DBMS > Atos Standard Common Repository vs. atoti vs. RocksDB vs. Splice Machine

System Properties Comparison Atos Standard Common Repository vs. atoti vs. RocksDB vs. Splice Machine

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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

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Key-value store

Object oriented DBMS

Key-value store

Relational DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

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

atoti.io

rocksdb.org

splicemachine.com

Technical documentation

docs.atoti.io

github.com/facebook/rocksdb/wiki

splicemachine.com/how-it-works

Developer

Atos Convergence Creators

ActiveViam

Facebook, Inc.

Splice Machine

Initial release

2016

2013

2014

Current release

1703

9.2.1, May 2024

3.1, March 2021

License

Commercial or Open Source

commercial

free versions available

Open Source

BSD

Open Source

AGPL 3.0, 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

C++

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

optional

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

Multidimensional Expressions (MDX)

yes

APIs and other access methods

LDAP

C++ API
Java API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages with LDAP bindings

C
C++
Go
Java
Perl
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Python

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding, horizontal partitioning

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

yes, multi-version concurrency control (MVCC)

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

LDAP bind authentication

Access rights for users, groups and roles according to SQL-standard

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