DBMS > Amazon DynamoDB vs. Cloudflare Workers KV vs. H2GIS vs. HBase vs. Sphinx

System Properties Comparison Amazon DynamoDB vs. Cloudflare Workers KV vs. H2GIS vs. HBase vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

Spatial extension of H2

Wide-column store based on Apache Hadoop and on concepts of BigTable

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Document store
Key-value store

Secondary database models

Relational DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.39
Rank	#274	Overall
	#39	Key-value stores

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

aws.amazon.com/dynamodb

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

www.h2gis.org

hbase.apache.org

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/dynamodb

developers.cloudflare.com/kv/api

www.h2gis.org/docs/home

hbase.apache.org/book.html

sphinxsearch.com/docs

Developer

Amazon

Cloudflare

CNRS

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Sphinx Technologies Inc.

Initial release

2012

2018

2013

2008

2001

Current release

2.3.4, January 2021

3.5.1, February 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

LGPL 3.0

Open Source

Apache version 2

Open Source

GPL version 2, commercial licence available

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

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

HTTP REST
Proprietary protocol

Java API
RESTful HTTP API
Thrift

Proprietary protocol

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

Java

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

based on H2

yes

Coprocessors in Java

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

yes

based on H2

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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