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DBMS > Apache IoTDB vs. EsgynDB vs. Hive vs. HugeGraph vs. Spark SQL

System Properties Comparison Apache IoTDB vs. EsgynDB vs. Hive vs. HugeGraph vs. Spark SQL

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonHive  Xexclude from comparisonHugeGraph  Xexclude from comparisonSpark SQL  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 FlinkEnterprise-class SQL-on-Hadoop solution, powered by Apache Trafodiondata warehouse software for querying and managing large distributed datasets, built on HadoopA fast-speed and highly-scalable Graph DBMSSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelTime Series DBMSRelational DBMSRelational DBMSGraph DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteiotdb.apache.orgwww.esgyn.cnhive.apache.orggithub.com/­hugegraph
hugegraph.apache.org
spark.apache.org/­sql
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcwiki.apache.org/­confluence/­display/­Hive/­Homehugegraph.apache.org/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationEsgynApache Software Foundation infoinitially developed by FacebookBaiduApache Software Foundation
Initial release20182015201220182014
Current release1.1.0, April 20233.1.3, April 20220.93.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++, JavaJavaJavaScala
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxAll OS with a Java VMLinux
macOS
Unix
Linux
OS X
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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.nononono
Secondary indexesyesyesyesyes infoalso supports composite index and range indexno
SQL infoSupport of SQLSQL-like query languageyesSQL-like DML and DDL statementsnoSQL-like DML and DDL statements
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
ODBC
JDBC
ODBC
Thrift
Java API
RESTful HTTP API
TinkerPop Gremlin
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
All languages supporting JDBC/ODBC/ADO.NetC++
Java
PHP
Python
Groovy
Java
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesJava Stored Proceduresyes infouser defined functions and integration of map-reduceasynchronous Gremlin script jobsno
Triggersyesnononono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseyes, utilizing Spark Core
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 between multi datacentersselectable replication factoryes infodepending on used storage backend, e.g. Cassandra and HBasenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesyes infoquery execution via MapReducevia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesnoyes infoedges in graphno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlyesfine grained access rights according to SQL-standardAccess rights for users, groups and rolesUsers, roles and permissionsno

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More resources
Apache IoTDBEsgynDBHiveHugeGraphSpark SQL
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