

Server-side scripts

Stored procedures

JavaScript

Triggers

yes, watching key changes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

selectable replication factor on file system level

none

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*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

User concepts

Access control

Access rights can be defined down to the item level

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