DBMS > Atos Standard Common Repository vs. Dgraph vs. etcd vs. Oracle vs. Solr

System Properties Comparison Atos Standard Common Repository vs. Dgraph vs. etcd vs. Oracle vs. Solr

Editorial information provided by DB-Engines

Name

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

Description

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

Distributed and scalable native Graph DBMS

A distributed reliable key-value store

Widely used RDBMS

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Key-value store

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

Spatial DBMS

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

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

dgraph.io

etcd.io
github.com/etcd-io/etcd

www.oracle.com/database

solr.apache.org

Technical documentation

dgraph.io/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.oracle.com/en/database

solr.apache.org/resources.html

Developer

Atos Convergence Creators

Dgraph Labs, Inc.

Oracle

Apache Software Foundation

Initial release

2016

1980

2006

Current release

1703

3.4, August 2019

23c, September 2023

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Apache Version 2

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 and C++

Java

Server operating systems

Linux

Linux
OS X
Windows

FreeBSD
Linux
Windows

experimental

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

Schemaless in JSON and XML columns

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

optional

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

LDAP

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

gRPC
JSON over HTTP

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

PL/SQL

also stored procedures in Java possible

Java plugins

Triggers

yes

yes, watching key changes

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

yes

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication via Raft

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual 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

isolation level can be parameterized

optimistic locking

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

LDAP bind authentication

Planned for future releases

fine grained access rights according to SQL-standard

yes

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