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DBMS > Badger vs. Blazegraph vs. Datomic vs. Google Cloud Datastore vs. Quasardb

System Properties Comparison Badger vs. Blazegraph vs. Datomic vs. Google Cloud Datastore vs. Quasardb

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonBlazegraph  Xexclude from comparisonDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonQuasardb  Xexclude from comparison
Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformDistributed, high-performance timeseries database
Primary database modelKey-value storeGraph DBMS
RDF store
Relational DBMSDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#320  Overall
#47  Key-value stores
Score0.81
Rank#213  Overall
#19  Graph DBMS
#8  RDF stores
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score0.21
Rank#322  Overall
#29  Time Series DBMS
Websitegithub.com/­dgraph-io/­badgerblazegraph.comwww.datomic.comcloud.google.com/­datastorequasar.ai
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerwiki.blazegraph.comdocs.datomic.comcloud.google.com/­datastore/­docsdoc.quasar.ai/­master
DeveloperDGraph LabsBlazegraphCognitectGooglequasardb
Initial release20172006201220082009
Current release2.1.5, March 20191.0.6735, June 20233.14.1, January 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoextended commercial license availablecommercial infolimited edition freecommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licenses
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 languageGoJavaJava, ClojureC++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
All OS with a Java VMhostedBSD
Linux
OS X
Windows
Data schemeschema-freeschema-freeyesschema-freeschema-free
Typing infopredefined data types such as float or datenoyes infoRDF literal typesyesyes, details hereyes infointeger and binary
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 indexesnoyesyesyesyes infowith tags
SQL infoSupport of SQLnoSPARQL is used as query languagenoSQL-like query language (GQL)SQL-like query language
APIs and other access methodsJava API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3
RESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP API
Supported programming languagesGo.Net
C
C++
Java
JavaScript
PHP
Python
Ruby
Clojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Server-side scripts infoStored proceduresnoyesyes infoTransaction Functionsusing Google App Engineno
TriggersnonoBy using transaction functionsCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingnone infoBut extensive use of caching in the application peersShardingSharding infoconsistent hashing
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesnone infoBut extensive use of caching in the application peersMulti-source replication using PaxosSource-replica replication with selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes infousing Google Cloud Dataflowwith Hadoop integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate Consistency or Eventual Consistency depending on configurationImmediate 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 Consistency
Foreign keys infoReferential integritynoyes infoRelationships in Graphsnoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes infoby using LevelDB
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 developmentnoyes infoTransient mode
User concepts infoAccess controlnoSecurity and Authentication via Web Application Container (Tomcat, Jetty)noAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Cryptographically strong user authentication and audit trail

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More resources
BadgerBlazegraphDatomicGoogle Cloud DatastoreQuasardb
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