DBMS > Bangdb vs. ObjectBox vs. OrigoDB vs. Vertica

System Properties Comparison Bangdb vs. ObjectBox vs. OrigoDB vs. Vertica

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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

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

A fully ACID in-memory object graph database

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Graph DBMS
Time Series DBMS

Object oriented DBMS
Vector DBMS

Document store
Object oriented DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS

Time Series DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.07
Rank	#346	Overall
	#47	Document stores
	#35	Graph DBMS
	#32	Time Series DBMS

Score	1.08
Rank	#179	Overall
	#6	Object oriented DBMS
	#9	Vector DBMS

Score	0.00
Rank	#385	Overall
	#54	Document stores
	#21	Object oriented DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

bangdb.com

github.com/objectbox
objectbox.io

origodb.com

www.vertica.com

Technical documentation

docs.bangdb.com

docs.objectbox.io

origodb.com/docs

vertica.com/documentation

Developer

Sachin Sinha, BangDB

ObjectBox Limited

Robert Friberg et al

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2017

2009

under the name LiveDB

2005

Current release

BangDB 2.0, October 2021

4.0 (May 2024)

12.0.3, January 2023

License

Commercial or Open Source

Open Source

BSD 3

Bindings are released under Apache 2.0

Apache License 2.0

Open Source

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

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

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Linux
Windows

Linux

Data scheme

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

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

yes, plus "flex" map-like types

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL like support with command line tool

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Proprietary native API

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C
C#
C++
Java
Python

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

.Net

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, Notifications (with Streaming only)

yes

Domain Events

yes, called Custom Alerts

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

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

Data sync between devices allowing occasional connected databases to work completely offline

Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

yes

Transaction concepts

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

yes

Role based authorization

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

The on-device Vector Database for Android and Java
29 May 2024

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