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DBMS > Amazon DocumentDB vs. Apache IoTDB vs. HBase vs. Solr

System Properties Comparison Amazon DocumentDB vs. Apache IoTDB vs. HBase vs. Solr

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonApache IoTDB  Xexclude from comparisonHBase  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkWide-column store based on Apache Hadoop and on concepts of BigTableA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument storeTime Series DBMSWide column storeSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#131  Overall
#24  Document stores
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Score41.02
Rank#24  Overall
#3  Search engines
Websiteaws.amazon.com/­documentdbiotdb.apache.orghbase.apache.orgsolr.apache.org
Technical documentationaws.amazon.com/­documentdb/­resourcesiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlhbase.apache.org/­book.htmlsolr.apache.org/­resources.html
DeveloperApache Software FoundationApache Software Foundation infoApache top-level project, originally developed by PowersetApache Software Foundation
Initial release2019201820082006
Current release1.1.0, April 20232.3.4, January 20219.6.1, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache version 2Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJava
Server operating systemshostedAll OS with a Java VM (>= 1.8)Linux
Unix
Windows infousing Cygwin
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyesschema-free, schema definition possibleyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVROyes 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.nononoyes
Secondary indexesyesyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoSQL-like query languagenoSolr Parallel SQL Interface
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
Native API
Java API
RESTful HTTP API
Thrift
Java API
RESTful HTTP/JSON API
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
C
C#
C++
Go
Java
Python
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnoyesyes infoCoprocessors in JavaJava plugins
Triggersnoyesyesyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)Integration with Hadoop and Sparkyesspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnoSingle row ACID (across millions of columns)optimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes
User concepts infoAccess controlAccess rights for users and rolesyesAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACyes

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More resources
Amazon DocumentDBApache IoTDBHBaseSolr
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