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DBMS > Google Cloud Bigtable vs. IRONdb vs. Quasardb vs. Vitess

System Properties Comparison Google Cloud Bigtable vs. IRONdb vs. Quasardb vs. Vitess

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonIRONdb  Xexclude from comparisonQuasardb  Xexclude from comparisonVitess  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityDistributed, high-performance timeseries databaseScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelKey-value store
Wide column store
Time Series DBMSTime Series DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.21
Rank#322  Overall
#29  Time Series DBMS
Score0.88
Rank#203  Overall
#95  Relational DBMS
Websitecloud.google.com/­bigtablewww.circonus.com/solutions/time-series-database/quasar.aivitess.io
Technical documentationcloud.google.com/­bigtable/­docsdocs.circonus.com/irondb/category/getting-starteddoc.quasar.ai/­mastervitess.io/­docs
DeveloperGoogleCirconus LLC.quasardbThe Linux Foundation, PlanetScale
Initial release2015201720092013
Current releaseV0.10.20, January 20183.14.1, January 202415.0.2, December 2022
License infoCommercial or Open Sourcecommercialcommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++C++Go
Server operating systemshostedLinuxBSD
Linux
OS X
Windows
Docker
Linux
macOS
Data schemeschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or datenoyes infotext, numeric, histogramsyes infointeger and binaryyes
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.nonono
Secondary indexesnonoyes infowith tagsyes
SQL infoSupport of SQLnoSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like query languageyes infowith proprietary extensions
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HTTP APIHTTP APIADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnoyes, in Luanoyes infoproprietary syntax
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic, metric affinity per nodeSharding infoconsistent hashingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesconfigurable replication factor, datacenter awareSource-replica replication with selectable replication factorMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnowith Hadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate consistency per node, eventual consistency across nodesImmediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynononoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsnoACIDACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyes infoby using LevelDByes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infoTransient modeyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noCryptographically strong user authentication and audit trailUsers with fine-grained authorization concept infono user groups or roles

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More resources
Google Cloud BigtableIRONdbQuasardbVitess
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