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DBMS > Amazon DocumentDB vs. CockroachDB vs. FatDB vs. Google Cloud Datastore vs. H2GIS

System Properties Comparison Amazon DocumentDB vs. CockroachDB vs. FatDB vs. Google Cloud Datastore vs. H2GIS

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonCockroachDB  Xexclude from comparisonFatDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonH2GIS  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceCockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.A .NET NoSQL DBMS that can integrate with and extend SQL Server.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformSpatial extension of H2
Primary database modelDocument storeRelational DBMSDocument store
Key-value store
Document storeSpatial 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
Score6.15
Rank#55  Overall
#33  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score0.00
Rank#383  Overall
#7  Spatial DBMS
Websiteaws.amazon.com/­documentdbwww.cockroachlabs.comcloud.google.com/­datastorewww.h2gis.org
Technical documentationaws.amazon.com/­documentdb/­resourceswww.cockroachlabs.com/­docscloud.google.com/­datastore/­docswww.h2gis.org/­docs/­home
DeveloperCockroach LabsFatCloudGoogleCNRS
Initial release20192015201220082013
Current release24.1.0, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0, commercial license availablecommercialcommercialOpen Source infoLGPL 3.0
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC#Java
Server operating systemshostedLinux
macOS
Windows
Windowshosted
Data schemeschema-freedynamic schemaschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyes, details hereyes
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 indexesyesyesyesyesyes
SQL infoSupport of SQLnoyes, wire compatible with PostgreSQLno infoVia inetgration in SQL ServerSQL-like query language (GQL)yes
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
C#.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Server-side scripts infoStored proceduresnonoyes infovia applicationsusing Google App Engineyes infobased on H2
Triggersnonoyes infovia applicationsCallbacks using the Google Apps Engineyes
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning (by key range) infoall tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFTShardingShardingnone
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 RAFTselectable replication factorMulti-source replication using Paxosyes infobased on H2
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)noyesyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate 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 possibleyesnoyes infovia ReferenceProperties or Ancestor pathsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDnoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
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.nonoyes
User concepts infoAccess controlAccess rights for users and rolesRole-based access controlno infoCan implement custom security layer via applicationsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes infobased on H2

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More resources
Amazon DocumentDBCockroachDBFatDBGoogle Cloud DatastoreH2GIS
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