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DBMS > Apache Druid vs. Hive vs. QuestDB vs. Vitess

System Properties Comparison Apache Druid vs. Hive vs. QuestDB vs. Vitess

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonHive  Xexclude from comparisonQuestDB  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality datadata warehouse software for querying and managing large distributed datasets, built on HadoopA high performance open source SQL database for time series dataScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSTime Series DBMSRelational DBMS
Secondary database modelsRelational DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score2.52
Rank#109  Overall
#9  Time Series DBMS
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websitedruid.apache.orghive.apache.orgquestdb.iovitess.io
Technical documentationdruid.apache.org/­docs/­latest/­designcwiki.apache.org/­confluence/­display/­Hive/­Homequestdb.io/­docsvitess.io/­docs
DeveloperApache Software Foundation and contributorsApache Software Foundation infoinitially developed by FacebookQuestDB Technology IncThe Linux Foundation, PlanetScale
Initial release2012201220142013
Current release29.0.1, April 20243.1.3, April 202215.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoApache Version 2Open Source infoApache 2.0Open Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaJava (Zero-GC), C++, RustGo
Server operating systemsLinux
OS X
Unix
All OS with a Java VMLinux
macOS
Windows
Docker
Linux
macOS
Data schemeyes infoschema-less columns are supportedyesyes infoschema-free via InfluxDB Line Protocolyes
Typing infopredefined data types such as float or dateyesyesyesyes
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 indexesyesyesnoyes
SQL infoSupport of SQLSQL for queryingSQL-like DML and DDL statementsSQL with time-series extensionsyes infowith proprietary extensions
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C++
Java
PHP
Python
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
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 infouser defined functions and integration of map-reducenoyes infoproprietary syntax
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardinghorizontal partitioning (by timestamps)Sharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesselectable replication factorSource-replica replication with eventual consistencyMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynononoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID for single-table writesACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infotable locks or row locks depending on storage engine
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.noyes infothrough memory mapped filesyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users, groups and rolesUsers with fine-grained authorization concept infono user groups or roles
More information provided by the system vendor
Apache DruidHiveQuestDBVitess
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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