DBMS > Atos Standard Common Repository vs. Couchbase vs. HBase vs. Splice Machine vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. Couchbase vs. HBase vs. Splice Machine vs. Yaacomo

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

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

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

Document store
Key-value store

Document store

Wide column store

Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

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

www.couchbase.com

hbase.apache.org

splicemachine.com

yaacomo.com

Technical documentation

docs.couchbase.com

hbase.apache.org/book.html

splicemachine.com/how-it-works

Developer

Atos Convergence Creators

Couchbase, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Splice Machine

Q2WEB GmbH

Initial release

2016

2011

2008

2014

2009

Current release

1703

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

2.3.4, January 2021

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache version 2

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C, C++, Go and Erlang

Java

Server operating systems

Linux

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

Linux
OS X
Solaris
Windows

Android
Linux
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

optional

yes

options to bring your own types, AVRO

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

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

LDAP

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

Java API
RESTful HTTP API
Thrift

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

Coprocessors in Java

yes

Java

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Automatic Sharding

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication
Source-replica 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

selectable on a per-operation basis

Immediate Consistency or Eventual Consistency

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

Single row ACID (across millions of columns)

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

Ephemeral buckets

yes

User concepts

Access control

LDAP bind authentication

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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

fine grained access rights according to SQL-standard

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