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DBMS > Apache Phoenix vs. Cloudflare Workers KV vs. GridDB vs. InfinityDB

System Properties Comparison Apache Phoenix vs. Cloudflare Workers KV vs. GridDB vs. InfinityDB

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonCloudflare Workers KV  Xexclude from comparisonGridDB  Xexclude from comparisonInfinityDB  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.Scalable in-memory time series database optimized for IoT and Big DataA Java embedded Key-Value Store which extends the Java Map interface
Primary database modelRelational DBMSKey-value storeTime Series DBMSKey-value store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.32
Rank#283  Overall
#40  Key-value stores
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score0.00
Rank#378  Overall
#57  Key-value stores
Websitephoenix.apache.orgwww.cloudflare.com/­developer-platform/­workers-kvgriddb.netboilerbay.com
Technical documentationphoenix.apache.orgdevelopers.cloudflare.com/­kv/­apidocs.griddb.netboilerbay.com/­infinitydb/­manual
DeveloperApache Software FoundationCloudflareToshiba CorporationBoiler Bay Inc.
Initial release2014201820132002
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20195.1, August 20224.0
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Java
Server operating systemsLinux
Unix
Windows
hostedLinuxAll OS with a Java VM
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgrade
Typing infopredefined data types such as float or dateyesnoyes infonumerical, string, blob, geometry, boolean, timestampyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arrays
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 indexesyesnoyesno infomanual creation possible, using inversions based on multi-value capability
SQL infoSupport of SQLyesnoSQL92, SQL-like TQL (Toshiba Query Language)no
APIs and other access methodsJDBCHTTP REST
Proprietary protocol
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Java
Server-side scripts infoStored proceduresuser defined functionsnonono
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency infoREAD-COMMITTED or SERIALIZED
Foreign keys infoReferential integritynononono infomanual creation possible, using inversions based on multi-value capability
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID at container levelACID infoOptimistic locking for transactions; no isolation for bulk loads
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.yesnoyesno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users can be defined per databaseno
More information provided by the system vendor
Apache PhoenixCloudflare Workers KVGridDBInfinityDB
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Apache PhoenixCloudflare Workers KVGridDBInfinityDB
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