DBMS > Bangdb vs. Oracle NoSQL vs. SQLite vs. StarRocks

System Properties Comparison Bangdb vs. Oracle NoSQL vs. SQLite vs. StarRocks

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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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Widely used embeddable, in-process RDBMS

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial 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	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	0.95
Rank	#193	Overall
	#89	Relational DBMS

Website

bangdb.com

www.oracle.com/database/nosql/technologies/nosql

www.sqlite.org

www.starrocks.io

Technical documentation

docs.bangdb.com

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.sqlite.org/docs.html

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Sachin Sinha, BangDB

Oracle

Dwayne Richard Hipp

The Linux Foundation

since Feb 2023

Initial release

2012

2011

2000

2020

Current release

BangDB 2.0, October 2021

24.1, May 2024

3.46.1 (13 August 2024), August 2024

3.3, June 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Public Domain

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java

C++, Java

Server operating systems

Linux

Linux
Solaris SPARC/x86

server-less

Linux

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

dynamic column types

yes

Typing

predefined data types such as float or date

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

optional

yes

not rigid because of 'dynamic typing' concept.

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 DML and DDL statements

yes

SQL-92 is not fully supported

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
MySQL protocol

Supported programming languages

C
C#
C++
Java
Python

C
C#
Go
Java
JavaScript (Node.js)
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Java

Server-side scripts

Stored procedures

user defined functions

Triggers

yes, Notifications (with Streaming only)

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

none

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

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

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

via file-system locks

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

off heap cache

yes

User concepts

Access control

yes (enterprise version only)

Access rights for users and roles

Role based access control and fine grained access rights

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