DBMS > Amazon Neptune vs. Bangdb vs. Google Cloud Datastore vs. TimesTen

System Properties Comparison Amazon Neptune vs. Bangdb vs. Google Cloud Datastore vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

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

In-Memory RDBMS compatible to Oracle

Primary database model

Graph DBMS
RDF store

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

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	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

aws.amazon.com/neptune

bangdb.com

cloud.google.com/datastore

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

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.bangdb.com

cloud.google.com/datastore/docs

docs.oracle.com/database/timesten-18.1

Developer

Amazon

Sachin Sinha, BangDB

Google

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2017

2012

2008

1998

Current release

BangDB 2.0, October 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

BSD 3

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

C, C++

Server operating systems

hosted

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

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

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.

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

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

Proprietary protocol
RESTful HTTP API

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C#
C++
Java
Python

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

using Google App Engine

PL/SQL

Triggers

yes, Notifications (with Streaming only)

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

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

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

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

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 or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

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

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes, implements WAL (Write ahead log) as well

yes

by means of logfiles and checkpoints

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

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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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