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DBMS > Citus vs. Google Cloud Datastore vs. HugeGraph vs. Teradata Aster

System Properties Comparison Citus vs. Google Cloud Datastore vs. HugeGraph vs. Teradata Aster

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Editorial information provided by DB-Engines
NameCitus  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHugeGraph  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA fast-speed and highly-scalable Graph DBMSPlatform for big data analytics on multistructured data sources and types
Primary database modelRelational DBMSDocument storeGraph DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score0.13
Rank#336  Overall
#32  Graph DBMS
Websitewww.citusdata.comcloud.google.com/­datastoregithub.com/­hugegraph
hugegraph.apache.org
Technical documentationdocs.citusdata.comcloud.google.com/­datastore/­docshugegraph.apache.org/­docs
DeveloperGoogleBaiduTeradata
Initial release2010200820182005
Current release8.1, December 20180.9
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availablecommercialOpen Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCJava
Server operating systemsLinuxhostedLinux
macOS
Unix
Linux
Data schemeyesschema-freeyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesyes, 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.yes infospecific XML type available, but no XML query functionalitynonoyes infoin Aster File Store
Secondary indexesyesyesyes infoalso supports composite index and range indexyes
SQL infoSupport of SQLyes infostandard, with numerous extensionsSQL-like query language (GQL)noyes
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Groovy
Java
Python
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.using Google App Engineasynchronous Gremlin script jobsR packages
TriggersyesCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsMulti-source replication using Paxosyes infodepending on used storage backend, e.g. Cassandra and HBaseyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowvia hugegraph-sparkyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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.Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesyes infovia ReferenceProperties or Ancestor pathsyes infoedges in graphno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID
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.nonoyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users, roles and permissionsfine grained access rights according to SQL-standard

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More resources
CitusGoogle Cloud DatastoreHugeGraphTeradata Aster
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