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DBMS > 4D vs. Apache IoTDB vs. Google Cloud Bigtable vs. PostgreSQL

System Properties Comparison 4D vs. Apache IoTDB vs. Google Cloud Bigtable vs. PostgreSQL

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Editorial information provided by DB-Engines
Name4D infoformer name: 4th Dimension  Xexclude from comparisonApache IoTDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonPostgreSQL  Xexclude from comparison
DescriptionApplication development environment with integrated database management systemAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Widely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL
Primary database modelRelational DBMSTime Series DBMSKey-value store
Wide column store
Relational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.
Secondary database modelsDocument store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.80
Rank#111  Overall
#55  Relational DBMS
Score1.19
Rank#183  Overall
#15  Time Series DBMS
Score3.86
Rank#90  Overall
#13  Key-value stores
#7  Wide column stores
Score634.91
Rank#4  Overall
#4  Relational DBMS
Websitewww.4d.comiotdb.apache.orgcloud.google.com/­bigtablewww.postgresql.org
Technical documentationdeveloper.4d.comiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcloud.google.com/­bigtable/­docswww.postgresql.org/­docs
Developer4D, IncApache Software FoundationGooglePostgreSQL Global Development Group infowww.postgresql.org/­developer
Initial release1984201820151989 info1989: Postgres, 1996: PostgreSQL
Current releasev20, April 20231.1.0, April 202316.2, February 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialOpen Source infoBSD
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Aiven for PostgreSQL: Fully managed PostgreSQL for developers with 70+ extensions and flexible orchestration tools.
Implementation languageJavaC
Server operating systemsOS X
Windows
All OS with a Java VM (>= 1.8)hostedFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes
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.yesnonoyes infospecific XML-type available, but no XML query functionality.
Secondary indexesyesyesnoyes
SQL infoSupport of SQLyes infoclose to SQL 92SQL-like query languagenoyes infostandard with numerous extensions
APIs and other access methodsODBC
RESTful HTTP API infoby using 4D Mobile
SOAP webservices
JDBC
Native API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languages4D proprietary IDE
PHP
C
C#
C++
Go
Java
Python
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresyesyesnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.
Triggersyesyesnoyes
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardingpartitioning by range, list and (since PostgreSQL 11) by hash
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replication infoother methods possible by using 3rd party extensions
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoIntegration with Hadoop and Sparkyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoAtomic single-row operationsACID
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.noyesnono
User concepts infoAccess controlUsers and groupsyesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standard

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