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DBMS > Apache IoTDB vs. Apache Phoenix vs. Milvus vs. Sphinx

System Properties Comparison Apache IoTDB vs. Apache Phoenix vs. Milvus vs. Sphinx

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonMilvus  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA scale-out RDBMS with evolutionary schema built on Apache HBaseA DBMS designed for efficient storage of vector data and vector similarity searchesOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMSVector DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score2.78
Rank#103  Overall
#4  Vector DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websiteiotdb.apache.orgphoenix.apache.orgmilvus.iosphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlphoenix.apache.orgmilvus.io/­docs/­overview.mdsphinxsearch.com/­docs
DeveloperApache Software FoundationApache Software FoundationSphinx Technologies Inc.
Initial release2018201420192001
Current release1.1.0, April 20235.0-HBase2, July 2018 and 4.15-HBase1, December 20192.4.4, May 20243.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenononono
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 languageJavaJavaC++, GoC++
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
Unix
Windows
Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyes infolate-bound, schema-on-read capabilitiesyes
Typing infopredefined data types such as float or dateyesyesVector, Numeric and Stringno
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.nonono
Secondary indexesyesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageyesnoSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
JDBCRESTful HTTP APIProprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C++
Go
Java
JavaScript (Node.js)
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesuser defined functionsnono
Triggersyesnonono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual ConsistencyBounded 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 datanoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes
User concepts infoAccess controlyesAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyRole based access control and fine grained access rightsno
More information provided by the system vendor
Apache IoTDBApache PhoenixMilvusSphinx
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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Apache IoTDBApache PhoenixMilvusSphinx
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