DBMS > BaseX vs. Spark SQL vs. TDSQL for MySQL vs. TerarkDB

System Properties Comparison BaseX vs. Spark SQL vs. TDSQL for MySQL vs. TerarkDB

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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.

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

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Native XML DBMS

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.85
Rank	#205	Overall
	#95	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

basex.org

spark.apache.org/sql

www.tencentcloud.com/products/dcdb

github.com/bytedance/terarkdb

Technical documentation

docs.basex.org

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

www.tencentcloud.com/document/product/1042

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

BaseX GmbH

Apache Software Foundation

Tencent

ByteDance, originally Terark

Initial release

2007

2014

2013

2016

Current release

10.7, August 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSD license

Open Source

Apache 2.0

commercial

restricted open source version available

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

C++

Server operating systems

Linux
OS X
Windows

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

XQuery supports types

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

SQL-like DML and DDL statements

yes

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

JDBC
ODBC

C++ API
Java API

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

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

Java
Python
R
Scala

C
C#
C++
Java
PHP
Python
Ruby

C++
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

via events

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
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

Foreign keys

Referential integrity

Transaction concepts

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

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

Users with fine-grained authorization concept

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