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DBMS > Apache IoTDB vs. GridGain vs. HugeGraph vs. Ignite

System Properties Comparison Apache IoTDB vs. GridGain vs. HugeGraph vs. Ignite

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGridGain  Xexclude from comparisonHugeGraph  Xexclude from comparisonIgnite  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 FlinkGridGain is an in-memory computing platform, built on Apache IgniteA fast-speed and highly-scalable Graph DBMSApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.
Primary database modelTime Series DBMSKey-value store
Relational DBMS
Graph DBMSKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Websiteiotdb.apache.orgwww.gridgain.comgithub.com/­hugegraph
hugegraph.apache.org
ignite.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.gridgain.com/­docs/­index.htmlhugegraph.apache.org/­docsapacheignite.readme.io/­docs
DeveloperApache Software FoundationGridGain Systems, Inc.BaiduApache Software Foundation
Initial release2018200720182015
Current release1.1.0, April 2023GridGain 8.5.10.9Apache Ignite 2.6
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, C++, .NetJavaC++, Java, .Net
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Solaris
Windows
Linux
macOS
Unix
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.noyesnoyes
Secondary indexesyesyesyes infoalso supports composite index and range indexyes
SQL infoSupport of SQLSQL-like query languageANSI-99 for query and DML statements, subset of DDLnoANSI-99 for query and DML statements, subset of DDL
APIs and other access methodsJDBC
Native API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Java API
RESTful HTTP API
TinkerPop Gremlin
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
Groovy
Java
Python
C#
C++
Java
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyesyes (compute grid and cache interceptors can be used instead)asynchronous Gremlin script jobsyes (compute grid and cache interceptors can be used instead)
Triggersyesyes (cache interceptors and events)noyes (cache interceptors and events)
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardingyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes (replicated cache)yes infodepending on used storage backend, e.g. Cassandra and HBaseyes (replicated cache)
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyes (compute grid and hadoop accelerator)via hugegraph-sparkyes (compute grid and hadoop accelerator)
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes infoedges in graphno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID
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.yesyesyesyes
User concepts infoAccess controlyesSecurity Hooks for custom implementationsUsers, roles and permissionsSecurity Hooks for custom implementations

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More resources
Apache IoTDBGridGainHugeGraphIgnite
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