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DBMS > Amazon DocumentDB vs. Google Cloud Firestore vs. Kinetica vs. Sphinx

System Properties Comparison Amazon DocumentDB vs. Google Cloud Firestore vs. Kinetica vs. Sphinx

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonKinetica  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.Fully vectorized database across both GPUs and CPUsOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument storeDocument storeRelational DBMSSearch engine
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score7.85
Rank#51  Overall
#8  Document stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websiteaws.amazon.com/­documentdbfirebase.google.com/­products/­firestorewww.kinetica.comsphinxsearch.com
Technical documentationaws.amazon.com/­documentdb/­resourcesfirebase.google.com/­docs/­firestoredocs.kinetica.comsphinxsearch.com/­docs
DeveloperGoogleKineticaSphinx Technologies Inc.
Initial release2019201720122001
Current release7.1, August 20213.5.1, February 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C++
Server operating systemshostedhostedLinuxFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyesyes
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 indexesyesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsSQL-like query language (SphinxQL)
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Proprietary protocol
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
C++
Java
JavaScript (Node.js)
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnoyes, Firebase Rules & Cloud Functionsuser defined functionsno
Triggersnoyes, with Cloud Functionsyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingSharding 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 replicasMulti-source replicationSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)Using Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsyesnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.Access rights for users and roles on table levelno

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More resources
Amazon DocumentDBGoogle Cloud FirestoreKineticaSphinx
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