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DBMS > Google Cloud Bigtable vs. Hazelcast vs. HyperSQL vs. Sphinx

System Properties Comparison Google Cloud Bigtable vs. Hazelcast vs. HyperSQL vs. Sphinx

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonHazelcast  Xexclude from comparisonHyperSQL infoalso known as HSQLDB  Xexclude from comparisonSphinx  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.A widely adopted in-memory data gridMultithreaded, transactional RDBMS written in Java infoalso known as HSQLDBOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelKey-value store
Wide column store
Key-value storeRelational DBMSSearch engine
Secondary database modelsDocument store infoJSON support with IMDG 3.12
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
Score5.97
Rank#57  Overall
#6  Key-value stores
Score3.49
Rank#87  Overall
#47  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websitecloud.google.com/­bigtablehazelcast.comhsqldb.orgsphinxsearch.com
Technical documentationcloud.google.com/­bigtable/­docshazelcast.org/­imdg/­docshsqldb.org/­web/­hsqlDocsFrame.htmlsphinxsearch.com/­docs
DeveloperGoogleHazelcastSphinx Technologies Inc.
Initial release2015200820012001
Current release5.3.6, November 20232.7.2, June 20233.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infobased on BSD licenseOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++
Server operating systemshostedAll OS with a Java VMAll OS with a Java VM infoEmbedded (into Java applications) and Client-Server operating modesFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or datenoyesyesno
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.noyes infothe object must implement a serialization strategyno
Secondary indexesnoyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLnoSQL-like query languageyesSQL-like query language (SphinxQL)
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JCache
JPA
Memcached protocol
RESTful HTTP API
HTTP API infoJDBC via HTTP
JDBC
ODBC
Proprietary protocol
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
All languages supporting JDBC/ODBC
Java
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor ServicesJava, SQLno
Triggersnoyes infoEventsyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding 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 infoReplicated Mapnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesnono
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 or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsone or two-phase-commit; repeatable reads; read commitedACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes 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.noyesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access controlfine grained access rights according to SQL-standardno

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More resources
Google Cloud BigtableHazelcastHyperSQL infoalso known as HSQLDBSphinx
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