DBMS > Atos Standard Common Repository vs. Realm vs. Spark SQL vs. STSdb

System Properties Comparison Atos Standard Common Repository vs. Realm vs. Spark SQL vs. STSdb

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Key-value store

Document store

Relational DBMS

Key-value store

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

Score	7.71
Rank	#52	Overall
	#9	Document stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

realm.io

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

realm.io/docs

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

Developer

Atos Convergence Creators

Realm, acquired by MongoDB in May 2019

Apache Software Foundation

STS Soft SC

Initial release

2016

2014

2011

Current release

1703

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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

Scala

Server operating systems

Linux

Android
Backend: server-less
iOS
Windows

Linux
OS X
Windows

Windows

Data scheme

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

optional

yes

primitive types and user defined types (classes)

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

SQL-like DML and DDL statements

APIs and other access methods

LDAP

JDBC
ODBC

.NET Client API

Supported programming languages

All languages with LDAP bindings

.Net
Java

with Android only
Objective-C
React Native
Swift

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

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

In-Memory realm

User concepts

Access control

LDAP bind authentication

yes

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