DBMS > Bangdb vs. mSQL vs. SAP HANA vs. Sphinx vs. Titan

System Properties Comparison Bangdb vs. mSQL vs. SAP HANA vs. Sphinx vs. Titan

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

In-memory, column based data store. Available as appliance or cloud service

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

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Search engine

Graph DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
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	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

bangdb.com

hughestech.com.au/products/msql

www.sap.com/products/hana.html

sphinxsearch.com

github.com/thinkaurelius/titan

Technical documentation

docs.bangdb.com

help.sap.com/hana

sphinxsearch.com/docs

github.com/thinkaurelius/titan/wiki

Developer

Sachin Sinha, BangDB

Hughes Technologies

SAP

Sphinx Technologies Inc.

Aurelius, owned by DataStax

Initial release

2012

1994

2010

2001

2012

Current release

BangDB 2.0, October 2021

4.4, October 2021

2.0 SPS07 (April 4, 2023), April 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

BSD 3

commercial

free licenses can be provided

commercial

Open Source

GPL version 2, commercial licence available

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C++

Java

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Appliance or cloud-service

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

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

full-text index on all search fields

yes

SQL

Support of SQL

SQL like support with command line tool

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

SQL-like query language (SphinxQL)

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

Proprietary protocol

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

C
C#
C++
Java
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Clojure
Java
Python

Server-side scripts

Stored procedures

SQLScript, R

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

none

yes

Sharding

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

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

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

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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)

yes

User authentification and security via Rexster Graph Server

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