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DBMS > Amazon DocumentDB vs. Citus vs. Google Cloud Bigtable vs. SiriDB vs. Sphinx

System Properties Comparison Amazon DocumentDB vs. Citus vs. Google Cloud Bigtable vs. SiriDB vs. Sphinx

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonCitus  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonSiriDB  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Open Source Time Series DBMSOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument storeRelational DBMSKey-value store
Wide column store
Time Series DBMSSearch engine
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websiteaws.amazon.com/­documentdbwww.citusdata.comcloud.google.com/­bigtablesiridb.comsphinxsearch.com
Technical documentationaws.amazon.com/­documentdb/­resourcesdocs.citusdata.comcloud.google.com/­bigtable/­docsdocs.siridb.comsphinxsearch.com/­docs
DeveloperGoogleCesbitSphinx Technologies Inc.
Initial release20192010201520172001
Current release8.1, December 20183.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infoAGPL, commercial license also availablecommercialOpen Source infoMIT LicenseOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCCC++
Server operating systemshostedLinuxhostedLinuxFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesnoyes infoNumeric datano
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.noyes infospecific XML type available, but no XML query functionalitynono
Secondary indexesyesyesnoyesyes infofull-text index on all search fields
SQL infoSupport of SQLnoyes infostandard, with numerous extensionsnonoSQL-like query language (SphinxQL)
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HTTP APIProprietary protocol
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C#
C++
Go
Java
JavaScript (Node.js)
Python
C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.nonono
Triggersnoyesnonono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingShardingSharding 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 replicasSource-replica replication infoother methods possible by using 3rd party extensionsInternal replication in Colossus, and regional replication between two clusters in different zonesyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)noyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possibleyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDAtomic single-row operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes 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.nonoyes
User concepts infoAccess controlAccess rights for users and rolesfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)simple rights management via user accountsno

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More resources
Amazon DocumentDBCitusGoogle Cloud BigtableSiriDBSphinx
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