DBMS > 4D vs. Atos Standard Common Repository vs. ClickHouse vs. Splice Machine vs. Teradata Aster

System Properties Comparison 4D vs. Atos Standard Common Repository vs. ClickHouse vs. Splice Machine vs. Teradata Aster

Editorial information provided by DB-Engines

Name

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

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Application development environment with integrated database management system

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

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

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

Platform for big data analytics on multistructured data sources and types

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.4d.com

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

clickhouse.com

splicemachine.com

Technical documentation

developer.4d.com

clickhouse.com/docs

splicemachine.com/how-it-works

Developer

4D, Inc

Atos Convergence Creators

Clickhouse Inc.

Splice Machine

Teradata

Initial release

1984

2016

2014

2005

Current release

v20, April 2023

1703

v24.4.1.2088-stable, May 2024

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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.

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Implementation language

Java

C++

Java

Server operating systems

OS X
Windows

Linux

FreeBSD
Linux
macOS

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

Schema and schema-less with LDAP views

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

Close to ANSI SQL (SQL/JSON + extensions)

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

LDAP

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

4D proprietary IDE
PHP

All languages with LDAP bindings

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

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

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

Java

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

key based and custom

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Multi-source replication
Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Users and groups

LDAP bind authentication

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

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

fine grained access rights according to SQL-standard

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