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DBMS > Apache Phoenix vs. GridDB vs. H2 vs. Solr

System Properties Comparison Apache Phoenix vs. GridDB vs. H2 vs. Solr

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGridDB  Xexclude from comparisonH2  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseScalable in-memory time series database optimized for IoT and Big DataFull-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.A widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSTime Series DBMSRelational DBMSSearch engine
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score8.13
Rank#49  Overall
#31  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitephoenix.apache.orggriddb.netwww.h2database.comsolr.apache.org
Technical documentationphoenix.apache.orgdocs.griddb.netwww.h2database.com/­html/­main.htmlsolr.apache.org/­resources.html
DeveloperApache Software FoundationToshiba CorporationThomas MuellerApache Software Foundation
Initial release2014201320052006
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20195.1, August 20222.2.220, July 20239.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infodual-licence (Mozilla public license, Eclipse public license)Open 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 languageJavaC++JavaJava
Server operating systemsLinux
Unix
Windows
LinuxAll OS with a Java VMAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes 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 indexesyesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesSQL92, SQL-like TQL (Toshiba Query Language)yesSolr Parallel SQL Interface
APIs and other access methodsJDBCJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Java.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functionsnoJava Stored Procedures and User-Defined FunctionsJava plugins
Triggersnoyesyesyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationWith clustering: 2 database servers on different computers operate on identical copies of a databaseyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate consistency within container, eventual consistency across containersImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID at container levelACIDoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes
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.yesyesyesyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users can be defined per databasefine grained access rights according to SQL-standardyes
More information provided by the system vendor
Apache PhoenixGridDBH2Solr
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Apache PhoenixGridDBH2Solr
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