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DBMS > Geode vs. Microsoft Azure Data Explorer vs. Solr vs. Sphinx vs. Splice Machine

System Properties Comparison Geode vs. Microsoft Azure Data Explorer vs. Solr vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameGeode  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSolr  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionGeode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processesFully managed big data interactive analytics platformA 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 Spark
Primary database modelKey-value storeRelational DBMS infocolumn orientedSearch engineSearch engineRelational DBMS
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.86
Rank#134  Overall
#24  Key-value stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitegeode.apache.orgazure.microsoft.com/­services/­data-explorersolr.apache.orgsphinxsearch.comsplicemachine.com
Technical documentationgeode.apache.org/­docsdocs.microsoft.com/­en-us/­azure/­data-explorersolr.apache.org/­resources.htmlsphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperOriginally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.MicrosoftApache Software FoundationSphinx Technologies Inc.Splice Machine
Initial release20022019200620012014
Current release1.1, February 2017cloud service with continuous releases9.6.1, May 20243.5.1, February 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses available as GemfirecommercialOpen Source infoApache Version 2Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC++Java
Server operating systemsAll OS with a Java VM infothe JDK (8 or later) is also requiredhostedAll 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
Data schemeschema-freeFixed schema with schema-less datatypes (dynamic)yes infoDynamic Fields enables on-the-fly addition of new fieldsyesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes infosupports customizable data types and automatic typingnoyes
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 indexesnoall fields are automatically indexedyes infoAll search fields are automatically indexedyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLSQL-like query language (OQL)Kusto Query Language (KQL), SQL subsetSolr Parallel SQL InterfaceSQL-like query language (SphinxQL)yes
APIs and other access methodsJava Client API
Memcached protocol
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
All JVM based languages
C++
Groovy
Java
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.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
Server-side scripts infoStored proceduresuser defined functionsYes, possible languages: KQL, Python, RJava pluginsnoyes infoJava
Triggersyes infoCache Event Listenersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoUser configurable commands triggered on index changesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceShardingSharding 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-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yesnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reducenoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Immediate Consistency
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayes, on a single nodenooptimistic lockingnoACID
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.yesnoyesyes
User concepts infoAccess controlAccess rights per client and object definableAzure Active Directory AuthenticationyesnoAccess rights for users, groups and roles according to SQL-standard

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More resources
GeodeMicrosoft Azure Data ExplorerSolrSphinxSplice Machine
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