DBMS > Amazon Neptune vs. Hawkular Metrics vs. Memcached vs. Microsoft SQL Server vs. PlanetScale

System Properties Comparison Amazon Neptune vs. Hawkular Metrics vs. Memcached vs. Microsoft SQL Server vs. PlanetScale

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

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

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

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	1.59
Rank	#151	Overall
	#70	Relational DBMS

Website

aws.amazon.com/neptune

www.hawkular.org

www.memcached.org

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

planetscale.com

Technical documentation

aws.amazon.com/neptune/developer-resources

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

github.com/memcached/memcached/wiki

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

planetscale.com/docs

Developer

Amazon

Community supported by Red Hat

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

PlanetScale

Initial release

2017

2014

2003

1989

2020

Current release

1.6.25, March 2024

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

BSD license

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Server operating systems

hosted

Linux
OS X
Windows

FreeBSD
Linux
OS X
Unix
Windows

Linux
Windows

Docker
Linux
macOS

Data scheme

schema-free

yes

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

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

HTTP REST

Proprietary protocol

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

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

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

yes

proprietary syntax

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

selectable replication factor

based on Cassandra

none

Repcached, a Memcached patch, provides this functionallity

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

with encyption-at-rest

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

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

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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