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DBMS > Amazon Neptune vs. Google Cloud Datastore vs. HugeGraph vs. KeyDB

System Properties Comparison Amazon Neptune vs. Google Cloud Datastore vs. HugeGraph vs. KeyDB

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHugeGraph  Xexclude from comparisonKeyDB  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA fast-speed and highly-scalable Graph DBMSAn ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols
Primary database modelGraph DBMS
RDF store
Document storeGraph DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score4.36
Rank#72  Overall
#12  Document stores
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score0.70
Rank#229  Overall
#32  Key-value stores
Websiteaws.amazon.com/­neptunecloud.google.com/­datastoregithub.com/­hugegraph
hugegraph.apache.org
github.com/­Snapchat/­KeyDB
keydb.dev
Technical documentationaws.amazon.com/­neptune/­developer-resourcescloud.google.com/­datastore/­docshugegraph.apache.org/­docsdocs.keydb.dev
DeveloperAmazonGoogleBaiduEQ Alpha Technology Ltd.
Initial release2017200820182019
Current release0.9
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2.0Open Source infoBSD-3
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++
Server operating systemshostedhostedLinux
macOS
Unix
Linux
Data schemeschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details hereyespartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes
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.nononono
Secondary indexesnoyesyes infoalso supports composite index and range indexyes infoby using the Redis Search module
SQL infoSupport of SQLnoSQL-like query language (GQL)nono
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
Proprietary protocol infoRESP - REdis Serialization Protoco
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Groovy
Java
Python
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
Server-side scripts infoStored proceduresnousing Google App Engineasynchronous Gremlin script jobsLua
TriggersnoCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.Multi-source replication using Paxosyes infodepending on used storage backend, e.g. Cassandra and HBaseMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowvia hugegraph-sparkno
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 ConsistencyEventual Consistency
Strong eventual consistency with CRDTs
Foreign keys infoReferential integrityyes infoRelationships in graphsyes 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 TransactionsACIDOptimistic locking, atomic execution of commands blocks and scripts
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logs
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users, roles and permissionssimple password-based access control and ACL

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More resources
Amazon NeptuneGoogle Cloud DatastoreHugeGraphKeyDB
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