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DBMS > Apache Druid vs. Kinetica vs. NSDb vs. Pinecone

System Properties Comparison Apache Druid vs. Kinetica vs. NSDb vs. Pinecone

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonKinetica  Xexclude from comparisonNSDb  Xexclude from comparisonPinecone  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataFully vectorized database across both GPUs and CPUsScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesA managed, cloud-native vector database
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSTime Series DBMSVector DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score3.23
Rank#92  Overall
#3  Vector DBMS
Websitedruid.apache.orgwww.kinetica.comnsdb.iowww.pinecone.io
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.kinetica.comnsdb.io/­Architecturedocs.pinecone.io/­docs/­overview
DeveloperApache Software Foundation and contributorsKineticaPinecone Systems, Inc
Initial release2012201220172019
Current release29.0.1, April 20247.1, August 2021
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++Java, Scala
Server operating systemsLinux
OS X
Unix
LinuxLinux
macOS
hosted
Data schemeyes infoschema-less columns are supportedyes
Typing infopredefined data types such as float or dateyesyesyes: int, bigint, decimal, stringString, 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.nononono
Secondary indexesyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL for queryingSQL-like DML and DDL statementsSQL-like query languageno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
gRPC
HTTP REST
WebSocket
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
Java
Scala
Python
Server-side scripts infoStored proceduresnouser defined functionsno
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAMno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users and roles on table level

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More resources
Apache DruidKineticaNSDbPinecone
DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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