DBMS > Apache Doris vs. Atos Standard Common Repository vs. Splice Machine vs. Yaacomo

System Properties Comparison Apache Doris vs. Atos Standard Common Repository vs. Splice Machine vs. Yaacomo

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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.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

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

splicemachine.com

yaacomo.com

Technical documentation

github.com/apache/doris/wiki

splicemachine.com/how-it-works

Developer

Apache Software Foundation, originally contributed from Baidu

Atos Convergence Creators

Splice Machine

Q2WEB GmbH

Initial release

2017

2016

2014

2009

Current release

1.2.2, February 2023

1703

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Android
Linux
Windows

Data scheme

yes

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

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

yes

APIs and other access methods

JDBC
MySQL client

LDAP

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC

Supported programming languages

Java

All languages with LDAP bindings

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

Server-side scripts

Stored procedures

user defined functions

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

cell division

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication
Source-replica 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

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

fine grained access rights according to SQL-standard

LDAP bind authentication

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

fine grained access rights according to SQL-standard

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More resources

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