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DBMS > Dgraph vs. EsgynDB vs. Google Cloud Bigtable vs. Google Cloud Datastore vs. HugeGraph

System Properties Comparison Dgraph vs. EsgynDB vs. Google Cloud Bigtable vs. Google Cloud Datastore vs. HugeGraph

Editorial information provided by DB-Engines
NameDgraph  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHugeGraph  Xexclude from comparison
DescriptionDistributed and scalable native Graph DBMSEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA fast-speed and highly-scalable Graph DBMS
Primary database modelGraph DBMSRelational DBMSWide column storeDocument storeGraph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.81
Rank#127  Overall
#10  Graph DBMS
Score0.27
Rank#274  Overall
#127  Relational DBMS
Score3.98
Rank#85  Overall
#6  Wide column stores
Score4.38
Rank#80  Overall
#12  Document stores
Score0.19
Rank#290  Overall
#26  Graph DBMS
Websitedgraph.iowww.esgyn.cncloud.google.com/­bigtablecloud.google.com/­datastoregithub.com/­hugegraph
Technical documentationdgraph.io/­docscloud.google.com/­bigtable/­docscloud.google.com/­datastore/­docshugegraph.github.io/­hugegraph-doc
DeveloperDgraph Labs, Inc.EsgynGoogleGoogleBaidu
Initial release20162015201520082018
Current release0.9
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC++, JavaJava
Server operating systemsLinux
OS X
Windows
LinuxhostedhostedLinux
macOS
Unix
Data schemeschema-freeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes, 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.nonononono
Secondary indexesyesyesnoyesyes infoalso supports composite index and range index
SQL infoSupport of SQLnoyesnoSQL-like query language (GQL)no
APIs and other access methodsGraphQL query language
gRPC (using protocol buffers) API
HTTP API
ADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Groovy
Java
Python
Server-side scripts infoStored proceduresnoJava Stored Proceduresnousing Google App Engineasynchronous Gremlin script jobs
TriggersnononoCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesyesShardingShardingShardingyes infodepending on used storage backend, e.g. Cassandra and HBase
Replication methods infoMethods for redundantly storing data on multiple nodesSynchronous replication via RaftMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication using Paxosyes infodepending on used storage backend, e.g. Cassandra and HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesyes infousing Google Cloud Dataflowvia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)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.Eventual Consistency
Foreign keys infoReferential integritynoyesnoyes infovia ReferenceProperties or Ancestor pathsyes infoedges in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-row operationsACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.nononoyes
User concepts infoAccess controlno infoPlanned for future releasesfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users, roles and permissions
More information provided by the system vendor
DgraphEsgynDBGoogle Cloud BigtableGoogle Cloud DatastoreHugeGraph
Competitive advantagesHorizontally scalable transactional graph database with fast arbitrary-depth joins...
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More resources
DgraphEsgynDBGoogle Cloud BigtableGoogle Cloud DatastoreHugeGraph
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