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DBMS > Dgraph vs. EsgynDB vs. Google Cloud Datastore vs. LokiJS vs. Solr

System Properties Comparison Dgraph vs. EsgynDB vs. Google Cloud Datastore vs. LokiJS vs. Solr

Editorial information provided by DB-Engines
NameDgraph  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonLokiJS  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionDistributed and scalable native Graph DBMSEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformIn-memory JavaScript DBMSA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelGraph DBMSRelational DBMSDocument storeDocument storeSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.53
Rank#152  Overall
#15  Graph DBMS
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score0.49
Rank#260  Overall
#42  Document stores
Score41.02
Rank#24  Overall
#3  Search engines
Websitedgraph.iowww.esgyn.cncloud.google.com/­datastoregithub.com/­techfort/­LokiJSsolr.apache.org
Technical documentationdgraph.io/­docscloud.google.com/­datastore/­docstechfort.github.io/­LokiJSsolr.apache.org/­resources.html
DeveloperDgraph Labs, Inc.EsgynGoogleApache Software Foundation
Initial release20162015200820142006
Current release9.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialOpen SourceOpen Source infoApache Version 2
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 languageGoC++, JavaJavaScriptJava
Server operating systemsLinux
OS X
Windows
Linuxhostedserver-less, requires a JavaScript environment (browser, Node.js)All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyesschema-freeschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyes, details herenoyes infosupports customizable data types and automatic typing
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.nonononoyes
Secondary indexesyesyesyesyes infovia viewsyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoyesSQL-like query language (GQL)noSolr Parallel SQL Interface
APIs and other access methodsGraphQL query language
gRPC (using protocol buffers) API
HTTP API
ADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JavaScript APIJava API
RESTful HTTP/JSON API
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.Net.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
JavaScript.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnoJava Stored Proceduresusing Google App EngineView functions in JavaScriptJava plugins
TriggersnonoCallbacks using the Google Apps Engineyesyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesyesShardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSynchronous replication via RaftMulti-source replication between multi datacentersMulti-source replication using Paxosnoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud Dataflowyesspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate 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.noneEventual Consistency
Foreign keys infoReferential integritynoyesyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno infoatomic operations within a single collection possibleoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoSerialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlno infoPlanned for future releasesfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noyes

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More resources
DgraphEsgynDBGoogle Cloud DatastoreLokiJSSolr
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