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DBMS > Pinecone vs. Riak KV vs. Vertica vs. VictoriaMetrics

System Properties Comparison Pinecone vs. Riak KV vs. Vertica vs. VictoriaMetrics

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Editorial information provided by DB-Engines
NamePinecone  Xexclude from comparisonRiak KV  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
DescriptionA managed, cloud-native vector databaseDistributed, fault tolerant key-value storeCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.A fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelVector DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexesRelational DBMS infoColumn orientedTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.16
Rank#95  Overall
#2  Vector DBMS
Score4.10
Rank#82  Overall
#9  Key-value stores
Score10.68
Rank#43  Overall
#27  Relational DBMS
Score1.32
Rank#162  Overall
#14  Time Series DBMS
Websitewww.pinecone.iowww.vertica.comvictoriametrics.com
Technical documentationdocs.pinecone.io/­docs/­overviewwww.tiot.jp/­riak-docs/­riak/­kv/­latestvertica.com/­documentationdocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperPinecone Systems, IncOpenSource, formerly Basho TechnologiesOpenText infopreviously Micro Focus and Hewlett PackardVictoriaMetrics
Initial release2019200920052018
Current release3.2.0, December 202212.0.3, January 2023v1.91, May 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache version 2, commercial enterprise editioncommercial infoLimited community edition freeOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containersno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageErlangC++Go
Server operating systemshostedLinux
OS X
LinuxFreeBSD
Linux
macOS
OpenBSD
Data schemeschema-freeYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateString, Number, Booleannoyes
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 indexesrestrictedNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnonoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.no
APIs and other access methodsRESTful HTTP APIHTTP API
Native Erlang Interface
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
Supported programming languagesPythonC infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresErlangyes, PostgreSQL PL/pgSQL, with minor differencesno
Triggersyes infopre-commit hooks and post-commit hooksyes, called Custom Alertsno
Partitioning methods infoMethods for storing different data on different nodesSharding infono "single point of failure"horizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.Synchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityno infolinks between data sets can be storedyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.nonono
User concepts infoAccess controlyes, using Riak Securityfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
PineconeRiak KVVertica infoOpenText™ Vertica™VictoriaMetrics
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more
Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
» more

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More resources
PineconeRiak KVVertica infoOpenText™ Vertica™VictoriaMetrics
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