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DBMS > Google Cloud Datastore vs. InterSystems Caché vs. TimescaleDB vs. Titan

System Properties Comparison Google Cloud Datastore vs. InterSystems Caché vs. TimescaleDB vs. Titan

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonInterSystems Caché  Xexclude from comparisonTimescaleDB  Xexclude from comparisonTitan  Xexclude from comparison
Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA multi-model DBMS and application serverA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQLTitan is a Graph DBMS optimized for distributed clusters.
Primary database modelDocument storeKey-value store
Object oriented DBMS
Relational DBMS
Time Series DBMSGraph DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitecloud.google.com/­datastorewww.intersystems.com/­products/­cachewww.timescale.comgithub.com/­thinkaurelius/­titan
Technical documentationcloud.google.com/­datastore/­docsdocs.intersystems.comdocs.timescale.comgithub.com/­thinkaurelius/­titan/­wiki
DeveloperGoogleInterSystemsTimescaleAurelius, owned by DataStax
Initial release2008199720172012
Current release2018.1.4, May 20202.13.0, November 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoApache license, version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCJava
Server operating systemshostedAIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Linux
OS X
Unix
Windows
Data schemeschema-freedepending on used data modelyesyes
Typing infopredefined data types such as float or dateyes, details hereyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data typesyes
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.noyesyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query language (GQL)yesyes infofull PostgreSQL SQL syntaxno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
.NET Client API
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Clojure
Java
Python
Server-side scripts infoStored proceduresusing Google App Engineyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shellyes
TriggersCallbacks using the Google Apps Engineyesyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneyes, across time and space (hash partitioning) attributesyes infovia pluggable storage backends
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSource-replica replicationSource-replica replication with hot standby and reads on replicas infoyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownonoyes infovia Faunus, a graph analytics engine
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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 ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesyesyes infoRelationships in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and rolesfine grained access rights according to SQL-standardUser authentification and security via Rexster Graph Server

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More resources
Google Cloud DatastoreInterSystems CachéTimescaleDBTitan
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