DBMS > Cloudflare Workers KV vs. Elasticsearch vs. Quasardb vs. SAP HANA vs. Sphinx

System Properties Comparison Cloudflare Workers KV vs. Elasticsearch vs. Quasardb vs. SAP HANA vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Distributed, high-performance timeseries database

In-memory, column based data store. Available as appliance or cloud service

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

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

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

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

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

www.elastic.co/elasticsearch

quasar.ai

www.sap.com/products/hana.html

sphinxsearch.com

Technical documentation

developers.cloudflare.com/kv/api

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

doc.quasar.ai/master

help.sap.com/hana

sphinxsearch.com/docs

Developer

Cloudflare

Elastic

quasardb

SAP

Sphinx Technologies Inc.

Initial release

2018

2010

2009

2010

2001

Current release

8.6, January 2023

3.14.1, January 2024

2.0 SPS07 (April 4, 2023), April 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Elastic License

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

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

All OS with a Java VM

BSD
Linux
OS X
Windows

Appliance or cloud-service

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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 search fields are automatically indexed

yes

with tags

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language (SphinxQL)

APIs and other access methods

HTTP REST
Proprietary protocol

Java API
RESTful HTTP/JSON API

HTTP API

JDBC
ODBC

Proprietary protocol

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

SQLScript, R

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

yes

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication with selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

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.

Memcached and Redis integration

yes

Transient mode

yes

User concepts

Access control

Cryptographically strong user authentication and audit trail

yes

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