DBMS > Apache Drill vs. Faircom DB vs. Hypertable vs. Riak TS vs. SWC-DB

System Properties Comparison Apache Drill vs. Faircom DB vs. Hypertable vs. Riak TS vs. SWC-DB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

An open source BigTable implementation based on distributed file systems such as Hadoop

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

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Relational DBMS

Key-value store
Relational DBMS

Wide column store

Time Series DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

drill.apache.org

www.faircom.com/products/faircom-db

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

drill.apache.org/docs

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

Developer

Apache Software Foundation

FairCom Corporation

Hypertable Inc.

Open Source, formerly Basho Technologies

Alex Kashirin

Initial release

2012

1979

2009

2015

2020

Current release

1.20.3, January 2023

V12, November 2020

0.9.8.11, March 2016

3.0.0, September 2022

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Restricted, free version available

Open Source

GNU version 3. Commercial license available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

C++

Erlang

C++

Server operating systems

Linux
OS X
Windows

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X

Linux

Data scheme

schema-free

schema free, schema optional, schema required, partial schema,

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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.

Secondary indexes

yes

restricted

only exact value or prefix value scans

restricted

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes, ANSI SQL with proprietary extensions

yes, limited

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

C++ API
Thrift

HTTP API
Native Erlang Interface

Proprietary protocol
Thrift

Supported programming languages

C++

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C++
Java
Perl
PHP
Python
Ruby

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

C++

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor on file system level

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

Yes, tunable from durable to delayed durability to in-memory

yes

In-memory capabilities

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

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

Fine grained access rights according to SQL-standard with additional protections for files

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