DBMS > Bangdb vs. Lovefield vs. MySQL vs. Snowflake vs. Stardog

System Properties Comparison Bangdb vs. Lovefield vs. MySQL vs. Snowflake vs. Stardog

Editorial information provided by DB-Engines

Name

Description

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

Embeddable relational database for web apps written in pure JavaScript

Widely used open source RDBMS

Cloud-based data warehousing service for structured and semi-structured data

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

bangdb.com

google.github.io/lovefield

www.mysql.com

www.snowflake.com

www.stardog.com

Technical documentation

docs.bangdb.com

github.com/google/lovefield/blob/master/docs/spec_index.md

dev.mysql.com/doc

docs.snowflake.net/manuals/index.html

docs.stardog.com

Developer

Sachin Sinha, BangDB

Google

Oracle

since 2010, originally MySQL AB, then Sun

Snowflake Computing Inc.

Stardog-Union

Initial release

2012

2014

1995

2014

2010

Current release

BangDB 2.0, October 2021

2.1.12, February 2017

8.4.0, April 2024

7.3.0, May 2020

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C, C++

JavaScript

C and C++

Java

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Linux
macOS
Windows

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

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

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.

yes

Import/export of XML data possible

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

via JavaScript builder pattern

yes

with proprietary extensions

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

CLI Client
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C
C#
C++
Java
Python

JavaScript

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes, Notifications (with Streaming only)

Using read-only observers

yes

similar concept for controling cloud resources

yes

via event handlers

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, sharding with MySQL Cluster or MySQL Fabric

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Multi-source replication
Source-replica replication

yes

Multi-source replication in HA-Cluster

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

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationships in graphs

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes, by using IndexedDB or the cloud service Firebase Realtime Database

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

using MemoryDB

yes

User concepts

Access control

yes (enterprise version only)

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users and roles

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