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DBMS > Apache Pinot vs. Hive vs. HugeGraph vs. Neo4j

System Properties Comparison Apache Pinot vs. Hive vs. HugeGraph vs. Neo4j

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonHive  Xexclude from comparisonHugeGraph  Xexclude from comparisonNeo4j  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencydata warehouse software for querying and managing large distributed datasets, built on HadoopA fast-speed and highly-scalable Graph DBMSScalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings
Primary database modelRelational DBMSRelational DBMSGraph DBMSGraph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.40
Rank#270  Overall
#125  Relational DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score44.46
Rank#23  Overall
#1  Graph DBMS
Websitepinot.apache.orghive.apache.orggithub.com/­hugegraph
hugegraph.apache.org
neo4j.com
Technical documentationdocs.pinot.apache.orgcwiki.apache.org/­confluence/­display/­Hive/­Homehugegraph.apache.org/­docsneo4j.com/­docs
DeveloperApache Software Foundation and contributorsApache Software Foundation infoinitially developed by FacebookBaiduNeo4j, Inc.
Initial release2015201220182007
Current release1.0.0, September 20233.1.3, April 20220.95.20, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoGPL version3, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.
Implementation languageJavaJavaJavaJava, Scala
Server operating systemsAll OS with a Java JDK11 or higherAll OS with a Java VMLinux
macOS
Unix
Linux infoCan also be used server-less as embedded Java database.
OS X
Solaris
Windows
Data schemeyesyesyesschema-free and schema-optional
Typing infopredefined data types such as float or dateyesyesyesyes
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.no
Secondary indexesyesyes infoalso supports composite index and range indexyes infopluggable indexing subsystem, by default Apache Lucene
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsnono
APIs and other access methodsJDBCJDBC
ODBC
Thrift
Java API
RESTful HTTP API
TinkerPop Gremlin
Bolt protocol
Cypher query language
Java API
Neo4j-OGM infoObject Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3
Supported programming languagesGo
Java
Python
C++
Java
PHP
Python
Groovy
Java
Python
.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceasynchronous Gremlin script jobsyes infoUser defined Procedures and Functions
Triggersnonoyes infovia event handler
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseyes using Neo4j Fabric
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes infodepending on used storage backend, e.g. Cassandra and HBaseCausal Clustering using Raft protocol infoavailable in in Enterprise Version only
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducevia hugegraph-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual ConsistencyCausal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode
Foreign keys infoReferential integritynoyes infoedges in graphyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
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.yes
User concepts infoAccess controlAccess rights for users, groups and rolesUsers, roles and permissionsUsers, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)
More information provided by the system vendor
Apache PinotHiveHugeGraphNeo4j
Specific characteristicsNeo4j delivers graph technology that has been battle tested for performance and scale...
» more
Competitive advantagesNeo4j is the market leader, graph database category creator, and the most widely...
» more
Typical application scenariosReal-Time Recommendations Master Data Management Identity and Access Management Network...
» more
Key customersOver 800 commercial customers and over 4300 startups use Neo4j. Flagship customers...
» more
Market metricsNeo4j boasts the world's largest graph database ecosystem with more than 140 million...
» more
Licensing and pricing modelsGPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
» more
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