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DBMS > Google Cloud Bigtable vs. HarperDB vs. RDF4J vs. Spark SQL

System Properties Comparison Google Cloud Bigtable vs. HarperDB vs. RDF4J vs. Spark SQL

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonHarperDB  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSpark SQL  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.RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelKey-value store
Wide column store
Document storeRDF storeRelational DBMS
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
Score0.69
Rank#230  Overall
#9  RDF stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitecloud.google.com/­bigtablewww.harperdb.iordf4j.orgspark.apache.org/­sql
Technical documentationcloud.google.com/­bigtable/­docsdocs.harperdb.io/­docsrdf4j.org/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGoogleHarperDBSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Apache Software Foundation
Initial release2015201720042014
Current release3.1, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercialcommercial infofree community edition availableOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageNode.jsJavaScala
Server operating systemshostedLinux
OS X
Linux
OS X
Unix
Windows
Linux
OS X
Windows
Data schemeschema-freedynamic schemayes infoRDF Schemasyes
Typing infopredefined data types such as float or datenoyes infoJSON data typesyesyes
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 indexesnoyesyesno
SQL infoSupport of SQLnoSQL-like data manipulation statementsnoSQL-like DML and DDL statements
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
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
JDBC
ODBC
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
Java
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoCustom Functions infosince release 3.1yesno
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingA table resides as a whole on one (or more) nodes in a clusternoneyes, utilizing Spark Core
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 definednonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
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 integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsAtomic execution of specific operationsACID infoIsolation support depends on the API usedno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes, using LMDByes infoin-memory storage is supported as wellyes
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)Access rights for users and rolesnono

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More resources
Google Cloud BigtableHarperDBRDF4J infoformerly known as SesameSpark SQL
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