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DBMS > EsgynDB vs. Google Cloud Bigtable vs. HBase vs. IRONdb

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. HBase vs. IRONdb

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Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHBase  Xexclude from comparisonIRONdb  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Wide-column store based on Apache Hadoop and on concepts of BigTableA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity
Primary database modelRelational DBMSKey-value store
Wide column store
Wide column storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Websitewww.esgyn.cncloud.google.com/­bigtablehbase.apache.orgwww.circonus.com/solutions/time-series-database/
Technical documentationcloud.google.com/­bigtable/­docshbase.apache.org/­book.htmldocs.circonus.com/irondb/category/getting-started
DeveloperEsgynGoogleApache Software Foundation infoApache top-level project, originally developed by PowersetCirconus LLC.
Initial release2015201520082017
Current release2.3.4, January 2021V0.10.20, January 2018
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache version 2commercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++, JavaJavaC and C++
Server operating systemsLinuxhostedLinux
Unix
Windows infousing Cygwin
Linux
Data schemeyesschema-freeschema-free, schema definition possibleschema-free
Typing infopredefined data types such as float or dateyesnooptions to bring your own types, AVROyes infotext, numeric, histograms
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 indexesyesnonono
SQL infoSupport of SQLyesnonoSQL-like query language (Circonus Analytics Query Language: CAQL)
APIs and other access methodsADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Java API
RESTful HTTP API
Thrift
HTTP API
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Server-side scripts infoStored proceduresJava Stored Proceduresnoyes infoCoprocessors in Javayes, in Lua
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingAutomatic, metric affinity per node
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesMulti-source replication
Source-replica replication
configurable replication factor, datacenter aware
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual ConsistencyImmediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsSingle row ACID (across millions of columns)no
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.nonoyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACno

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More resources
EsgynDBGoogle Cloud BigtableHBaseIRONdb
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