DBMS > ArcadeDB vs. BaseX vs. NSDb vs. Spark SQL

System Properties Comparison ArcadeDB vs. BaseX vs. NSDb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Native XML DBMS

Time Series DBMS

Relational DBMS

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

Score	0.09
Rank	#355	Overall
	#48	Document stores
	#37	Graph DBMS
	#52	Key-value stores
	#34	Time Series DBMS

Score	1.90
Rank	#136	Overall
	#4	Native XML DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

docs.arcadedb.com

docs.basex.org

nsdb.io/Architecture

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Arcade Data

BaseX GmbH

Apache Software Foundation

Initial release

2021

2007

2017

2014

Current release

September 2021

10.7, August 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD license

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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

Java, Scala

Scala

Server operating systems

All OS with a Java VM

Linux
OS X
Windows

Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

XQuery supports types

yes: int, bigint, decimal, string

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.

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language, no joins

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

gRPC
HTTP REST
WebSocket

JDBC
ODBC

Supported programming languages

Java

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

Java
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

via events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

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

relationship in graphs

Transaction concepts

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

ACID

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

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