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DBMS > Apache IoTDB vs. Apache Phoenix vs. Faircom EDGE vs. Hive

System Properties Comparison Apache IoTDB vs. Apache Phoenix vs. Faircom EDGE vs. Hive

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonHive  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA scale-out RDBMS with evolutionary schema built on Apache HBaseFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsdata warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelTime Series DBMSRelational DBMSKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.09
Rank#362  Overall
#53  Key-value stores
#155  Relational DBMS
Score59.76
Rank#18  Overall
#12  Relational DBMS
Websiteiotdb.apache.orgphoenix.apache.orgwww.faircom.com/­products/­faircom-edgehive.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlphoenix.apache.orgdocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmlcwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperApache Software FoundationApache Software FoundationFairCom CorporationApache Software Foundation infoinitially developed by Facebook
Initial release2018201419792012
Current release1.1.0, April 20235.0-HBase2, July 2018 and 4.15-HBase1, December 2019V3, October 20203.1.3, April 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0commercial infoRestricted, free version 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 languageJavaJavaANSI C, C++Java
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
Unix
Windows
Android
Linux infoARM, x86
Raspbian
Windows
All OS with a Java VM
Data schemeyesyes infolate-bound, schema-on-read capabilitiesFlexible Schema (defined schema, partial schema, schema free)yes
Typing infopredefined data types such as float or dateyesyesyes, ANSI Standard SQL Typesyes
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 indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageyesyes infoANSI SQL queriesSQL-like DML and DDL statements
APIs and other access methodsJDBC
Native API
JDBCADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
JDBC
ODBC
Thrift
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C#
C++
Java
JavaScript
PHP
Python
VB.Net
C++
Java
PHP
Python
Server-side scripts infoStored proceduresyesuser defined functionsyes info.Net, JavaScript, C/C++yes infouser defined functions and integration of map-reduce
Triggersyesnoyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingFile partitioning infoCustomizable business rules for partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
yes infoSynchronous and asynchronous realtime replication based on transaction logsselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkHadoop integrationnoyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual ConsistencyImmediate Consistency
Tunable Consistency
Eventual Consistency
Foreign keys infoReferential integritynonoyes infowhen using SQLno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoacross SQL and NoSQLyes
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 controlyesAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.Access rights for users, groups and roles

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Apache IoTDBApache PhoenixFaircom EDGE infoformerly c-treeEDGEHive
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