DB-EnginesExtremeDB for everyone with an RTOSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Heroic vs. mSQL vs. Spark SQL vs. Sphinx

System Properties Comparison Heroic vs. mSQL vs. Spark SQL vs. Sphinx

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameHeroic  Xexclude from comparisonmSQL infoMini SQL  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchmSQL (Mini SQL) is a simple and lightweight RDBMSSpark SQL is a component on top of 'Spark Core' for structured data processingOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMSRelational DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.57
Rank#250  Overall
#21  Time Series DBMS
Score1.67
Rank#151  Overall
#70  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score6.03
Rank#60  Overall
#6  Search engines
Websitegithub.com/­spotify/­heroichughestech.com.au/­products/­msqlspark.apache.org/­sqlsphinxsearch.com
Technical documentationspotify.github.io/­heroicspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docs
DeveloperSpotifyHughes TechnologiesApache Software FoundationSphinx Technologies Inc.
Initial release2014199420142001
Current release4.4, October 20213.5.0 ( 2.13), September 20233.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial infofree licenses can be providedOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaCScalaC++
Server operating systemsAIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
Linux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesno
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonono
Secondary indexesyes infovia Elasticsearchyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLnoA subset of ANSI SQL is implemented infono subqueries, aggregate functions, views, foreign keys, triggersSQL-like DML and DDL statementsSQL-like query language (SphinxQL)
APIs and other access methodsHQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
ODBC
JDBC
ODBC
Proprietary protocol
Supported programming languagesC
C++
Delphi
Java
Perl
PHP
Tcl
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnononono
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesyesnonenonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
none
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
Concurrency infoSupport for concurrent manipulation of datayesnoyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlnonono

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
HeroicmSQL infoMini SQLSpark SQLSphinx
DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

Make Your MySQL Server More Secure With These 7 Steps - MUO
1 December 2022, MakeUseOf

Writing a Web Service in Perl
9 July 2003, PCQuest

Higher Education PS rules out ghost students before PAC - Zambia
29 November 2018, diggers.news

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

1.5 Years of Spark Knowledge in 8 Tips | by Michael Berk
23 December 2023, Towards Data Science

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

Switching From Sphinx to MkDocs Documentation — What Did I Gain and Lose
2 February 2024, Towards Data Science

Manticore is a Faster Alternative to Elasticsearch in C++
25 July 2022, hackernoon.com

Perplexity AI: From Its Use To Operation, Everything You Need To Know About Googles Newest Challenger
11 January 2024, Free Press Journal

The Pirate Bay was recently down for over a week due to a DDoS attack
29 October 2019, The Hacker News

How to Build 600+ Links in One Month
4 September 2020, Search Engine Journal

provided by Google News



Share this page

Featured Products

SingleStore logo

The database to transact, analyze and contextualize your data in real time.
Try it today.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Ontotext logo

GraphDB allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs. Get it free.

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Present your product here