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

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

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonGreenplum  Xexclude from comparisonHazelcast  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 widely adopted in-memory data gridRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KV
Primary database modelDocument storeRelational DBMSKey-value storeTime Series DBMS
Secondary database modelsDocument store
Spatial DBMS
Document store infoJSON support with IMDG 3.12
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
Score5.46
Rank#61  Overall
#7  Key-value stores
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Websitecloud.google.com/­datastoregreenplum.orghazelcast.com
Technical documentationcloud.google.com/­datastore/­docsdocs.greenplum.orghazelcast.org/­imdg/­docswww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperGooglePivotal Software Inc.HazelcastOpen Source, formerly Basho Technologies
Initial release2008200520082015
Current release7.0.0, September 20235.3.6, November 20233.0.0, September 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoApache Version 2; commercial licenses availableOpen Source
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 languageJavaErlang
Server operating systemshostedLinuxAll OS with a Java VMLinux
OS X
Data schemeschema-freeyesschema-freeschema-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 4.2yes infothe object must implement a serialization strategyno
Secondary indexesyesyesyesrestricted
SQL infoSupport of SQLSQL-like query language (GQL)yesSQL-like query languageyes, limited
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
JCache
JPA
Memcached protocol
RESTful HTTP API
HTTP API
Native Erlang Interface
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
Java
Perl
Python
R
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
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 Engineyesyes infoEvent Listeners, Executor ServicesErlang
TriggersCallbacks using the Google Apps Engineyesyes infoEventsyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSource-replica replicationyes infoReplicated Mapselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyesyesyes
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 Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesnono 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 TransactionsACIDone or two-phase-commit; repeatable reads; read commitedno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
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, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardRole-based access controlno

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More resources
Google Cloud DatastoreGreenplumHazelcastRiak TS
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