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DBMS > BigObject vs. Google Cloud Datastore vs. HBase vs. Solr

System Properties Comparison BigObject vs. Google Cloud Datastore vs. HBase vs. Solr

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Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHBase  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformWide-column store based on Apache Hadoop and on concepts of BigTableA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedDocument storeWide column storeSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#333  Overall
#147  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Score42.91
Rank#24  Overall
#3  Search engines
Websitebigobject.iocloud.google.com/­datastorehbase.apache.orgsolr.apache.org
Technical documentationdocs.bigobject.iocloud.google.com/­datastore/­docshbase.apache.org/­book.htmlsolr.apache.org/­resources.html
DeveloperBigObject, Inc.GoogleApache Software Foundation infoApache top-level project, originally developed by PowersetApache Software Foundation
Initial release2015200820082006
Current release2.3.4, January 20219.6.0, April 2024
License infoCommercial or Open Sourcecommercial infofree community edition availablecommercialOpen Source infoApache version 2Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
hostedLinux
Unix
Windows infousing Cygwin
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-freeschema-free, schema definition possibleyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyes, details hereoptions to bring your own types, AVROyes 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.nononoyes
Secondary indexesyesyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query language (GQL)noSolr Parallel SQL Interface
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
Java API
RESTful HTTP/JSON API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresLuausing Google App Engineyes infoCoprocessors in JavaJava plugins
TriggersnoCallbacks using the Google Apps Engineyesyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication using PaxosMulti-source replication
Source-replica replication
yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowyesspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate 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 ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsSingle row ACID (across millions of columns)optimistic locking
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyes
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACyes

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More resources
BigObjectGoogle Cloud DatastoreHBaseSolr
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