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DBMS > Google Cloud Datastore vs. HarperDB vs. Hypertable vs. Sphinx

System Properties Comparison Google Cloud Datastore vs. HarperDB vs. Hypertable vs. Sphinx

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHarperDB  Xexclude from comparisonHypertable  Xexclude from comparisonSphinx  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformUltra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.An open source BigTable implementation based on distributed file systems such as HadoopOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument storeDocument storeWide column storeSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score0.60
Rank#244  Overall
#38  Document stores
Score5.95
Rank#55  Overall
#5  Search engines
Websitecloud.google.com/­datastorewww.harperdb.iosphinxsearch.com
Technical documentationcloud.google.com/­datastore/­docsdocs.harperdb.io/­docssphinxsearch.com/­docs
DeveloperGoogleHarperDBHypertable Inc.Sphinx Technologies Inc.
Initial release2008201720092001
Current release3.1, August 20210.9.8.11, March 20163.5.1, February 2023
License infoCommercial or Open Sourcecommercialcommercial infofree community edition availableOpen Source infoGNU version 3. Commercial license availableOpen Source infoGPL version 2, commercial licence available
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 languageNode.jsC++C++
Server operating systemshostedLinux
OS X
Linux
OS X
Windows infoan inofficial Windows port is available
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freedynamic schemaschema-freeyes
Typing infopredefined data types such as float or dateyes, details hereyes infoJSON data typesnono
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.nono
Secondary indexesyesyesrestricted infoonly exact value or prefix value scansyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like data manipulation statementsnoSQL-like query language (SphinxQL)
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
C++ API
Thrift
Proprietary protocol
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
C++
Java
Perl
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresusing Google App EngineCustom Functions infosince release 3.1nono
TriggersCallbacks using the Google Apps Enginenonono
Partitioning methods infoMethods for storing different data on different nodesShardingA table resides as a whole on one (or more) nodes in a clusterShardingSharding 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 infothe nodes on which a table resides can be definedselectable replication factor on file system levelnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownoyesno
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
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsAtomic execution of specific operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes, using LMDByesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and rolesnono

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More resources
Google Cloud DatastoreHarperDBHypertableSphinx
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