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DBMS > Apache Phoenix vs. GeoSpock vs. Google BigQuery vs. Google Cloud Bigtable vs. Milvus

System Properties Comparison Apache Phoenix vs. GeoSpock vs. Google BigQuery vs. Google Cloud Bigtable vs. Milvus

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGeoSpock  Xexclude from comparisonGoogle BigQuery  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMilvus  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseSpatial and temporal data processing engine for extreme data scaleLarge scale data warehouse service with append-only tablesGoogle'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 searches
Primary database modelRelational DBMSRelational DBMSRelational DBMSKey-value store
Wide column store
Vector DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.10
Rank#131  Overall
#63  Relational DBMS
Score62.67
Rank#19  Overall
#13  Relational DBMS
Score3.86
Rank#90  Overall
#13  Key-value stores
#7  Wide column stores
Score1.70
Rank#150  Overall
#5  Vector DBMS
Websitephoenix.apache.orggeospock.comcloud.google.com/­bigquerycloud.google.com/­bigtablemilvus.io
Technical documentationphoenix.apache.orgcloud.google.com/­bigquery/­docscloud.google.com/­bigtable/­docsmilvus.io/­docs/­overview.md
DeveloperApache Software FoundationGeoSpockGoogleGoogle
Initial release2014201020152019
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20192.0, September 20192.3.4, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesyesyesno
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 languageJavaJava, JavascriptC++, Go
Server operating systemsLinux
Unix
Windows
hostedhostedhostedLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesnoVector, Numeric and String
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.nonononono
Secondary indexesyestemporal, categoricalnonono
SQL infoSupport of SQLyesANSI SQL for query only (using Presto)yesnono
APIs and other access methodsJDBCJDBCRESTful HTTP/JSON APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functionsnouser defined functions infoin JavaScriptnono
Triggersnonononono
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic shardingnoneShardingSharding
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 zones
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate 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 integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono infoSince BigQuery is designed for querying dataAtomic single-row operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnononoyes
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 tableAccess privileges (owner, writer, reader) on dataset, table or view level infoGoogle Cloud Identity & Access Management (IAM)Access 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 PhoenixGeoSpockGoogle BigQueryGoogle Cloud BigtableMilvus
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 PhoenixGeoSpockGoogle BigQueryGoogle Cloud BigtableMilvus
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