DBMS > Atos Standard Common Repository vs. Bangdb vs. GigaSpaces vs. Google Cloud Datastore

System Properties Comparison Atos Standard Common Repository vs. Bangdb vs. GigaSpaces vs. Google Cloud Datastore

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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

Converged and high performance database for device data, events, time series, document and graph

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

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

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Time Series DBMS

Document store
Object oriented DBMS

Values are user defined objects

Document store

Secondary database models

Spatial DBMS

Graph DBMS
Search engine

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Website

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

bangdb.com

www.gigaspaces.com

cloud.google.com/datastore

Technical documentation

docs.bangdb.com

docs.gigaspaces.com/latest/landing.html

cloud.google.com/datastore/docs

Developer

Atos Convergence Creators

Sachin Sinha, BangDB

Gigaspaces Technologies

Google

Initial release

2016

2012

2000

2008

Current release

1703

BangDB 2.0, October 2021

15.5, September 2020

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

Apache Version 2; Commercial licenses available

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, C++

Java, C++, .Net

Server operating systems

Linux

Linux
macOS
Solaris
Windows

hosted

Data scheme

Schema and schema-less with LDAP views

schema-free

Typing

predefined data types such as float or date

optional

yes: string, long, double, int, geospatial, stream, events

yes

yes, details here

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

XML can be used for describing objects metadata

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-99 for query and DML statements

SQL-like query language (GQL)

APIs and other access methods

LDAP

Proprietary protocol
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

Supported programming languages

All languages with LDAP bindings

C
C#
C++
Java
Python

.Net
C++
Java
Python
Scala

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

Server-side scripts

Stored procedures

yes

using Google App Engine

Triggers

yes

yes, Notifications (with Streaming only)

yes, event driven architecture

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

yes

User concepts

Access control

LDAP bind authentication

yes (enterprise version only)

Role-based access control

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

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