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DBMS > Apache Pinot vs. Blazegraph vs. Google Cloud Datastore vs. Solr

System Properties Comparison Apache Pinot vs. Blazegraph vs. Google Cloud Datastore vs. Solr

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonBlazegraph  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSolr  Xexclude from comparison
Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyHigh-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSGraph DBMS
RDF store
Document storeSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
Score0.81
Rank#213  Overall
#19  Graph DBMS
#8  RDF stores
Score4.36
Rank#72  Overall
#12  Document stores
Score41.02
Rank#24  Overall
#3  Search engines
Websitepinot.apache.orgblazegraph.comcloud.google.com/­datastoresolr.apache.org
Technical documentationdocs.pinot.apache.orgwiki.blazegraph.comcloud.google.com/­datastore/­docssolr.apache.org/­resources.html
DeveloperApache Software Foundation and contributorsBlazegraphGoogleApache Software Foundation
Initial release2015200620082006
Current release1.0.0, September 20232.1.5, March 20199.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoextended commercial license availablecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJava
Server operating systemsAll OS with a Java JDK11 or higherLinux
OS X
Windows
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-freeschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyes infoRDF literal typesyes, details hereyes 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.noyes
Secondary indexesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageSPARQL is used as query languageSQL-like query language (GQL)Solr Parallel SQL Interface
APIs and other access methodsJDBCJava API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP/JSON API
Supported programming languagesGo
Java
Python
.Net
C
C++
Java
JavaScript
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyesusing Google App EngineJava plugins
TriggersnoCallbacks using the Google Apps Engineyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using Paxosyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate 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.Eventual Consistency
Foreign keys infoReferential integrityyes infoRelationships in Graphsyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlSecurity and Authentication via Web Application Container (Tomcat, Jetty)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes

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More resources
Apache PinotBlazegraphGoogle Cloud DatastoreSolr
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