DBMS > Bangdb vs. Google BigQuery vs. ObjectBox vs. OrigoDB vs. Rockset

System Properties Comparison Bangdb vs. Google BigQuery vs. ObjectBox vs. OrigoDB vs. Rockset

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Extremely fast embedded database for small devices, IoT and Mobile

A fully ACID in-memory object graph database

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Object oriented DBMS

Document store
Object oriented DBMS

Document store

Secondary database models

Spatial DBMS

Time Series DBMS

Relational 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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

bangdb.com

cloud.google.com/bigquery

objectbox.io

origodb.com

rockset.com

Technical documentation

docs.bangdb.com

cloud.google.com/bigquery/docs

docs.objectbox.io

origodb.com/docs

docs.rockset.com

Developer

Sachin Sinha, BangDB

Google

ObjectBox Limited

Robert Friberg et al

Rockset

Initial release

2012

2010

2017

2009

under the name LiveDB

2019

Current release

BangDB 2.0, October 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Apache License 2.0

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

C, C++

C and C++

C++

Server operating systems

Linux

hosted

Android
iOS
Linux
macOS
Windows

Linux
Windows

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

User defined using .NET types and collections

dynamic 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.

can be achieved using .NET

ingestion from XML files supported

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

all fields are automatically indexed

SQL

Support of SQL

SQL like support with command line tool

yes

Read-only SQL queries, including JOINs

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

Proprietary native API

.NET Client API
HTTP API
LINQ

HTTP REST

Supported programming languages

C
C#
C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes, Notifications (with Streaming only)

yes

Domain Events

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

none

horizontal partitioning

client side managed; servers are not synchronized

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

online/offline synchronization between client and server

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

depending on model

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

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

Write ahead log

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

User concepts

Access control

yes (enterprise version only)

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

Role based authorization

Access rights for users and organizations can be defined via Rockset console

More information provided by the system vendor

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