DBMS > NSDb vs. Riak KV vs. TimescaleDB vs. TimesTen

System Properties Comparison NSDb vs. Riak KV vs. TimescaleDB vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Time Series DBMS

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	0.00
Rank	#385	Overall
	#40	Time Series DBMS

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

nsdb.io

www.timescale.com

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

Technical documentation

nsdb.io/Architecture

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

2017

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

Apache Version 2.0

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

Java, Scala

Erlang

Server operating systems

Linux
macOS

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

yes: int, bigint, decimal, string

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

all fields are automatically indexed

restricted

yes

SQL

Support of SQL

SQL-like query language

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

gRPC
HTTP REST
WebSocket

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

Java
Scala

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

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

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

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

Using Apache Lucene

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, using Riak Security

fine grained access rights according to SQL-standard

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More resources

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