DBMS > Cloudflare Workers KV vs. Microsoft SQL Server vs. OpenTSDB vs. Sphinx vs. Teradata Aster

System Properties Comparison Cloudflare Workers KV vs. Microsoft SQL Server vs. OpenTSDB vs. Sphinx vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be 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.

Microsofts flagship relational DBMS

Scalable Time Series DBMS based on HBase

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

Platform for big data analytics on multistructured data sources and types

Primary database model

Secondary database models

Document store
Graph DBMS
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	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.68
Rank	#146	Overall
	#12	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

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

www.microsoft.com/en-us/sql-server

opentsdb.net

sphinxsearch.com

Technical documentation

developers.cloudflare.com/kv/api

learn.microsoft.com/en-US/sql/sql-server

opentsdb.net/docs/build/html/index.html

sphinxsearch.com/docs

Developer

Cloudflare

Microsoft

currently maintained by Yahoo and other contributors

Sphinx Technologies Inc.

Teradata

Initial release

2018

1989

2011

2001

2005

Current release

SQL Server 2022, November 2022

3.5.1, February 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

LGPL

Open Source

GPL version 2, commercial licence available

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++

Java

C++

Server operating systems

hosted

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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

in Aster File Store

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

yes

APIs and other access methods

HTTP REST
Proprietary protocol

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API
Telnet API

Proprietary protocol

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Erlang
Go
Java
Python
R
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

based on HBase

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

selectable replication factor

based on HBase

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

based on HBase

Immediate Consistency or Eventual Consistency depending on configuration

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

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

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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