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DBMS > Datomic vs. IBM Db2 Event Store vs. MarkLogic vs. Solr

System Properties Comparison Datomic vs. IBM Db2 Event Store vs. MarkLogic vs. Solr

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonMarkLogic  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityDistributed Event Store optimized for Internet of Things use casesOperational and transactional Enterprise NoSQL databaseA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSEvent Store
Time Series DBMS
Document store
Native XML DBMS
RDF store infoas of version 7
Search engine
Search engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score0.19
Rank#323  Overall
#2  Event Stores
#28  Time Series DBMS
Score5.92
Rank#58  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#6  Search engines
Score42.91
Rank#24  Overall
#3  Search engines
Websitewww.datomic.comwww.ibm.com/­products/­db2-event-storewww.marklogic.comsolr.apache.org
Technical documentationdocs.datomic.comwww.ibm.com/­docs/­en/­db2-event-storedocs.marklogic.comsolr.apache.org/­resources.html
DeveloperCognitectIBMMarkLogic Corp.Apache Software Foundation
Initial release2012201720012006
Current release1.0.6735, June 20232.011.0, December 20229.5.0, February 2024
License infoCommercial or Open Sourcecommercial infolimited edition freecommercial infofree developer edition availablecommercial inforestricted free version is availableOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureC and C++C++Java
Server operating systemsAll OS with a Java VMLinux infoLinux, macOS, Windows for the developer additionLinux
OS X
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesschema-free infoSchema can be enforcedyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes 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.nonoyesyes
Secondary indexesyesnoyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoyes infothrough the embedded Spark runtimeyes infoSQL92Solr Parallel SQL Interface
APIs and other access methodsRESTful HTTP APIADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Java API
RESTful HTTP/JSON API
Supported programming languagesClojure
Java
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyes infoTransaction Functionsyesyes infovia XQuery or JavaScriptJava plugins
TriggersBy using transaction functionsnoyesyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersActive-active shard replicationyesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID infocan act as a resource manager in an XA/JTA transactionoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesNo - written data is immutableyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentyesyes, with Range Indexesyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardRole-based access control at the document and subdocument levelsyes

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More resources
DatomicIBM Db2 Event StoreMarkLogicSolr
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