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DBMS > Amazon DocumentDB vs. Google Cloud Datastore vs. Kyligence Enterprise vs. Spark SQL vs. Sphinx

System Properties Comparison Amazon DocumentDB vs. Google Cloud Datastore vs. Kyligence Enterprise vs. Spark SQL vs. Sphinx

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonKyligence Enterprise  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA distributed analytics engine for big data, built on top of Apache KylinSpark 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 storeRelational DBMSRelational DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#131  Overall
#24  Document stores
Score4.36
Rank#72  Overall
#12  Document stores
Score0.46
Rank#266  Overall
#124  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websiteaws.amazon.com/­documentdbcloud.google.com/­datastorekyligence.io/­kyligence-enterprisespark.apache.org/­sqlsphinxsearch.com
Technical documentationaws.amazon.com/­documentdb/­resourcescloud.google.com/­datastore/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docs
DeveloperGoogleKyligence, Inc.Apache Software FoundationSphinx Technologies Inc.
Initial release20192008201620142001
Current release3.5.0 ( 2.13), September 20233.5.1, February 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaScalaC++
Server operating systemshostedhostedLinuxLinux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyesno
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.nononono
Secondary indexesyesyesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLnoSQL-like query language (GQL)ANSI SQL for queries (using Apache Calcite)SQL-like DML and DDL statementsSQL-like query language (SphinxQL)
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnousing Google App Enginenono
TriggersnoCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyes, 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 replicasMulti-source replication using Paxosnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)yes infousing Google Cloud Dataflowyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possibleyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnono
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.nonono
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nono

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More resources
Amazon DocumentDBGoogle Cloud DatastoreKyligence EnterpriseSpark SQLSphinx
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