DBMS > Bangdb vs. HEAVY.AI vs. Lovefield vs. Microsoft Azure AI Search vs. SQLite

System Properties Comparison Bangdb vs. HEAVY.AI vs. Lovefield vs. Microsoft Azure AI Search vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Embeddable relational database for web apps written in pure JavaScript

Search-as-a-service for web and mobile app development

Widely used embeddable, in-process RDBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

Vector DBMS

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	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

bangdb.com

github.com/heavyai/heavydb
www.heavy.ai

google.github.io/lovefield

azure.microsoft.com/en-us/services/search

www.sqlite.org

Technical documentation

docs.bangdb.com

docs.heavy.ai

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

learn.microsoft.com/en-us/azure/search

www.sqlite.org/docs.html

Developer

Sachin Sinha, BangDB

HEAVY.AI, Inc.

Google

Microsoft

Dwayne Richard Hipp

Initial release

2012

2016

2014

2015

2000

Current release

BangDB 2.0, October 2021

5.10, January 2022

2.1.12, February 2017

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C++ and CUDA

JavaScript

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

hosted

server-less

Data scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

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

yes

not rigid because of 'dynamic typing' concept.

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

yes

SQL-like query language

via JavaScript builder pattern

yes

SQL-92 is not fully supported

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
C++
Java
Python

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

C#
Java
JavaScript
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

Server-side scripts

Stored procedures

Triggers

yes, Notifications (with Streaming only)

Using read-only observers

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

Round robin

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

none

yes

Implicit feature of the cloud service

none

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

Foreign keys

Referential integrity

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

via file-system locks

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

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)

fine grained access rights according to SQL-standard

yes

using Azure authentication

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