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DBMS > Microsoft Azure Table Storage vs. Solr vs. Sphinx vs. Splice Machine vs. TimescaleDB

System Properties Comparison Microsoft Azure Table Storage vs. Solr vs. Sphinx vs. Splice Machine vs. TimescaleDB

Editorial information provided by DB-Engines
NameMicrosoft Azure Table Storage  Xexclude from comparisonSolr  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionA Wide Column Store for rapid development using massive semi-structured datasetsA widely used distributed, scalable search engine based on Apache LuceneOpen 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 SparkA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelWide column storeSearch engineSearch engineRelational DBMSTime Series DBMS
Secondary database modelsSpatial DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.04
Rank#77  Overall
#6  Wide column stores
Score41.02
Rank#24  Overall
#3  Search engines
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websiteazure.microsoft.com/­en-us/­services/­storage/­tablessolr.apache.orgsphinxsearch.comsplicemachine.comwww.timescale.com
Technical documentationsolr.apache.org/­resources.htmlsphinxsearch.com/­docssplicemachine.com/­how-it-worksdocs.timescale.com
DeveloperMicrosoftApache Software FoundationSphinx Technologies Inc.Splice MachineTimescale
Initial release20122006200120142017
Current release9.6.1, May 20243.5.1, February 20233.1, March 20212.15.0, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++JavaC
Server operating systemshostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeschema-freeyes infoDynamic Fields enables on-the-fly addition of new fieldsyesyesyes
Typing infopredefined data types such as float or dateyesyes infosupports customizable data types and automatic typingnoyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.noyesyes
Secondary indexesnoyes infoAll search fields are automatically indexedyes infofull-text index on all search fieldsyesyes
SQL infoSupport of SQLnoSolr Parallel SQL InterfaceSQL-like query language (SphinxQL)yesyes infofull PostgreSQL SQL syntax
APIs and other access methodsRESTful HTTP APIJava API
RESTful HTTP/JSON API
Proprietary protocolJDBC
Native Spark Datasource
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languages.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnoJava pluginsnoyes infoJavauser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyes infoUser configurable commands triggered on index changesnoyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioningyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yesnoneMulti-source replication
Source-replica replication
Source-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reducenoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataoptimistic lockingoptimistic lockingnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesno
User concepts infoAccess controlAccess rights based on private key authentication or shared access signaturesyesnoAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Microsoft Azure Table StorageSolrSphinxSplice MachineTimescaleDB
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