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DBMS > Amazon DocumentDB vs. Bangdb vs. Spark SQL vs. Sphinx

System Properties Comparison Amazon DocumentDB vs. Bangdb vs. Spark SQL vs. Sphinx

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonBangdb  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceConverged and high performance database for device data, events, time series, document and graphSpark 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 modelDocument storeDocument store
Graph DBMS
Time Series DBMS
Relational DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score0.08
Rank#347  Overall
#47  Document stores
#34  Graph DBMS
#31  Time Series DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websiteaws.amazon.com/­documentdbbangdb.comspark.apache.org/­sqlsphinxsearch.com
Technical documentationaws.amazon.com/­documentdb/­resourcesdocs.bangdb.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docs
DeveloperSachin Sinha, BangDBApache Software FoundationSphinx Technologies Inc.
Initial release2019201220142001
Current releaseBangDB 2.0, October 20213.5.0 ( 2.13), September 20233.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infoBSD 3Open Source infoApache 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++ScalaC++
Server operating systemshostedLinuxLinux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes: string, long, double, int, geospatial, stream, eventsyesno
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 indexesyesyes infosecondary, composite, nested, reverse, geospatialnoyes infofull-text index on all search fields
SQL infoSupport of SQLnoSQL like support with command line toolSQL-like DML and DDL statementsSQL-like query language (SphinxQL)
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)Proprietary protocol
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C
C#
C++
Java
Python
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnononono
Triggersnoyes, Notifications (with Streaming only)nono
Partitioning methods infoMethods for storing different data on different nodesnoneSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasselectable replication factor, Knob for CAP (enterprise version only)nonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyTunable consistency, set CAP knob accordingly
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyes, optimistic concurrency controlyesyes
Durability infoSupport for making data persistentyesyes, implements WAL (Write ahead log) as wellyesyes 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.yes, run db with in-memory only modeno
User concepts infoAccess controlAccess rights for users and rolesyes (enterprise version only)nono

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More resources
Amazon DocumentDBBangdbSpark SQLSphinx
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