DBMS > Bangdb vs. Google Cloud Datastore vs. Oracle vs. STSdb

System Properties Comparison Bangdb vs. Google Cloud Datastore vs. Oracle vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

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

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

Widely used RDBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

bangdb.com

cloud.google.com/datastore

www.oracle.com/database

github.com/STSSoft/STSdb4

Technical documentation

docs.bangdb.com

cloud.google.com/datastore/docs

docs.oracle.com/en/database

Developer

Sachin Sinha, BangDB

Google

Oracle

STS Soft SC

Initial release

2012

2008

1980

2011

Current release

BangDB 2.0, October 2021

23c, September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

BSD 3

commercial

restricted free version is available

Open Source

GPLv2, commercial license available

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

C, C++

C and C++

Server operating systems

Linux

hosted

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

Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

yes

Typing

predefined data types such as float or date

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

yes, details here

yes

primitive types and user defined types (classes)

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language (GQL)

yes

with proprietary extensions

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

.NET Client API

Supported programming languages

C
C#
C++
Java
Python

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

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

C#
Java

Server-side scripts

Stored procedures

using Google App Engine

PL/SQL

also stored procedures in Java possible

Triggers

yes, Notifications (with Streaming only)

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

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, run db with in-memory only mode

yes

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

User concepts

Access control

yes (enterprise version only)

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

fine grained access rights according to SQL-standard

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