DBMS > Alibaba Cloud Table Store vs. Drizzle vs. Faircom EDGE vs. Kinetica vs. Riak TS

System Properties Comparison Alibaba Cloud Table Store vs. Drizzle vs. Faircom EDGE vs. Kinetica vs. Riak TS

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Fully vectorized database across both GPUs and CPUs

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Wide column store

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

www.alibabacloud.com/product/table-store

www.faircom.com/products/faircom-edge

www.kinetica.com

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

docs.kinetica.com

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Alibaba

Drizzle project, originally started by Brian Aker

FairCom Corporation

Kinetica

Open Source, formerly Basho Technologies

Initial release

2016

2008

1979

2012

2015

Current release

7.2.4, September 2012

V3, October 2020

7.1, August 2021

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Restricted, free version available

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

ANSI C, C++

C, C++

Erlang

Server operating systems

hosted

FreeBSD
Linux
OS X

Android
Linux

ARM, x86
Raspbian
Windows

Linux

Linux
OS X

Data scheme

schema-free

yes

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

restricted

SQL

Support of SQL

yes

with proprietary extensions

yes

ANSI SQL queries

SQL-like DML and DDL statements

yes, limited

APIs and other access methods

HTTP API

JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

Java
Python

C
C++
Java
PHP

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

C++
Java
JavaScript (Node.js)
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Erlang

Triggers

hooks for callbacks inside the server can be used.

yes

triggers when inserted values for one or more columns fall within a specified range

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

links between datasets can be stored

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

GPU vRAM or System RAM

User concepts

Access control

Access rights based on subaccounts and tokens

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Access rights for users and roles on table level

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