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DBMS > Apache Druid vs. Vertica vs. Vitess

System Properties Comparison Apache Druid vs. Vertica vs. Vitess

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataCloud 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.Scalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMS infoColumn orientedRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Spatial 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
Score10.06
Rank#42  Overall
#26  Relational DBMS
Score0.88
Rank#203  Overall
#95  Relational DBMS
Websitedruid.apache.orgwww.vertica.comvitess.io
Technical documentationdruid.apache.org/­docs/­latest/­designvertica.com/­documentationvitess.io/­docs
DeveloperApache Software Foundation and contributorsOpenText infopreviously Micro Focus and Hewlett PackardThe Linux Foundation, PlanetScale
Initial release201220052013
Current release29.0.1, April 202412.0.3, January 202315.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoApache license v2commercial infoLimited community edition freeOpen Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenono 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 languageJavaC++Go
Server operating systemsLinux
OS X
Unix
LinuxDocker
Linux
macOS
Data schemeyes infoschema-less columns are supportedYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.yes
Typing infopredefined data types such as float or dateyesyesyes
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 indexesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.yes
SQL infoSupport of SQLSQL for queryingFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.yes infowith proprietary extensions
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnoyes, PostgreSQL PL/pgSQL, with minor differencesyes infoproprietary syntax
Triggersnoyes, called Custom Alertsyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedhorizontal partitioning, hierarchical partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynoyesyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyes infotable locks or row locks depending on storage engine
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.nonoyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hashUsers with fine-grained authorization concept infono user groups or roles
More information provided by the system vendor
Apache DruidVertica infoOpenText™ Vertica™Vitess
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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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...
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More resources
Apache DruidVertica infoOpenText™ Vertica™Vitess
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