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DBMS > Elasticsearch vs. Graph Engine vs. Microsoft Azure Data Explorer vs. Netezza vs. Sphinx

System Properties Comparison Elasticsearch vs. Graph Engine vs. Microsoft Azure Data Explorer vs. Netezza vs. Sphinx

Editorial information provided by DB-Engines
NameElasticsearch  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionA distributed, RESTful modern search and analytics engine based on Apache Lucene infoElasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metricA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineFully managed big data interactive analytics platformData warehouse and analytics appliance part of IBM PureSystemsOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelSearch engineGraph DBMS
Key-value store
Relational DBMS infocolumn orientedRelational DBMSSearch engine
Secondary database modelsDocument store
Spatial DBMS
Vector DBMS
Document 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score135.35
Rank#7  Overall
#1  Search engines
Score0.61
Rank#240  Overall
#21  Graph DBMS
#35  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websitewww.elastic.co/­elasticsearchwww.graphengine.ioazure.microsoft.com/­services/­data-explorerwww.ibm.com/­products/­netezzasphinxsearch.com
Technical documentationwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmlwww.graphengine.io/­docs/­manualdocs.microsoft.com/­en-us/­azure/­data-explorersphinxsearch.com/­docs
DeveloperElasticMicrosoftMicrosoftIBMSphinx Technologies Inc.
Initial release20102010201920002001
Current release8.6, January 2023cloud service with continuous releases3.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoElastic LicenseOpen Source infoMIT LicensecommercialcommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava.NET and CC++
Server operating systemsAll OS with a Java VM.NEThostedLinux infoincluded in applianceFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-free infoFlexible type definitions. Once a type is defined, it is persistentyesFixed schema with schema-less datatypes (dynamic)yesyes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesno
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.nonoyes
Secondary indexesyes infoAll search fields are automatically indexedall fields are automatically indexedyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languagenoKusto Query Language (KQL), SQL subsetyesSQL-like query language (SphinxQL)
APIs and other access methodsJava API
RESTful HTTP/JSON API
RESTful HTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
OLE DB
Proprietary protocol
Supported programming languages.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
C#
C++
F#
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Fortran
Java
Lua
Perl
Python
R
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesyesYes, possible languages: KQL, Python, Ryesno
Triggersyes infoby using the 'percolation' featurenoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynono
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningSharding infoImplicit feature of the cloud serviceShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsES-Hadoop ConnectorSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.Memcached and Redis integrationyesno
User concepts infoAccess controlAzure Active Directory AuthenticationUsers with fine-grained authorization conceptno

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More resources
ElasticsearchGraph Engine infoformer name: TrinityMicrosoft Azure Data ExplorerNetezza infoAlso called PureData System for Analytics by IBMSphinx
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