DBMS > Bangdb vs. Databend vs. HEAVY.AI vs. Lovefield

System Properties Comparison Bangdb vs. Databend vs. HEAVY.AI vs. Lovefield

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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

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial 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	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

bangdb.com

github.com/datafuselabs/databend
www.databend.com

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

google.github.io/lovefield

Technical documentation

docs.bangdb.com

docs.databend.com

docs.heavy.ai

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

Developer

Sachin Sinha, BangDB

Databend Labs

HEAVY.AI, Inc.

Google

Initial release

2012

2021

2016

2014

Current release

BangDB 2.0, October 2021

1.0.59, April 2023

5.10, January 2022

2.1.12, February 2017

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

Rust

C++ and CUDA

JavaScript

Server operating systems

Linux

hosted
Linux
macOS

Linux

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

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

yes

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.

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

APIs and other access methods

Proprietary protocol
RESTful HTTP API

CLI Client
JDBC
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

Supported programming languages

C
C#
C++
Java
Python

Go
Java
JavaScript (Node.js)
Python
Rust

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

Server-side scripts

Stored procedures

Triggers

yes, Notifications (with Streaming only)

Using read-only observers

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

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Multi-source replication

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

yes

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

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

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

User concepts

Access control

yes (enterprise version only)

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

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