DBMS > Atos Standard Common Repository vs. Axibase vs. Riak TS vs. Splice Machine

System Properties Comparison Atos Standard Common Repository vs. Axibase vs. Riak TS 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

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

Time Series DBMS

Relational DBMS

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

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

axibase.com/docs/atsd/finance

splicemachine.com

Technical documentation

www.tiot.jp/riak-docs/riak/ts/latest

splicemachine.com/how-it-works

Developer

Atos Convergence Creators

Axibase Corporation

Open Source, formerly Basho Technologies

Splice Machine

Initial release

2016

2013

2015

2014

Current release

1703

15585

3.0.0, September 2022

3.1, March 2021

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

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

Erlang

Java

Server operating systems

Linux

Linux
OS X

Linux
OS X
Solaris
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

short, integer, long, float, double, decimal, string

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

restricted

yes

SQL

Support of SQL

SQL-like query language

yes, limited

yes

APIs and other access methods

LDAP

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

HTTP API
Native Erlang Interface

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages with LDAP bindings

Go
Java
PHP
Python
R
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

yes

Erlang

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

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

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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