DBMS > BigchainDB vs. Cachelot.io vs. Riak KV vs. TimescaleDB vs. TimesTen

System Properties Comparison BigchainDB vs. Cachelot.io vs. Riak KV vs. TimescaleDB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

In-memory caching system

Distributed, fault tolerant key-value store

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Document store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.76
Rank	#216	Overall
	#36	Document stores

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

www.bigchaindb.com

cachelot.io

www.timescale.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

bigchaindb.readthedocs.io/en/latest

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

docs.timescale.com

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

OpenSource, formerly Basho Technologies

Timescale

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2016

2015

2009

2017

1998

Current release

3.2.0, December 2022

2.15.0, May 2024

Release 22.1

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

Simplified BSD License

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Python

C++

Erlang

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux
OS X

Linux
OS X
Windows

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

restricted

yes

SQL

Support of SQL

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

CLI Client
RESTful HTTP API

Memcached protocol

HTTP API
Native Erlang Interface

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

Erlang

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

PL/SQL

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

no "single point of failure"

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

selectable replication factor

Source-replica replication with hot standby and reads on replicas

Multi-source replication
Source-replica replication

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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

links between data sets can be stored

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

User concepts

Access control

yes

yes, using Riak Security

fine grained access rights according to SQL-standard

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More resources

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