DBMS > Atos Standard Common Repository vs. DataFS vs. Google Cloud Spanner vs. TigerGraph vs. Transwarp Hippo

System Properties Comparison Atos Standard Common Repository vs. DataFS vs. Google Cloud Spanner vs. TigerGraph vs. Transwarp Hippo

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Document store
Key-value store

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Score	0.05
Rank	#386	Overall
	#15	Vector DBMS

Website

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

newdatabase.com

cloud.google.com/spanner

www.tigergraph.com

www.transwarp.cn/en/subproduct/hippo

Technical documentation

dev.mobiland.com/Overview.xsp

cloud.google.com/spanner/docs

docs.tigergraph.com

Developer

Atos Convergence Creators

Mobiland AG

Google

Initial release

2016

2018

2017

2023

Current release

1703

1.1.263, October 2022

1.0, May 2023

License

Commercial or Open Source

commercial

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++

Server operating systems

Linux

Windows

hosted

Linux

Linux
macOS

Data scheme

Schema and schema-less with LDAP views

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

optional

yes

Vector, Numeric and String

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

Query statements complying to ANSI 2011

SQL-like query language (GSQL)

APIs and other access methods

LDAP

.NET Client API
Proprietary client DLL
WinRT client

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

All languages with LDAP bindings

.Net
C
C#
C++
VB.Net

Go
Java
JavaScript (Node.js)
Python

C++
Java

C++
Java
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Proprietary Sharding system

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication with 3 replicas for regional instances.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Relationships in graphs

Transaction concepts

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

Atomic execution of specific operations

ACID

Strict serializable isolation

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

Windows-Profile

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

Role-based access control

Role based access control and fine grained access rights

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