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DBMS > Amazon DocumentDB vs. Apache Impala vs. Riak TS vs. Sphinx vs. Splice Machine

System Properties Comparison Amazon DocumentDB vs. Apache Impala vs. Riak TS vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonApache Impala  Xexclude from comparisonRiak TS  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceAnalytic DBMS for HadoopRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KVOpen 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 modelDocument storeRelational DBMSTime Series DBMSSearch engineRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.89
Rank#137  Overall
#24  Document stores
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score0.25
Rank#308  Overall
#26  Time Series DBMS
Score6.03
Rank#60  Overall
#6  Search engines
Score0.54
Rank#255  Overall
#116  Relational DBMS
Websiteaws.amazon.com/­documentdbimpala.apache.orgsphinxsearch.comsplicemachine.com
Technical documentationaws.amazon.com/­documentdb/­resourcesimpala.apache.org/­impala-docs.htmlwww.tiot.jp/­riak-docs/­riak/­ts/­latestsphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaOpen Source, formerly Basho TechnologiesSphinx Technologies Inc.Splice Machine
Initial release20192013201520012014
Current release4.1.0, June 20223.0.0, September 20223.5.1, February 20233.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open SourceOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ErlangC++Java
Server operating systemshostedLinuxLinux
OS X
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeschema-freeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesnonoyes
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 indexesyesyesrestrictedyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyes, limitedSQL-like query language (SphinxQL)yes
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
ODBC
HTTP API
Native Erlang Interface
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
All languages supporting JDBC/ODBCC 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++ 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 proceduresnoyes infouser defined functions and integration of map-reduceErlangnoyes infoJava
Triggersnonoyes infopre-commit hooks and post-commit hooksnoyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingSharding 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 nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasselectable replication factorselectable replication factornoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)yes infoquery execution via MapReduceyesnoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenono infolinks between datasets can be storednoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes 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.noyes
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users, groups and roles infobased on Apache Sentry and KerberosnonoAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon DocumentDBApache ImpalaRiak TSSphinxSplice Machine
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