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DBMS > Amazon SimpleDB vs. Apache Phoenix vs. Riak KV vs. Splice Machine

System Properties Comparison Amazon SimpleDB vs. Apache Phoenix vs. Riak KV vs. Splice Machine

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Editorial information provided by DB-Engines
NameAmazon SimpleDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonRiak KV  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionHosted simple database service by Amazon, with the data stored in the Amazon Cloud. infoThere is an unrelated product called SimpleDB developed by Edward ScioreA scale-out RDBMS with evolutionary schema built on Apache HBaseDistributed, fault tolerant key-value storeOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelKey-value storeRelational DBMSKey-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
Score1.88
Rank#133  Overall
#23  Key-value stores
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score4.01
Rank#79  Overall
#9  Key-value stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteaws.amazon.com/­simpledbphoenix.apache.orgsplicemachine.com
Technical documentationdocs.aws.amazon.com/­simpledbphoenix.apache.orgwww.tiot.jp/­riak-docs/­riak/­kv/­latestsplicemachine.com/­how-it-works
DeveloperAmazonApache Software FoundationOpenSource, formerly Basho TechnologiesSplice Machine
Initial release2007201420092014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20193.2.0, December 20223.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache version 2, commercial enterprise editionOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaErlangJava
Server operating systemshostedLinux
Unix
Windows
Linux
OS X
Linux
OS X
Solaris
Windows
Data schemeschema-freeyes infolate-bound, schema-on-read capabilitiesschema-freeyes
Typing infopredefined data types such as float or datenoyesnoyes
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 indexesyes infoAll columns are indexed automaticallyyesrestrictedyes
SQL infoSupport of SQLnoyesnoyes
APIs and other access methodsRESTful HTTP APIJDBCHTTP API
Native Erlang Interface
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
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
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsErlangyes infoJava
Triggersnonoyes infopre-commit hooks and post-commit hooksyes
Partitioning methods infoMethods for storing different data on different nodesnone infoSharding must be implemented in the applicationShardingSharding infono "single point of failure"Shared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
selectable replication factorMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoHadoop integrationyesYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency infocan be specified for read operations
Immediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono infolinks between data sets can be storedyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoConcurrent data updates can be detected by the applicationACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyyes, using Riak SecurityAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon SimpleDBApache PhoenixRiak KVSplice Machine
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