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

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

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMilvus  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.A DBMS designed for efficient storage of vector data and vector similarity searchesA general purpose database with bitemporal SQL and Datalog and graph queries
Primary database modelRelational DBMSKey-value store
Wide column store
Vector DBMSDocument store
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
Score2.31
Rank#113  Overall
#3  Vector DBMS
Score0.11
Rank#343  Overall
#46  Document stores
Websitephoenix.apache.orgcloud.google.com/­bigtablemilvus.iogithub.com/­xtdb/­xtdb
www.xtdb.com
Technical documentationphoenix.apache.orgcloud.google.com/­bigtable/­docsmilvus.io/­docs/­overview.mdwww.xtdb.com/­docs
DeveloperApache Software FoundationGoogleJuxt Ltd.
Initial release2014201520192019
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20192.3.4, January 20241.19, September 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0Open Source infoMIT License
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Zilliz Cloud – Cloud-native service for Milvus
Implementation languageJavaC++, GoClojure
Server operating systemsLinux
Unix
Windows
hostedLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
All OS with a Java 8 (and higher) VM
Linux
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-free
Typing infopredefined data types such as float or dateyesnoVector, Numeric and Stringyes, 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 indexesyesnonoyes
SQL infoSupport of SQLyesnonolimited SQL, making use of Apache Calcite
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
RESTful HTTP APIHTTP REST
JDBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Go
Java
JavaScript (Node.js)
Python
Clojure
Java
Server-side scripts infoStored proceduresuser defined functionsnonono
Triggersnononono
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 zonesyes, each node contains all data
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyesnono
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)Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsnoACID
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)Role based access control and fine grained access rights
More information provided by the system vendor
Apache PhoenixGoogle Cloud BigtableMilvusXTDB infoformerly named Crux
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosRAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Key customersMilvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricsAs of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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More resources
Apache PhoenixGoogle Cloud BigtableMilvusXTDB infoformerly named Crux
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Supercharge SQL on Your Data in Apache HBase with Apache Phoenix | Amazon Web Services
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