DBMS > Bangdb vs. Brytlyt vs. Postgres-XL vs. Sphinx vs. VelocityDB

System Properties Comparison Bangdb vs. Brytlyt vs. Postgres-XL vs. Sphinx vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open source search engine for searching in data from different sources, e.g. relational databases

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Search engine

Graph DBMS
Object oriented DBMS

Secondary database models

Spatial DBMS

Document store
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	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

Technical documentation

docs.bangdb.com

docs.brytlyt.io

www.postgres-xl.org/documentation

sphinxsearch.com/docs

velocitydb.com/UserGuide

Developer

Sachin Sinha, BangDB

Brytlyt

Sphinx Technologies Inc.

VelocityDB Inc

Initial release

2012

2016

2014

since 2012, originally named StormDB

2001

2011

Current release

BangDB 2.0, October 2021

5.0, August 2023

10 R1, October 2018

3.5.1, February 2023

7.x

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Mozilla public license

Open Source

GPL version 2, commercial licence available

commercial

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, C++ and CUDA

C++

Server operating systems

Linux

Linux
OS X
Windows

Linux
macOS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Any that supports .NET

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.

yes

specific XML-type available, but no XML query functionality.

yes

XML type, but no XML query functionality

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL like support with command line tool

yes

distributed, parallel query execution

SQL-like query language (SphinxQL)

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Proprietary protocol

.Net

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

.Net

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions

Triggers

yes, Notifications (with Streaming only)

yes

Callbacks are triggered when data changes

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

horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica 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

MVCC

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

The original contents of fields are not stored in the Sphinx index.

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

Based on Windows Authentication

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More resources

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