DBMS > BaseX vs. MongoDB vs. Spark SQL

System Properties Comparison BaseX vs. MongoDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

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

Primary database model

Native XML DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0

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

Score	2.11
Rank	#137	Overall
	#4	Native XML DBMS

Score	458.78
Rank	#5	Overall
	#1	Document stores

Score	20.15
Rank	#36	Overall
	#22	Relational DBMS

Website

basex.org

www.mongodb.com

spark.apache.org/sql

Technical documentation

docs.basex.org

docs.mongodb.com/manual

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

Developer

BaseX GmbH

MongoDB, Inc

Apache Software Foundation

Initial release

2007

2009

2014

Current release

10.4, December 2022

6.0.1, August 2022

3.3.0 ( 2.13), June 2022

License

Commercial or Open Source

Open Source

BSD license

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

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Implementation language

Java

C++

Scala

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

yes

Typing

predefined data types such as float or date

XQuery supports types

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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

SQL

Support of SQL

Read-only SQL queries via the MongoDB Connector for BI

SQL-like DML and DDL statements

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

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

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes

via events

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

none

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

can be individually decided for each write operation

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

multiple readers, single writer

Multi-document ACID Transactions with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional, enabled by default

yes

In-memory capabilities

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

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

Access rights for users and roles

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