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DBMS > Drizzle vs. HarperDB vs. KeyDB vs. Kinetica vs. Spark SQL

System Properties Comparison Drizzle vs. HarperDB vs. KeyDB vs. Kinetica vs. Spark SQL

Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonHarperDB  Xexclude from comparisonKeyDB  Xexclude from comparisonKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocolsFully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument storeKey-value storeRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.55
Rank#248  Overall
#38  Document stores
Score0.71
Rank#226  Overall
#33  Key-value stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.harperdb.iogithub.com/­Snapchat/­KeyDB
keydb.dev
www.kinetica.comspark.apache.org/­sql
Technical documentationdocs.harperdb.io/­docsdocs.keydb.devdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDrizzle project, originally started by Brian AkerHarperDBEQ Alpha Technology Ltd.KineticaApache Software Foundation
Initial release20082017201920122014
Current release7.2.4, September 20123.1, August 20217.1, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercial infofree community edition availableOpen Source infoBSD-3commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Node.jsC++C, C++Scala
Server operating systemsFreeBSD
Linux
OS X
Linux
OS X
LinuxLinuxLinux
OS X
Windows
Data schemeyesdynamic schemaschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes infoJSON data typespartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexesyesyes
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 indexesyesyesyes infoby using the Redis Search moduleyesno
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like data manipulation statementsnoSQL-like DML and DDL statementsSQL-like DML and DDL statements
APIs and other access methodsJDBCJDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
Proprietary protocol infoRESP - REdis Serialization ProtocoJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesC
C++
Java
PHP
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
C++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoCustom Functions infosince release 3.1Luauser defined functionsno
Triggersno infohooks for callbacks inside the server can be used.nonoyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingA table resides as a whole on one (or more) nodes in a clusterShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yes infothe nodes on which a table resides can be definedMulti-source replication
Source-replica replication
Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Strong eventual consistency with CRDTs
Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesnonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic execution of specific operationsOptimistic locking, atomic execution of commands blocks and scriptsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes, using LMDByes infoConfigurable mechanisms for persistency via snapshots and/or operations logsyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users and rolessimple password-based access control and ACLAccess rights for users and roles on table levelno

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More resources
DrizzleHarperDBKeyDBKineticaSpark SQL
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