DBMS > Spark SQL vs. Sphinx vs. STSdb vs. SWC-DB vs. VelocityDB

System Properties Comparison Spark SQL vs. Sphinx vs. STSdb vs. SWC-DB vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A high performance, scalable Wide Column DBMS

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

Primary database model

Graph DBMS
Object oriented DBMS

Secondary database models

Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

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

Website

spark.apache.org/sql

sphinxsearch.com

github.com/STSSoft/STSdb4

github.com/kashirin-alex/swc-db
www.swcdb.org

velocitydb.com

Technical documentation

spark.apache.org/docs/latest/sql-programming-guide.html

sphinxsearch.com/docs

velocitydb.com/UserGuide

Developer

Apache Software Foundation

Sphinx Technologies Inc.

STS Soft SC

Alex Kashirin

VelocityDB Inc

Initial release

2014

2001

2011

2020

2011

Current release

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

4.0.8, September 2015

0.5, April 2021

7.x

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, commercial license available

Open Source

GPL V3

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

Scala

C++

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Linux

Any that supports .NET

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

SQL-like query language

APIs and other access methods

JDBC
ODBC

Proprietary protocol

.NET Client API

Proprietary protocol
Thrift

.Net

Supported programming languages

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

C++

.Net

Server-side scripts

Stored procedures

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Sharding

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

User concepts

Access control

Based on Windows Authentication

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More resources

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