DBMS > Atos Standard Common Repository vs. Drizzle vs. Google BigQuery vs. Kingbase vs. Newts

System Properties Comparison Atos Standard Common Repository vs. Drizzle vs. Google BigQuery vs. Kingbase vs. Newts

Editorial information provided by DB-Engines

Name

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

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Large scale data warehouse service with append-only tables

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

Time Series DBMS based on Cassandra

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.45
Rank	#262	Overall
	#123	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

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

cloud.google.com/bigquery

www.kingbase.com.cn

opennms.github.io/newts

Technical documentation

cloud.google.com/bigquery/docs

github.com/OpenNMS/newts/wiki

Developer

Atos Convergence Creators

Drizzle project, originally started by Brian Aker

Google

BeiJing KINGBASE Information technologies inc.

OpenNMS Group

Initial release

2016

2008

2010

1999

2014

Current release

1703

7.2.4, September 2012

V8.0, August 2021

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

C and Java

Java

Server operating systems

Linux

FreeBSD
Linux
OS X

hosted

Linux
Windows

Linux
OS X
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

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

yes

with proprietary extensions

yes

Standard with numerous extensions

APIs and other access methods

LDAP

JDBC

RESTful HTTP/JSON API

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

HTTP REST
Java API

Supported programming languages

All languages with LDAP bindings

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

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

LDAP bind authentication

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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