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DBMS > Apache IoTDB vs. EsgynDB vs. EventStoreDB vs. Google Cloud Bigtable vs. Kdb

System Properties Comparison Apache IoTDB vs. EsgynDB vs. EventStoreDB vs. Google Cloud Bigtable vs. Kdb

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonEventStoreDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonKdb  Xexclude from comparison
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 FlinkEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionIndustrial-strength, open-source database solution built from the ground up for event sourcing.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.High performance Time Series DBMS
Primary database modelTime Series DBMSRelational DBMSEvent StoreKey-value store
Wide column store
Time Series DBMS
Vector DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score1.10
Rank#179  Overall
#1  Event Stores
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Websiteiotdb.apache.orgwww.esgyn.cnwww.eventstore.comcloud.google.com/­bigtablekx.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldevelopers.eventstore.comcloud.google.com/­bigtable/­docscode.kx.com
DeveloperApache Software FoundationEsgynEvent Store LimitedGoogleKx Systems, a division of First Derivatives plc
Initial release20182015201220152000 infokdb was released 2000, kdb+ in 2003
Current release1.1.0, April 202321.2, February 20213.6, May 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Sourcecommercialcommercial infofree 32-bit version
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++, Javaq
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxLinux
Windows
hostedLinux
OS X
Solaris
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.nononoyes
Secondary indexesyesyesnoyes infotable attribute 'grouped'
SQL infoSupport of SQLSQL-like query languageyesnoSQL-like query language (q)
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
Server-side scripts infoStored proceduresyesJava Stored Proceduresnouser defined functions
Triggersyesnonoyes infowith views
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardinghorizontal partitioning
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 between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesyesno infosimilar paradigm used for internal processing
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDAtomic single-row operationsno
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.yesnonoyes
User concepts infoAccess controlyesfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)rights management via user accounts
More information provided by the system vendor
Apache IoTDBEsgynDBEventStoreDBGoogle Cloud BigtableKdb
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsupon request
» more

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More resources
Apache IoTDBEsgynDBEventStoreDBGoogle Cloud BigtableKdb
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