DBMS > Atos Standard Common Repository vs. Drizzle vs. Google Cloud Datastore vs. Oracle Rdb vs. SwayDB

System Properties Comparison Atos Standard Common Repository vs. Drizzle vs. Google Cloud Datastore vs. Oracle Rdb vs. SwayDB

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.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Key-value store

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

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

cloud.google.com/datastore

www.oracle.com/database/technologies/related/rdb.html

swaydb.simer.au

Technical documentation

cloud.google.com/datastore/docs

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Atos Convergence Creators

Drizzle project, originally started by Brian Aker

Google

Oracle, originally developed by Digital Equipment Corporation (DEC)

Simer Plaha

Initial release

2016

2008

1984

2018

Current release

1703

7.2.4, September 2012

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Open Source

GNU Affero GPL V3.0

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++

Scala

Server operating systems

Linux

FreeBSD
Linux
OS X

hosted

HP Open VMS

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

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

schema-free

Typing

predefined data types such as float or date

optional

yes

yes, details here

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

SQL-like query language (GQL)

yes

APIs and other access methods

LDAP

JDBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

C
C++
Java
PHP

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Kotlin
Scala

Server-side scripts

Stored procedures

using Google App Engine

Triggers

yes

hooks for callbacks inside the server can be used.

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

yes, on a single node

Atomic execution of operations

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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