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

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

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceSpark 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 databasesA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelDocument storeRelational DBMSSearch engineTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websiteaws.amazon.com/­documentdbspark.apache.org/­sqlsphinxsearch.comwww.timescale.com
Technical documentationaws.amazon.com/­documentdb/­resourcesspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docsdocs.timescale.com
DeveloperApache Software FoundationSphinx Technologies Inc.Timescale
Initial release2019201420012017
Current release3.5.0 ( 2.13), September 20233.5.1, February 20232.13.0, November 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++C
Server operating systemshostedLinux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesnonumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nonoyes
Secondary indexesyesnoyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like query language (SphinxQL)yes infofull PostgreSQL SQL syntax
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
ODBC
Proprietary protocolADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnononouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesnoneyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supportedyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasnonenoneSource-replica replication with hot standby and reads on replicas info
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 ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users and rolesnonofine grained access rights according to SQL-standard

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More resources
Amazon DocumentDBSpark SQLSphinxTimescaleDB
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