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DBMS > Apache Phoenix vs. Google Cloud Bigtable vs. GridDB vs. XTDB

System Properties Comparison Apache Phoenix vs. Google Cloud Bigtable vs. GridDB vs. XTDB

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonGridDB  Xexclude from comparisonXTDB infoformerly named Crux  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Scalable in-memory time series database optimized for IoT and Big DataA general purpose database with bitemporal SQL and Datalog and graph queries
Primary database modelRelational DBMSKey-value store
Wide column store
Time Series DBMSDocument 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
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score0.11
Rank#343  Overall
#46  Document stores
Websitephoenix.apache.orgcloud.google.com/­bigtablegriddb.netgithub.com/­xtdb/­xtdb
www.xtdb.com
Technical documentationphoenix.apache.orgcloud.google.com/­bigtable/­docsdocs.griddb.netwww.xtdb.com/­docs
DeveloperApache Software FoundationGoogleToshiba CorporationJuxt Ltd.
Initial release2014201520132019
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20195.1, August 20221.19, September 2021
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 availableOpen Source infoMIT License
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Clojure
Server operating systemsLinux
Unix
Windows
hostedLinuxAll OS with a Java 8 (and higher) VM
Linux
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesnoyes infonumerical, string, blob, geometry, boolean, timestampyes, extensible-data-notation format
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 indexesyesnoyesyes
SQL infoSupport of SQLyesnoSQL92, SQL-like TQL (Toshiba Query Language)limited SQL, making use of Apache Calcite
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
HTTP REST
JDBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Clojure
Java
Server-side scripts infoStored proceduresuser defined functionsnonono
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Internal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replicationyes, each node contains all data
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyesConnector 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 ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate consistency within container, eventual consistency across containers
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACID at container levelACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users can be defined per database
More information provided by the system vendor
Apache PhoenixGoogle Cloud BigtableGridDBXTDB infoformerly named Crux
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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More resources
Apache PhoenixGoogle Cloud BigtableGridDBXTDB infoformerly named Crux
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