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DBMS > Apache Phoenix vs. Cloudflare Workers KV vs. Databend vs. Spark SQL

System Properties Comparison Apache Phoenix vs. Cloudflare Workers KV vs. Databend vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonCloudflare Workers KV  Xexclude from comparisonDatabend  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexitySpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.39
Rank#274  Overall
#39  Key-value stores
Score0.34
Rank#283  Overall
#130  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitephoenix.apache.orgwww.cloudflare.com/­developer-platform/­workers-kvgithub.com/­datafuselabs/­databend
www.databend.com
spark.apache.org/­sql
Technical documentationphoenix.apache.orgdevelopers.cloudflare.com/­kv/­apidocs.databend.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationCloudflareDatabend LabsApache Software Foundation
Initial release2014201820212014
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20191.0.59, April 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaRustScala
Server operating systemsLinux
Unix
Windows
hostedhosted
Linux
macOS
Linux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesyes
Typing infopredefined data types such as float or dateyesnoyesyes
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 indexesyesnonono
SQL infoSupport of SQLyesnoyesSQL-like DML and DDL statements
APIs and other access methodsJDBCHTTP REST
Proprietary protocol
CLI Client
JDBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala
Go
Java
JavaScript (Node.js)
Python
Rust
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnonono
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoyesno
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.yesnono
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyUsers with fine-grained authorization concept, user rolesno

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Apache PhoenixCloudflare Workers KVDatabendSpark SQL
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