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

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

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHazelcast  Xexclude from comparisonQuasardb  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 durabilityAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA widely adopted in-memory data gridDistributed, high-performance timeseries database
Primary database modelKey-value storeRelational DBMSDocument storeKey-value storeTime Series DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score5.97
Rank#57  Overall
#6  Key-value stores
Score0.14
Rank#332  Overall
#29  Time Series DBMS
Websitegithub.com/­dgraph-io/­badgerwww.datomic.comcloud.google.com/­datastorehazelcast.comquasar.ai
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.datomic.comcloud.google.com/­datastore/­docshazelcast.org/­imdg/­docsdoc.quasar.ai/­master
DeveloperDGraph LabsCognitectGoogleHazelcastquasardb
Initial release20172012200820082009
Current release1.0.6735, June 20235.3.6, November 20233.14.1, January 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial infolimited edition freecommercialOpen Source infoApache Version 2; commercial licenses availablecommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licenses
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoJava, ClojureJavaC++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
All OS with a Java VMhostedAll OS with a Java VMBSD
Linux
OS X
Windows
Data schemeschema-freeyesschema-freeschema-freeschema-free
Typing infopredefined data types such as float or datenoyesyes, details hereyesyes 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.nononoyes infothe object must implement a serialization strategyno
Secondary indexesnoyesyesyesyes infowith tags
SQL infoSupport of SQLnonoSQL-like query language (GQL)SQL-like query languageSQL-like query language
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JCache
JPA
Memcached protocol
RESTful HTTP API
HTTP API
Supported programming languagesGoClojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Server-side scripts infoStored proceduresnoyes infoTransaction Functionsusing Google App Engineyes infoEvent Listeners, Executor Servicesno
TriggersnoBy using transaction functionsCallbacks using the Google Apps Engineyes infoEventsno
Partitioning methods infoMethods for storing different data on different nodesnonenone infoBut extensive use of caching in the application peersShardingShardingSharding infoconsistent hashing
Replication methods infoMethods for redundantly storing data on multiple nodesnonenone infoBut extensive use of caching in the application peersMulti-source replication using Paxosyes infoReplicated MapSource-replica replication with selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflowyeswith Hadoop integration
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 Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsone or two-phase-commit; repeatable reads; read commitedACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes 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 developmentnoyesyes infoTransient mode
User concepts infoAccess controlnonoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access controlCryptographically strong user authentication and audit trail

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More resources
BadgerDatomicGoogle Cloud DatastoreHazelcastQuasardb
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