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DBMS > Google Cloud Bigtable vs. HarperDB vs. Sphinx vs. Typesense

System Properties Comparison Google Cloud Bigtable vs. HarperDB vs. Sphinx vs. Typesense

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonHarperDB  Xexclude from comparisonSphinx  Xexclude from comparisonTypesense  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Ultra-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.Open source search engine for searching in data from different sources, e.g. relational databasesA typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity
Primary database modelKey-value store
Wide column store
Document storeSearch engineSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.55
Rank#248  Overall
#38  Document stores
Score5.98
Rank#56  Overall
#5  Search engines
Score0.79
Rank#210  Overall
#14  Search engines
Websitecloud.google.com/­bigtablewww.harperdb.iosphinxsearch.comtypesense.org
Technical documentationcloud.google.com/­bigtable/­docsdocs.harperdb.io/­docssphinxsearch.com/­docstypesense.org/­docs
DeveloperGoogleHarperDBSphinx Technologies Inc.
Initial release2015201720012015
Current release3.1, August 20213.5.1, February 2023
License infoCommercial or Open Sourcecommercialcommercial infofree community edition availableOpen Source infoGPL version 2, commercial licence availableOpen Source infoGPL V3
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
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
Data schemeschema-freedynamic schemayesschema-free infopre-defined schema optional
Typing infopredefined data types such as float or datenoyes infoJSON data typesnoyes
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 indexesnoyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoSQL-like data manipulation statementsSQL-like query language (SphinxQL)no
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
Proprietary protocolRESTful HTTP API
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
.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++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
.Net infocommunity maintained
Clojure infocommunity maintained
Dart infocommunity maintained
Go infocommunity maintained
Java infocommunity maintained
JavaScript
Perl infocommunity maintained
PHP
Python
Ruby
Rust infocommunity maintained
Swift infocommunity maintained
Server-side scripts infoStored proceduresnoCustom Functions infosince release 3.1nono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesShardingA table resides as a whole on one (or more) nodes in a clusterSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesyes infothe nodes on which a table resides can be definednoneMulti-source replication using RAFT
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsAtomic execution of specific operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes, using LMDByes 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.noyesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and rolesno

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More resources
Google Cloud BigtableHarperDBSphinxTypesense
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