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DBMS > Apache Impala vs. Apache Pinot vs. Pinecone

System Properties Comparison Apache Impala vs. Apache Pinot vs. Pinecone

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache Pinot  Xexclude from comparisonPinecone  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyA managed, cloud-native vector database
Primary database modelRelational DBMSRelational DBMSVector DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score10.63
Rank#40  Overall
#24  Relational DBMS
Score0.35
Rank#274  Overall
#128  Relational DBMS
Score3.02
Rank#87  Overall
#3  Vector DBMS
Websiteimpala.apache.orgpinot.apache.orgwww.pinecone.io
Technical documentationimpala.apache.org/­impala-docs.htmldocs.pinot.apache.orgdocs.pinecone.io/­docs/­overview
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software Foundation and contributorsPinecone Systems, Inc
Initial release201320152019
Current release4.1.0, June 20221.0.0, September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java
Server operating systemsLinuxAll OS with a Java JDK11 or higherhosted
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyesString, Number, Boolean
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.nono
Secondary indexesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageno
APIs and other access methodsJDBC
ODBC
JDBCRESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCGo
Java
Python
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduce
Triggersno
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberos

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More resources
Apache ImpalaApache PinotPinecone
DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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