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

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

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonHugeGraph  Xexclude from comparisonIgnite  Xexclude from comparisonKdb  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 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.High performance Time Series DBMS
Primary database modelTime Series DBMSGraph DBMSKey-value store
Relational DBMS
Time Series DBMS
Vector DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Websiteiotdb.apache.orggithub.com/­hugegraph
hugegraph.apache.org
ignite.apache.orgkx.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlhugegraph.apache.org/­docsapacheignite.readme.io/­docscode.kx.com
DeveloperApache Software FoundationBaiduApache Software FoundationKx Systems, a division of First Derivatives plc
Initial release2018201820152000 infokdb was released 2000, kdb+ in 2003
Current release1.1.0, April 20230.9Apache Ignite 2.63.6, May 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoApache 2.0commercial infofree 32-bit version
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++, Java, .Netq
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
macOS
Unix
Linux
OS X
Solaris
Windows
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.nonoyesyes
Secondary indexesyesyes infoalso supports composite index and range indexyesyes infotable attribute 'grouped'
SQL infoSupport of SQLSQL-like query languagenoANSI-99 for query and DML statements, subset of DDLSQL-like query language (q)
APIs and other access methodsJDBC
Native API
Java API
RESTful HTTP API
TinkerPop Gremlin
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Groovy
Java
Python
C#
C++
Java
PHP
Python
Ruby
Scala
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
Server-side scripts infoStored proceduresyesasynchronous Gremlin script jobsyes (compute grid and cache interceptors can be used instead)user defined functions
Triggersyesnoyes (cache interceptors and events)yes infowith views
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)yes infodepending on used storage backend, e.g. Cassandra and HBaseShardinghorizontal partitioning
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 infodepending on used storage backend, e.g. Cassandra and HBaseyes (replicated cache)Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkvia hugegraph-sparkyes (compute grid and hadoop accelerator)no infosimilar paradigm used for internal processing
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes infoedges in graphnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDno
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 controlyesUsers, roles and permissionsSecurity Hooks for custom implementationsrights management via user accounts
More information provided by the system vendor
Apache IoTDBHugeGraphIgniteKdb
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsupon request
» more

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Apache IoTDBHugeGraphIgniteKdb
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