DBMS > EventStoreDB vs. MarkLogic vs. Spark SQL vs. SQL.JS

System Properties Comparison EventStoreDB vs. MarkLogic vs. Spark SQL vs. SQL.JS

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Editorial information provided by DB-Engines

Name

Description

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Operational and transactional Enterprise NoSQL database

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

Port of SQLite to JavaScript

Primary database model

Event Store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

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

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Website

www.eventstore.com

www.progress.com/marklogic

spark.apache.org/sql

sql.js.org

Technical documentation

developers.eventstore.com

www.progress.com/marklogic/documentation

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

sql.js.org/documentation/index.html

Developer

Event Store Limited

MarkLogic Corp.

Apache Software Foundation

Alon Zakai

enhancements implemented by others

Initial release

2012

2001

2014

2012

Current release

21.2, February 2021

11.0, December 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

commercial

restricted free version is available

Open Source

Apache 2.0

Open Source

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++

Scala

JavaScript

Server operating systems

Linux
Windows

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

schema-free

Schema can be enforced

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

JDBC
ODBC

JavaScript API

Supported programming languages

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java
Python
R
Scala

JavaScript

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

except by serializing a db to a file

In-memory capabilities

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

yes, with Range Indexes

yes

User concepts

Access control

Role-based access control at the document and subdocument levels

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More resources

DB-Engines blog posts

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