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DBMS > Apache IoTDB vs. Dragonfly vs. Hawkular Metrics vs. Teradata Aster

System Properties Comparison Apache IoTDB vs. Dragonfly vs. Hawkular Metrics vs. Teradata Aster

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDragonfly  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
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 drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instanceHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Platform for big data analytics on multistructured data sources and types
Primary database modelTime Series DBMSKey-value storeTime Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.49
Rank#261  Overall
#38  Key-value stores
Score0.08
Rank#366  Overall
#39  Time Series DBMS
Websiteiotdb.apache.orggithub.com/­dragonflydb/­dragonfly
www.dragonflydb.io
www.hawkular.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.dragonflydb.io/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guide
DeveloperApache Software FoundationDragonflyDB team and community contributorsCommunity supported by Red HatTeradata
Initial release2018202320142005
Current release1.1.0, April 20231.0, March 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoBSL 1.1Open Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Java
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxLinux
OS X
Windows
Linux
Data schemeyesscheme-freeschema-freeFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesstrings, hashes, lists, sets, sorted sets, bit arraysyesyes
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 infoin Aster File Store
Secondary indexesyesnonoyes
SQL infoSupport of SQLSQL-like query languagenonoyes
APIs and other access methodsJDBC
Native API
Proprietary protocol infoRESP - REdis Serialization ProtocolHTTP RESTADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl
Go
Java
Python
Ruby
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyesLuanoR packages
Triggersyespublish/subscribe channels provide some trigger functionalityyes infovia Hawkular Alertingno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infobased on CassandraSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replicationselectable replication factor infobased on Cassandrayes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of command blocks and scriptsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyes, strict serializability by the serveryesyes
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.yesyesnono
User concepts infoAccess controlyesPassword-based authenticationnofine grained access rights according to SQL-standard

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More resources
Apache IoTDBDragonflyHawkular MetricsTeradata Aster
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