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DBMS > Apache IoTDB vs. Apache Phoenix vs. Fauna vs. Spark SQL vs. Teradata Aster

System Properties Comparison Apache IoTDB vs. Apache Phoenix vs. Fauna vs. Spark SQL vs. Teradata Aster

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonFauna infopreviously named FaunaDB  Xexclude from comparisonSpark SQL  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 scale-out RDBMS with evolutionary schema built on Apache HBaseFauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.Spark SQL is a component on top of 'Spark Core' for structured data processingPlatform for big data analytics on multistructured data sources and types
Primary database modelTime Series DBMSRelational DBMSDocument store
Graph DBMS
Relational DBMS
Time Series DBMS
Relational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score1.52
Rank#153  Overall
#26  Document stores
#14  Graph DBMS
#71  Relational DBMS
#13  Time Series DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteiotdb.apache.orgphoenix.apache.orgfauna.comspark.apache.org/­sql
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlphoenix.apache.orgdocs.fauna.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationApache Software FoundationFauna, Inc.Apache Software FoundationTeradata
Initial release20182014201420142005
Current release1.1.0, April 20235.0-HBase2, July 2018 and 4.15-HBase1, December 20193.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0commercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaScalaScala
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
Unix
Windows
hostedLinux
OS X
Windows
Linux
Data schemeyesyes infolate-bound, schema-on-read capabilitiesschema-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 dateyesyesnoyesyes
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.nonononoyes infoin Aster File Store
Secondary indexesyesyesyesnoyes
SQL infoSupport of SQLSQL-like query languageyesnoSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
Native API
JDBCRESTful HTTP APIJDBC
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
Java
Python
R
Scala
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyesuser defined functionsuser defined functionsnoR packages
Triggersyesnononono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardinghorizontal partitioning infoconsistent hashingyes, utilizing Spark CoreSharding
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
Source-replica replication
Multi-source replicationnoneyes 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 SparkHadoop integrationnoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDnoACID
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.yesyesnonono
User concepts infoAccess controlyesAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyIdentity management, authentication, and access controlnofine grained access rights according to SQL-standard

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More resources
Apache IoTDBApache PhoenixFauna infopreviously named FaunaDBSpark SQLTeradata Aster
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