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DBMS > Apache Spark (SQL) vs. MongoDB vs. Sphinx

System Properties Comparison Apache Spark (SQL) vs. MongoDB vs. Sphinx

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonMongoDB  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingOne of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructureOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSDocument storeSearch engine
Secondary database modelsSpatial DBMS
Search engine infointegrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS infoTime Series Collections introduced in Release 5.0
Vector DBMS infocurrently available in the MongoDB Atlas cloud service only
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.45
Rank#31  Overall
#19  Relational DBMS
Score396.63
Rank#5  Overall
#1  Document stores
Score5.88
Rank#56  Overall
#5  Search engines
Websitespark.apache.org/­sqlwww.mongodb.comsphinxsearch.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.mongodb.com/­docs/­manualsphinxsearch.com/­docs
DeveloperApache Software FoundationMongoDB, IncSphinx Technologies Inc.
Initial release201420092001
Current release3.5.0 ( 2.13), September 20237.0.5, January 20243.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoMongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenono infoMongoDB available as DBaaS (MongoDB Atlas)no
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++C++
Server operating systemsLinux
OS X
Windows
Linux
OS X
Solaris
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesschema-free infoAlthough schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.yes
Typing infopredefined data types such as float or dateyesyes infostring, integer, double, decimal, boolean, date, object_id, geospatialno
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.no
Secondary indexesnoyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like DML and DDL statementsRead-only SQL queries via the MongoDB Atlas SQL InterfaceSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
GraphQL
HTTP REST
Prisma
proprietary protocol using JSON
Proprietary protocol
Supported programming languagesJava
Python
R
Scala
Actionscript infounofficial driver
C
C#
C++
Clojure infounofficial driver
ColdFusion infounofficial driver
D infounofficial driver
Dart infounofficial driver
Delphi infounofficial driver
Erlang
Go
Groovy infounofficial driver
Haskell
Java
JavaScript
Kotlin
Lisp infounofficial driver
Lua infounofficial driver
MatLab infounofficial driver
Perl
PHP
PowerShell infounofficial driver
Prolog infounofficial driver
Python
R infounofficial driver
Ruby
Rust
Scala
Smalltalk infounofficial driver
Swift
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnoJavaScriptno
Triggersnoyes infoin MongoDB Atlas onlyno
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreSharding infoPartitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.Sharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infocan be individually decided for each read operation
Immediate Consistency infodefault behaviour
Foreign keys infoReferential integritynono infotypically not used, however similar functionality with DBRef possibleno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoMulti-document ACID Transactions with snapshot isolationno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infooptional, enabled by defaultyes 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.noyes infoIn-memory storage engine introduced with MongoDB version 3.2
User concepts infoAccess controlnoAccess rights for users and rolesno
More information provided by the system vendor
Apache Spark (SQL)MongoDBSphinx
Specific characteristicsMongoDB provides an integrated suite of cloud database and data services to accelerate...
» more
Competitive advantagesBuilt around the flexible document data model and unified API, MongoDB is a developer...
» more
Typical application scenariosAI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
» more
Key customersADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
» more
Market metricsHundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
» more
Licensing and pricing modelsMongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
» more

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Apache Spark (SQL)MongoDBSphinx
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