DBMS > CrateDB vs. Faircom EDGE vs. Hypertable vs. Riak TS

System Properties Comparison CrateDB vs. Faircom EDGE vs. Hypertable vs. Riak TS

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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

Distributed Database based on Lucene

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

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

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store
Relational DBMS

Wide column store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	0.02
Rank	#370	Overall
	#55	Key-value stores
	#157	Relational DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

cratedb.com

www.faircom.com/products/faircom-edge

Technical documentation

cratedb.com/docs

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

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

Developer

Crate

FairCom Corporation

Hypertable Inc.

Open Source, formerly Basho Technologies

Initial release

2013

1979

2009

2015

Current release

5.8.1, August 2024

V3, October 2020

0.9.8.11, March 2016

3.0.0, September 2022

License

Commercial or Open Source

Open Source

commercial

Restricted, free version available

Open Source

GNU version 3. Commercial license available

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

ANSI C, C++

C++

Erlang

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X

Data scheme

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

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

only exact value or prefix value scans

restricted

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

ANSI SQL queries

yes, limited

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

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

C++ API
Thrift

HTTP API
Native Erlang Interface

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

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

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

Server-side scripts

Stored procedures

user defined functions (Javascript)

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

Customizable business rules for partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

Synchronous and asynchronous realtime replication based on transaction logs

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

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

when using SQL

links between datasets can be stored

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

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

User concepts

Access control

rights management via user accounts

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

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