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DBMS > Apache IoTDB vs. Drizzle vs. FoundationDB vs. GigaSpaces

System Properties Comparison Apache IoTDB vs. Drizzle vs. FoundationDB vs. GigaSpaces

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDrizzle  Xexclude from comparisonFoundationDB  Xexclude from comparisonGigaSpaces  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the 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 FlinkMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Ordered key-value store. Core features are complimented by layers.High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions
Primary database modelTime Series DBMSRelational DBMSDocument store infosupported via specific layer
Key-value store
Relational DBMS infosupported via specific SQL-layer
Document store
Object oriented DBMS infoValues are user defined objects
Secondary database modelsGraph DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score1.06
Rank#185  Overall
#31  Document stores
#28  Key-value stores
#85  Relational DBMS
Score1.03
Rank#188  Overall
#32  Document stores
#6  Object oriented DBMS
Websiteiotdb.apache.orggithub.com/­apple/­foundationdbwww.gigaspaces.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlapple.github.io/­foundationdbdocs.gigaspaces.com/­latest/­landing.html
DeveloperApache Software FoundationDrizzle project, originally started by Brian AkerFoundationDBGigaspaces Technologies
Initial release2018200820132000
Current release1.1.0, April 20237.2.4, September 20126.2.28, November 202015.5, September 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoGNU GPLOpen Source infoApache 2.0Open Source infoApache Version 2; Commercial licenses available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++Java, C++, .Net
Server operating systemsAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
Linux
OS X
Windows
Linux
macOS
Solaris
Windows
Data schemeyesyesschema-free infosome layers support schemasschema-free
Typing infopredefined data types such as float or dateyesyesno infosome layers support typingyes
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.nono infoXML can be used for describing objects metadata
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL-like query languageyes infowith proprietary extensionssupported in specific SQL layer onlySQL-99 for query and DML statements
APIs and other access methodsJDBC
Native API
JDBCGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Java
PHP
.Net
C
C++
Go
Java
JavaScript infoNode.js
PHP
Python
Ruby
Swift
.Net
C++
Java
Python
Scala
Server-side scripts infoStored proceduresyesnoin SQL-layer onlyyes
Triggersyesno infohooks for callbacks inside the server can be used.noyes, event driven architecture
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingSharding
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
yesMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonoyes infoMap-Reduce pattern can be built with XAP task executors
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Linearizable consistencyImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANY
Foreign keys infoReferential integritynoyesin SQL-layer onlyno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID
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.yesyes
User concepts infoAccess controlyesPluggable authentication mechanisms infoe.g. LDAP, HTTPnoRole-based access control

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More resources
Apache IoTDBDrizzleFoundationDBGigaSpaces
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