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DBMS > GridDB vs. Qdrant vs. Vertica

System Properties Comparison GridDB vs. Qdrant vs. Vertica

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonQdrant  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionScalable in-memory time series database optimized for IoT and Big DataA high-performance vector database with neural network or semantic-based matchingCloud 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.
Primary database modelTime Series DBMSVector DBMSRelational DBMS infoColumn oriented
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score1.28
Rank#167  Overall
#6  Vector DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitegriddb.netgithub.com/­qdrant/­qdrant
qdrant.tech
www.vertica.com
Technical documentationdocs.griddb.netqdrant.tech/­documentationvertica.com/­documentation
DeveloperToshiba CorporationQdrantOpenText infopreviously Micro Focus and Hewlett Packard
Initial release201320212005
Current release5.1, August 202212.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++RustC++
Server operating systemsLinuxDocker
Linux
macOS
Windows
Linux
Data schemeyesschema-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 dateyes infonumerical, string, blob, geometry, boolean, timestampNumbers, Strings, Geo, Booleanyes
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.nonono
Secondary indexesyesyes infoKeywords, numberic ranges, geo, full-textNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)noFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
gRPC
OpenAPI 3.0
RESTful HTTP/JSON API infoOpenAPI 3.0
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
Python
Rust
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationCollection-level replicationMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersEventual Consistency, tunable consistencyImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelACID
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.yesyesno
User concepts infoAccess controlAccess rights for users can be defined per databaseKey-based authenticationfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
GridDBQdrantVertica infoOpenText™ Vertica™
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more
Cost-based models and subscription-based models are both available. One license is...
» more

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More resources
GridDBQdrantVertica infoOpenText™ Vertica™
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