DBMS > NSDb vs. OrigoDB vs. PlanetScale vs. XTDB

System Properties Comparison NSDb vs. OrigoDB vs. PlanetScale vs. XTDB

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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 fully ACID in-memory object graph database

Scalable, distributed, serverless MySQL database platform built on top of Vitess

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

Primary database model

Time Series DBMS

Document store
Object oriented DBMS

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

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

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

nsdb.io

origodb.com

planetscale.com

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

Technical documentation

Developer

Robert Friberg et al

PlanetScale

Juxt Ltd.

Initial release

2017

2009

under the name LiveDB

2020

2019

Current release

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Scala

Clojure

Server operating systems

Linux
macOS

Linux
Windows

Docker
Linux
macOS

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

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

User defined using .NET types and collections

yes

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

yes

with proprietary extensions

limited SQL, making use of Apache Calcite

APIs and other access methods

gRPC
HTTP REST
WebSocket

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
MySQL protocol
ODBC

HTTP REST
JDBC

Supported programming languages

Java
Scala

.Net

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Clojure
Java

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
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

Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

depending on model

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

Using Apache Lucene

yes

Write ahead log

yes

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

Role based authorization

Users with fine-grained authorization concept

no user groups or roles

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