DBMS > Bangdb vs. HEAVY.AI vs. RavenDB vs. SiriDB

System Properties Comparison Bangdb vs. HEAVY.AI vs. RavenDB vs. SiriDB

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

Open Source Time Series DBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Document store

Time Series DBMS

Secondary database models

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series 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	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Website

bangdb.com

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

ravendb.net

siridb.com

Technical documentation

Developer

Sachin Sinha, BangDB

HEAVY.AI, Inc.

Hibernating Rhinos

Cesbit

Initial release

2012

2016

2010

2017

Current release

BangDB 2.0, October 2021

5.10, January 2022

5.4, July 2022

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2; enterprise edition available

Open Source

AGPL version 3, commercial license available

Open Source

MIT License

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++

C++ and CUDA

Server operating systems

Linux

Linux
macOS
Raspberry Pi
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

yes

Numeric data

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 (RQL)

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

HTTP API

Supported programming languages

C
C#
C++
Java
Python

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

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

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

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

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)

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

simple rights management via user accounts

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