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DBMS > Apache IoTDB vs. Ehcache vs. Firebase Realtime Database vs. Sphinx vs. Teradata Aster

System Properties Comparison Apache IoTDB vs. Ehcache vs. Firebase Realtime Database vs. Sphinx vs. Teradata Aster

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonEhcache  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonSphinx  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 widely adopted Java cache with tiered storage optionsCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Open source search engine for searching in data from different sources, e.g. relational databasesPlatform for big data analytics on multistructured data sources and types
Primary database modelTime Series DBMSKey-value storeDocument storeSearch engineRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score4.79
Rank#66  Overall
#8  Key-value stores
Score13.60
Rank#39  Overall
#6  Document stores
Score5.97
Rank#56  Overall
#5  Search engines
Websiteiotdb.apache.orgwww.ehcache.orgfirebase.google.com/­products/­realtime-databasesphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.ehcache.org/­documentationfirebase.google.com/­docs/­databasesphinxsearch.com/­docs
DeveloperApache Software FoundationTerracotta Inc, owned by Software AGGoogle infoacquired by Google 2014Sphinx Technologies Inc.Teradata
Initial release20182009201220012005
Current release1.1.0, April 20233.10.0, March 20223.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoGPL version 2, commercial licence availablecommercial
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++
Server operating systemsAll OS with a Java VM (>= 1.8)All OS with a Java VMhostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
Data schemeyesschema-freeschema-freeyesFlexible 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 dateyesyesyesnoyes
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 indexesyesnoyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLSQL-like query languagenonoSQL-like query language (SphinxQL)yes
APIs and other access methodsJDBC
Native API
JCacheAndroid
iOS
JavaScript API
RESTful HTTP API
Proprietary protocolADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
JavaJava
JavaScript
Objective-C
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyesnolimited functionality with using 'rules'noR packages
Triggersyesyes infoCache Event ListenersCallbacks are triggered when data changesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoby using Terracotta ServerSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding
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 infoby using Terracotta Servernoneyes 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 Sparknononoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Tunable Consistency (Strong, Eventual, Weak)Eventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyes infosupports JTA and can work as an XA resourceyesnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes infousing a tiered cache-storage approachyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlyesnoyes, based on authentication and database rulesnofine grained access rights according to SQL-standard

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More resources
Apache IoTDBEhcacheFirebase Realtime DatabaseSphinxTeradata Aster
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