DBMS > DataFS vs. LevelDB vs. NSDb vs. OrigoDB vs. XTDB

System Properties Comparison DataFS vs. LevelDB vs. NSDb vs. OrigoDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

A fully ACID in-memory object graph database

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Object oriented DBMS

Key-value store

Time Series DBMS

Document store
Object oriented DBMS

Document store

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

newdatabase.com

github.com/google/leveldb

nsdb.io

origodb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

dev.mobiland.com/Overview.xsp

github.com/google/leveldb/blob/main/doc/index.md

nsdb.io/Architecture

origodb.com/docs

www.xtdb.com/docs

Developer

Mobiland AG

Google

Robert Friberg et al

Juxt Ltd.

Initial release

2018

2011

2017

2009

under the name LiveDB

2019

Current release

1.1.263, October 2022

1.23, February 2021

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache Version 2.0

Open Source

MIT License

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

C++

Java, Scala

Clojure

Server operating systems

Windows

Illumos
Linux
OS X
Windows

Linux
macOS

Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

User defined using .NET types and collections

yes, extensible-data-notation format

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.

can be achieved using .NET

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

gRPC
HTTP REST
WebSocket

.NET Client API
HTTP API
LINQ

HTTP REST
JDBC

Supported programming languages

.Net
C
C#
C++
VB.Net

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
Scala

.Net

Clojure
Java

Server-side scripts

Stored procedures

yes

Triggers

no, except callback-events from server when changes happened

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Sharding

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

depending on model

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 automatic compression on writes

Using Apache Lucene

yes

Write ahead log

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Windows-Profile

Role based authorization

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