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DBMS > Google Cloud Datastore vs. HugeGraph vs. Microsoft Azure AI Search vs. Sphinx vs. SurrealDB

System Properties Comparison Google Cloud Datastore vs. HugeGraph vs. Microsoft Azure AI Search vs. Sphinx vs. SurrealDB

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHugeGraph  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonSphinx  Xexclude from comparisonSurrealDB  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA fast-speed and highly-scalable Graph DBMSSearch-as-a-service for web and mobile app developmentOpen source search engine for searching in data from different sources, e.g. relational databasesA fully ACID transactional, developer-friendly, multi-model DBMS
Primary database modelDocument storeGraph DBMSSearch engineSearch engineDocument store
Graph DBMS
Secondary database modelsVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Score5.98
Rank#56  Overall
#5  Search engines
Score0.86
Rank#203  Overall
#34  Document stores
#18  Graph DBMS
Websitecloud.google.com/­datastoregithub.com/­hugegraph
hugegraph.apache.org
azure.microsoft.com/­en-us/­services/­searchsphinxsearch.comsurrealdb.com
Technical documentationcloud.google.com/­datastore/­docshugegraph.apache.org/­docslearn.microsoft.com/­en-us/­azure/­searchsphinxsearch.com/­docssurrealdb.com/­docs
DeveloperGoogleBaiduMicrosoftSphinx Technologies Inc.SurrealDB Ltd
Initial release20082018201520012022
Current release0.9V13.5.1, February 2023v1.5.0, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialOpen Source infoGPL version 2, commercial licence availableOpen Source
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Rust
Server operating systemshostedLinux
macOS
Unix
hostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
macOS
Windows
Data schemeschema-freeyesyesyesschema-free
Typing infopredefined data types such as float or dateyes, details hereyesyesnoyes
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.nonono
Secondary indexesyesyes infoalso supports composite index and range indexyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query language (GQL)nonoSQL-like query language (SphinxQL)SQL-like query language
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
RESTful HTTP APIProprietary protocolGraphQL
RESTful HTTP API
WebSocket
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Groovy
Java
Python
C#
Java
JavaScript
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Deno
Go
JavaScript (Node.js)
Rust
Server-side scripts infoStored proceduresusing Google App Engineasynchronous Gremlin script jobsnono
TriggersCallbacks using the Google Apps Enginenonono
Partitioning methods infoMethods for storing different data on different nodesShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding infoImplicit feature of the cloud serviceSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyes infodepending on used storage backend, e.g. Cassandra and HBaseyes infoImplicit feature of the cloud servicenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowvia hugegraph-sparknonono
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.Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyes infoedges in graphnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users, roles and permissionsyes infousing Azure authenticationnoyes, based on authentication and database rules

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More resources
Google Cloud DatastoreHugeGraphMicrosoft Azure AI SearchSphinxSurrealDB
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