DBMS > Spark SQL vs. Sphinx vs. TimesTen vs. Tkrzw

System Properties Comparison Spark SQL vs. Sphinx vs. TimesTen vs. Tkrzw

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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

In-Memory RDBMS compatible to Oracle

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

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

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

spark.apache.org/sql

sphinxsearch.com

www.oracle.com/database/technologies/related/timesten.html

dbmx.net/tkrzw

Technical documentation

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

sphinxsearch.com/docs

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation

Sphinx Technologies Inc.

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Mikio Hirabayashi

Initial release

2014

2001

1998

2020

Current release

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

11 Release 2 (11.2.2.8.0)

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

commercial

Open Source

Apache Version 2.0

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

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

Linux
macOS

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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)

yes

APIs and other access methods

JDBC
ODBC

Proprietary protocol

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C
C++
Java
PL/SQL

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

PL/SQL

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

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

by means of logfiles and checkpoints

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
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