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DBMS > Hypertable vs. Riak KV vs. Spark SQL

System Properties Comparison Hypertable vs. Riak KV vs. Spark SQL

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Editorial information provided by DB-Engines
NameHypertable  Xexclude from comparisonRiak KV  Xexclude from comparisonSpark SQL  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionAn open source BigTable implementation based on distributed file systems such as HadoopDistributed, fault tolerant key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelWide column storeKey-value store infowith links between data sets and object tags for the creation of secondary indexesRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.07
Rank#67  Overall
#9  Key-value stores
Score19.46
Rank#34  Overall
#21  Relational DBMS
Websiteriak.com/­products/­riak-kvspark.apache.org/­sql
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperHypertable Inc.Basho TechnologiesApache Software Foundation
Initial release200920092014
Current release0.9.8.11, March 20162.1.0, April 20153.0.2, February 2021
License infoCommercial or Open SourceOpen Source infoGNU version 3. Commercial license availableOpen Source infoApache version 2, commercial enterprise editionOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ErlangScala
Server operating systemsLinux
OS X
Windows infoan inofficial Windows port is available
Linux
OS X
Linux
OS X
Windows
Data schemeschema-freeschema-freeyes
Typing infopredefined data types such as float or datenonoyes
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 indexesrestricted infoonly exact value or prefix value scansrestrictedno
SQL infoSupport of SQLnonoSQL-like DML and DDL statements
APIs and other access methodsC++ API
Thrift
HTTP API
Native Erlang Interface
JDBC
ODBC
Supported programming languagesC++
Java
Perl
PHP
Python
Ruby
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoJavaScript and Erlangno
Triggersnoyes infopre-commit hooks and post-commit hooksno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infono "single point of failure"yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor on file system levelselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynono infolinks between data sets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlnonono

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HypertableRiak KVSpark SQL
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