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DBMS > Amazon DocumentDB vs. Apache Phoenix vs. Google Cloud Datastore vs. PostGIS vs. Spark SQL

System Properties Comparison Amazon DocumentDB vs. Apache Phoenix vs. Google Cloud Datastore vs. PostGIS vs. Spark SQL

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonPostGIS  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceA scale-out RDBMS with evolutionary schema built on Apache HBaseAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformSpatial extension of PostgreSQLSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument storeRelational DBMSDocument storeSpatial DBMSRelational 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
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score22.69
Rank#29  Overall
#1  Spatial DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­documentdbphoenix.apache.orgcloud.google.com/­datastorepostgis.netspark.apache.org/­sql
Technical documentationaws.amazon.com/­documentdb/­resourcesphoenix.apache.orgcloud.google.com/­datastore/­docspostgis.net/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationGoogleApache Software Foundation
Initial release20192014200820052014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20193.4.2, February 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialOpen Source infoGPL v2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCScala
Server operating systemshostedLinux
Unix
Windows
hostedLinux
OS X
Windows
Data schemeschema-freeyes infolate-bound, schema-on-read capabilitiesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyes, details hereyesyes
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.nononoyesno
Secondary indexesyesyesyesyesno
SQL infoSupport of SQLnoyesSQL-like query language (GQL)yesSQL-like DML and DDL statements
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsusing Google App Engineuser defined functionsno
TriggersnonoCallbacks using the Google Apps Engineyesno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingyes infobased on PostgreSQLyes, utilizing Spark Core
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
Source-replica replication
Multi-source replication using Paxosyes infobased on PostgreSQLnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)Hadoop integrationyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual 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 possiblenoyes infovia ReferenceProperties or Ancestor pathsyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
User concepts infoAccess controlAccess rights for users and rolesAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes infobased on PostgreSQLno

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Amazon DocumentDBApache PhoenixGoogle Cloud DatastorePostGISSpark SQL
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