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DBMS > Amazon Redshift vs. CouchDB vs. Kinetica vs. RocksDB vs. Spark SQL

System Properties Comparison Amazon Redshift vs. CouchDB vs. Kinetica vs. RocksDB vs. Spark SQL

Editorial information provided by DB-Engines
NameAmazon Redshift  Xexclude from comparisonCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"  Xexclude from comparisonKinetica  Xexclude from comparisonRocksDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionLarge scale data warehouse service for use with business intelligence toolsA native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.Fully vectorized database across both GPUs and CPUsEmbeddable persistent key-value store optimized for fast storage (flash and RAM)Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument storeRelational DBMSKey-value storeRelational DBMS
Secondary database modelsSpatial DBMS infousing the Geocouch extensionSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.94
Rank#34  Overall
#21  Relational DBMS
Score9.30
Rank#45  Overall
#7  Document stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score3.65
Rank#85  Overall
#11  Key-value stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­redshiftcouchdb.apache.orgwww.kinetica.comrocksdb.orgspark.apache.org/­sql
Technical documentationdocs.aws.amazon.com/­redshiftdocs.couchdb.org/­en/­stabledocs.kinetica.comgithub.com/­facebook/­rocksdb/­wikispark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazon (based on PostgreSQL)Apache Software Foundation infoApache top-level project, originally developed by Damien Katz, a former Lotus Notes developerKineticaFacebook, Inc.Apache Software Foundation
Initial release20122005201220132014
Current release3.3.3, December 20237.1, August 20218.11.4, April 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2commercialOpen Source infoBSDOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCErlangC, C++C++Scala
Server operating systemshostedAndroid
BSD
Linux
OS X
Solaris
Windows
LinuxLinuxLinux
OS X
Windows
Data schemeyesschema-freeyesschema-freeyes
Typing infopredefined data types such as float or dateyesnoyesnoyes
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.nonononono
Secondary indexesrestrictedyes infovia viewsyesnono
SQL infoSupport of SQLyes infodoes not fully support an SQL-standardnoSQL-like DML and DDL statementsnoSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP/JSON APIJDBC
ODBC
RESTful HTTP API
C++ API
Java API
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk
C++
Java
JavaScript (Node.js)
Python
C
C++
Go
Java
Perl
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions infoin PythonView functions in JavaScriptuser defined functionsnono
Triggersnoyesyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoimproved architecture with release 2.0Shardinghorizontal partitioningyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
Source-replica replicationyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infoinformational only, not enforced by the systemnoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infoatomic operations within a single document possiblenoyesno
Concurrency infoSupport for concurrent manipulation of datayesyes infostrategy: optimistic lockingyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes infoGPU vRAM or System RAMyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users can be defined per databaseAccess rights for users and roles on table levelnono

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More resources
Amazon RedshiftCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"KineticaRocksDBSpark SQL
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