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DBMS > Amazon Aurora vs. HarperDB vs. Sphinx vs. Splice Machine

System Properties Comparison Amazon Aurora vs. HarperDB vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonHarperDB  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonHarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.Open source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSDocument storeSearch engineRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.84
Rank#44  Overall
#28  Relational DBMS
Score0.55
Rank#243  Overall
#38  Document stores
Score5.97
Rank#56  Overall
#5  Search engines
Score0.54
Rank#244  Overall
#114  Relational DBMS
Websiteaws.amazon.com/­rds/­aurorawww.harperdb.iosphinxsearch.comsplicemachine.com
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmldocs.harperdb.io/­docssphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperAmazonHarperDBSphinx Technologies Inc.Splice Machine
Initial release2015201720012014
Current release3.1, August 20213.5.1, February 20233.1, March 2021
License infoCommercial or Open Sourcecommercialcommercial infofree community edition availableOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageNode.jsC++Java
Server operating systemshostedLinux
OS X
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesdynamic schemayesyes
Typing infopredefined data types such as float or dateyesyes infoJSON data typesnoyes
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.yesno
Secondary indexesyesyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLyesSQL-like data manipulation statementsSQL-like query language (SphinxQL)yes
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
.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
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyesCustom Functions infosince release 3.1noyes infoJava
Triggersyesnonoyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningA table resides as a whole on one (or more) nodes in a clusterSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infothe nodes on which a table resides can be definednoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic execution of specific operationsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes, using LMDByes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users and rolesnoAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon AuroraHarperDBSphinxSplice Machine
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