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DBMS > Google Cloud Datastore vs. Greenplum vs. JanusGraph vs. Riak TS

System Properties Comparison Google Cloud Datastore vs. Greenplum vs. JanusGraph vs. Riak TS

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonGreenplum  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparisonRiak TS  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformAnalytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database infoA logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.A Graph DBMS optimized for distributed clusters infoIt was forked from the latest code base of Titan in January 2017Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV
Primary database modelDocument storeRelational DBMSGraph DBMSTime Series DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score8.08
Rank#48  Overall
#31  Relational DBMS
Score2.02
Rank#125  Overall
#12  Graph DBMS
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Websitecloud.google.com/­datastoregreenplum.orgjanusgraph.org
Technical documentationcloud.google.com/­datastore/­docsdocs.greenplum.orgdocs.janusgraph.orgwww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperGooglePivotal Software Inc.Linux Foundation; originally developed as Titan by AureliusOpen Source, formerly Basho Technologies
Initial release2008200520172015
Current release7.0.0, September 20230.6.3, February 20233.0.0, September 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoApache 2.0Open Source
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaErlang
Server operating systemshostedLinuxLinux
OS X
Unix
Windows
Linux
OS X
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyes, 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.noyes infosince Version 4.2nono
Secondary indexesyesyesyesrestricted
SQL infoSupport of SQLSQL-like query language (GQL)yesnoyes, limited
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
HTTP API
Native Erlang Interface
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
Java
Perl
Python
R
Clojure
Java
Python
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresusing Google App EngineyesyesErlang
TriggersCallbacks using the Google Apps Engineyesyesyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)Sharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSource-replica replicationyesselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyesyes infovia Faunus, a graph analytics engineyes
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 ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesyes infoRelationships in graphsno infolinks between datasets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcastyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardUser authentification and security via Rexster Graph Serverno

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More resources
Google Cloud DatastoreGreenplumJanusGraph infosuccessor of TitanRiak TS
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