DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Hawkular Metrics vs. KeyDB vs. Microsoft Azure Table Storage vs. OrigoDB

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Hawkular Metrics vs. KeyDB vs. Microsoft Azure Table Storage vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

A Wide Column Store for rapid development using massive semi-structured datasets

A fully ACID in-memory object graph database

Primary database model

Document store
Object oriented DBMS

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

Score	0.68
Rank	#231	Overall
	#105	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

www.hawkular.org

github.com/Snapchat/KeyDB
keydb.dev

azure.microsoft.com/en-us/services/storage/tables

origodb.com

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

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

docs.keydb.dev

origodb.com/docs

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Community supported by Red Hat

EQ Alpha Technology Ltd.

Microsoft

Robert Friberg et al

Initial release

2016

2014

2019

2012

2009

under the name LiveDB

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

BSD-3

commercial

Open Source

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
OS X
Windows

Linux

hosted

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

by using the Redis Search module

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC

HTTP REST

Proprietary protocol

RESP - REdis Serialization Protoco

RESTful HTTP API

.NET Client API
HTTP API
LINQ

Supported programming languages

C
Java
Perl
Python
R

Go
Java
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

.Net

Server-side scripts

Stored procedures

yes

Lua

yes

Triggers

yes

via Hawkular Alerting

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

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

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

Write ahead log

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

simple password-based access control and ACL

Access rights based on private key authentication or shared access signatures

Role based authorization

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