DBMS > Hawkular Metrics vs. Memcached vs. Microsoft SQL Server vs. SurrealDB vs. Teradata Aster

System Properties Comparison Hawkular Metrics vs. Memcached vs. Microsoft SQL Server vs. SurrealDB 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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

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

Microsofts flagship relational DBMS

A fully ACID transactional, developer-friendly, multi-model DBMS

Platform for big data analytics on multistructured data sources and types

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

Document store
Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

www.hawkular.org

www.memcached.org

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

surrealdb.com

Technical documentation

www.hawkular.org/hawkular-metrics/docs/user-guide

github.com/memcached/memcached/wiki

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

surrealdb.com/docs

Developer

Community supported by Red Hat

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

SurrealDB Ltd

Teradata

Initial release

2014

2003

1989

2022

2005

Current release

1.6.25, March 2024

SQL Server 2022, November 2022

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD license

commercial

restricted free version is available

Open Source

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Rust

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X
Unix
Windows

Linux
Windows

Linux
macOS
Windows

Linux

Data scheme

schema-free

yes

schema-free

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

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

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

HTTP REST

Proprietary protocol

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

GraphQL
RESTful HTTP API
WebSocket

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Go
Java
Python
Ruby

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

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

Deno
Go
JavaScript (Node.js)
Rust

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

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

Repcached, a Memcached patch, provides this functionallity

yes, but depending on the SQL-Server Edition

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

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

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

In-memory capabilities

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

yes

User concepts

Access control

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

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