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DBMS > Badger vs. Datomic vs. Google Cloud Bigtable vs. RethinkDB

System Properties Comparison Badger vs. Datomic vs. Google Cloud Bigtable vs. RethinkDB

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonDatomic  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonRethinkDB  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.DBMS for the Web with a mechanism to push updated query results to applications in realtime.
Primary database modelKey-value storeRelational DBMSKey-value store
Wide column store
Document store
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.12
Rank#306  Overall
#46  Key-value stores
Score1.70
Rank#138  Overall
#68  Relational DBMS
Score4.26
Rank#86  Overall
#14  Key-value stores
#6  Wide column stores
Score3.09
Rank#99  Overall
#19  Document stores
Websitegithub.com/­dgraph-io/­badgerwww.datomic.comcloud.google.com/­bigtablerethinkdb.com
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.datomic.comcloud.google.com/­bigtable/­docsrethinkdb.com/­docs
DeveloperDGraph LabsCognitectGoogleThe Linux Foundation infosince July 2017
Initial release2017201220152009
Current release1.0.6362, January 20222.4.1, August 2020
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial infolimited edition freecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoJava, ClojureC++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
All OS with a Java VMhostedLinux
OS X
Windows
Data schemeschema-freeyesschema-freeschema-free
Typing infopredefined data types such as float or datenoyesnoyes infostring, binary, float, bool, date, geometry
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 indexesnoyesnoyes
SQL infoSupport of SQLnononono
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Supported programming languagesGoClojure
Java
C#
C++
Go
Java
JavaScript (Node.js)
Python
C infocommunity-supported driver
C# infocommunity-supported driver
C++ infocommunity-supported driver
Clojure infocommunity-supported driver
Dart infocommunity-supported driver
Erlang infocommunity-supported driver
Go infocommunity-supported driver
Haskell infocommunity-supported driver
Java infoofficial driver
JavaScript (Node.js) infoofficial driver
Lisp infocommunity-supported driver
Lua infocommunity-supported driver
Objective-C infocommunity-supported driver
Perl infocommunity-supported driver
PHP infocommunity-supported driver
Python infoofficial driver
Ruby infoofficial driver
Scala infocommunity-supported driver
Server-side scripts infoStored proceduresnoyes infoTransaction Functionsno
TriggersnoBy using transaction functionsnoClient-side triggers through changefeeds
Partitioning methods infoMethods for storing different data on different nodesnonenone infoBut extensive use of caching in the application peersShardingSharding inforange based
Replication methods infoMethods for redundantly storing data on multiple nodesnonenone infoBut extensive use of caching in the application peersInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDAtomic single-row operationsAtomic single-document operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoMVCC based
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and developmentnono
User concepts infoAccess controlnonoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes infousers and table-level permissions

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More resources
BadgerDatomicGoogle Cloud BigtableRethinkDB
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