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DBMS > Hazelcast vs. IBM Db2 Event Store vs. Ingres vs. Microsoft Azure Data Explorer vs. Solr

System Properties Comparison Hazelcast vs. IBM Db2 Event Store vs. Ingres vs. Microsoft Azure Data Explorer vs. Solr

Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonIngres  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionA widely adopted in-memory data gridDistributed Event Store optimized for Internet of Things use casesWell established RDBMSFully managed big data interactive analytics platformA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelKey-value storeEvent Store
Time Series DBMS
Relational DBMSRelational DBMS infocolumn orientedSearch engine
Secondary database modelsDocument store infoJSON support with IMDG 3.12Document 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
Score5.46
Rank#61  Overall
#7  Key-value stores
Score0.27
Rank#309  Overall
#2  Event Stores
#28  Time Series DBMS
Score3.80
Rank#82  Overall
#44  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websitehazelcast.comwww.ibm.com/­products/­db2-event-storewww.actian.com/­databases/­ingresazure.microsoft.com/­services/­data-explorersolr.apache.org
Technical documentationhazelcast.org/­imdg/­docswww.ibm.com/­docs/­en/­db2-event-storedocs.actian.com/­ingresdocs.microsoft.com/­en-us/­azure/­data-explorersolr.apache.org/­resources.html
DeveloperHazelcastIBMActian CorporationMicrosoftApache Software Foundation
Initial release200820171974 infooriginally developed at University Berkely in early 1970s20192006
Current release5.3.6, November 20232.011.2, May 2022cloud service with continuous releases9.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availablecommercial infofree developer edition availablecommercialcommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC and C++CJava
Server operating systemsAll OS with a Java VMLinux infoLinux, macOS, Windows for the developer additionAIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyesyesFixed schema with schema-less datatypes (dynamic)yes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes 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 typing
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.yes infothe object must implement a serialization strategynono infobut tools for importing/exporting data from/to XML-files availableyesyes
Secondary indexesyesnoyesall fields are automatically indexedyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageyes infothrough the embedded Spark runtimeyesKusto Query Language (KQL), SQL subsetSolr Parallel SQL Interface
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
.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
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor ServicesyesyesYes, possible languages: KQL, Python, RJava plugins
Triggersyes infoEventsnoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning infoIngres Star to access multiple databases simultaneouslySharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapActive-active shard replicationIngres Replicatoryes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitednoACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesNo - written data is immutableyes infoMVCCyesyes
Durability infoSupport for making data persistentyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnonoyes
User concepts infoAccess controlRole-based access controlfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardAzure Active Directory Authenticationyes

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More resources
HazelcastIBM Db2 Event StoreIngresMicrosoft Azure Data ExplorerSolr
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