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DBMS > Badger vs. EsgynDB vs. Google Cloud Datastore vs. IBM Db2 warehouse vs. Riak KV

System Properties Comparison Badger vs. EsgynDB vs. Google Cloud Datastore vs. IBM Db2 warehouse vs. Riak KV

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonIBM Db2 warehouse infoformerly named IBM dashDB  Xexclude from comparisonRiak KV  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Enterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformCloud-based data warehousing serviceDistributed, fault tolerant key-value store
Primary database modelKey-value storeRelational DBMSDocument storeRelational DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexes
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score1.30
Rank#164  Overall
#75  Relational DBMS
Score4.10
Rank#82  Overall
#9  Key-value stores
Websitegithub.com/­dgraph-io/­badgerwww.esgyn.cncloud.google.com/­datastorewww.ibm.com/­products/­db2/­warehouse
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgercloud.google.com/­datastore/­docswww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperDGraph LabsEsgynGoogleIBMOpenSource, formerly Basho Technologies
Initial release20172015200820142009
Current release3.2.0, December 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialcommercialOpen Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC++, JavaErlang
Server operating systemsBSD
Linux
OS X
Solaris
Windows
LinuxhostedhostedLinux
OS X
Data schemeschema-freeyesschema-freeyesschema-free
Typing infopredefined data types such as float or datenoyesyes, details hereyesno
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 infoImport/export of XML data possibleno
Secondary indexesnoyesyesyesrestricted
SQL infoSupport of SQLnoyesSQL-like query language (GQL)yesno
APIs and other access methodsADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
.NET Client API
JDBC
ODBC
OLE DB
HTTP API
Native Erlang Interface
Supported programming languagesGoAll languages supporting JDBC/ODBC/ADO.Net.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresnoJava Stored Proceduresusing Google App EnginePL/SQL, SQL PLErlang
TriggersnonoCallbacks using the Google Apps Engineyesyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingShardingSharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication between multi datacentersMulti-source replication using Paxosyesselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud Dataflownoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesyes infovia ReferenceProperties or Ancestor pathsyesno infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardyes, using Riak Security

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More resources
BadgerEsgynDBGoogle Cloud DatastoreIBM Db2 warehouse infoformerly named IBM dashDBRiak KV
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