DBMS > EJDB vs. Spark SQL vs. SWC-DB vs. Transbase vs. Ultipa

System Properties Comparison EJDB vs. Spark SQL vs. SWC-DB vs. Transbase vs. Ultipa

Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

A high performance, scalable Wide Column DBMS

A resource-optimized, high-performance, universally applicable RDBMS

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

github.com/Softmotions/ejdb

spark.apache.org/sql

github.com/kashirin-alex/swc-db
www.swcdb.org

www.transaction.de/en/products/transbase.html

www.ultipa.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

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

www.transaction.de/en/products/transbase/features.html

www.ultipa.com/document

Developer

Softmotions

Apache Software Foundation

Alex Kashirin

Transaction Software GmbH

Ultipa

Initial release

2012

2014

2020

1987

2019

Current release

3.5.0 ( 2.13), September 2023

0.5, April 2021

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

Open Source

GPL V3

commercial

free development license

commercial

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

Scala

C++

C and C++

Server operating systems

server-less

Linux
OS X
Windows

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

SQL-like query language

yes

APIs and other access methods

in-process shared library

JDBC
ODBC

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Java
Python
R
Scala

C++

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

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

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

User concepts

Access control

fine grained access rights according to SQL-standard

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