DBMS > NSDb vs. Tkrzw vs. YottaDB

System Properties Comparison NSDb vs. Tkrzw vs. YottaDB

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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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

A fast and solid embedded Key-value store

Primary database model

Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

nsdb.io

dbmx.net/tkrzw

yottadb.com

Technical documentation

nsdb.io/Architecture

yottadb.com/resources/documentation

Developer

Mikio Hirabayashi

YottaDB, LLC

Initial release

2017

2020

2001

Current release

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Scala

C++

Server operating systems

Linux
macOS

Docker
Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

by using the Octo plugin

APIs and other access methods

gRPC
HTTP REST
WebSocket

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

Java
Scala

C++
Java
Python
Ruby

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Using Apache Lucene

yes

In-memory capabilities

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

yes

using specific database classes

yes

User concepts

Access control

Users and groups based on OS-security mechanisms

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