DBMS > Spark SQL vs. Sphinx vs. ToroDB

System Properties Comparison Spark SQL vs. Sphinx vs. ToroDB

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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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Relational DBMS

Search engine

Document store

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

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Score	6.72
Rank	#63	Overall
	#7	Search engines

Score	0.03
Rank	#337	Overall
	#46	Document stores

Website

spark.apache.org/sql

sphinxsearch.com

www.torodb.com

Technical documentation

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

sphinxsearch.com/docs

www.torodb.com/stampede/docs

Developer

Apache Software Foundation

Sphinx Technologies Inc.

8Kdata

Initial release

2014

2001

2016

Current release

3.2.0, October 2021

3.4.1, July 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++

Java

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

All OS with a Java 7 VM

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

Proprietary protocol

Supported programming languages

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Triggers

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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.

User concepts

Access control

Access rights for users and roles

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