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DBMS > Apache IoTDB vs. Blazegraph vs. RocksDB vs. Spark SQL vs. Transbase

System Properties Comparison Apache IoTDB vs. Blazegraph vs. RocksDB vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonBlazegraph  Xexclude from comparisonRocksDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTransbase  Xexclude from comparison
Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.
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 FlinkHigh-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.Embeddable persistent key-value store optimized for fast storage (flash and RAM)Spark SQL is a component on top of 'Spark Core' for structured data processingA resource-optimized, high-performance, universally applicable RDBMS
Primary database modelTime Series DBMSGraph DBMS
RDF store
Key-value storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.75
Rank#219  Overall
#19  Graph DBMS
#8  RDF stores
Score3.65
Rank#85  Overall
#11  Key-value stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.11
Rank#341  Overall
#150  Relational DBMS
Websiteiotdb.apache.orgblazegraph.comrocksdb.orgspark.apache.org/­sqlwww.transaction.de/­en/­products/­transbase.html
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwiki.blazegraph.comgithub.com/­facebook/­rocksdb/­wikispark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.transaction.de/­en/­products/­transbase/­features.html
DeveloperApache Software FoundationBlazegraphFacebook, Inc.Apache Software FoundationTransaction Software GmbH
Initial release20182006201320141987
Current release1.1.0, April 20232.1.5, March 20198.11.4, April 20243.5.0 ( 2.13), September 2023Transbase 8.3, 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoextended commercial license availableOpen Source infoBSDOpen Source infoApache 2.0commercial infofree development license
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++ScalaC and C++
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Windows
LinuxLinux
OS X
Windows
FreeBSD
Linux
macOS
Solaris
Windows
Data schemeyesschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes infoRDF literal typesnoyesyes
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.nononono
Secondary indexesyesyesnonoyes
SQL infoSupport of SQLSQL-like query languageSPARQL is used as query languagenoSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
Native API
Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3
C++ API
Java API
JDBC
ODBC
ADO.NET
JDBC
ODBC
Proprietary native API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C++
Java
JavaScript
PHP
Python
Ruby
C
C++
Go
Java
Perl
Python
Ruby
Java
Python
R
Scala
C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
Server-side scripts infoStored proceduresyesyesnonoyes
Triggersyesnonoyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardinghorizontal partitioningyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyesyesnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyes infoRelationships in Graphsnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDyesnoyes
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.yesyesnono
User concepts infoAccess controlyesSecurity and Authentication via Web Application Container (Tomcat, Jetty)nonofine grained access rights according to SQL-standard

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Apache IoTDBBlazegraphRocksDBSpark SQLTransbase
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