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DBMS > EXASOL vs. HugeGraph vs. IRONdb vs. Milvus vs. Solr

System Properties Comparison EXASOL vs. HugeGraph vs. IRONdb vs. Milvus vs. Solr

Editorial information provided by DB-Engines
NameEXASOL  Xexclude from comparisonHugeGraph  Xexclude from comparisonIRONdb  Xexclude from comparisonMilvus  Xexclude from comparisonSolr  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.A fast-speed and highly-scalable Graph DBMSA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityA DBMS designed for efficient storage of vector data and vector similarity searchesA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSGraph DBMSTime Series DBMSVector DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.76
Rank#139  Overall
#62  Relational DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score2.78
Rank#103  Overall
#3  Vector DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websitewww.exasol.comgithub.com/­hugegraph
hugegraph.apache.org
www.circonus.com/solutions/time-series-database/milvus.iosolr.apache.org
Technical documentationwww.exasol.com/­resourceshugegraph.apache.org/­docsdocs.circonus.com/irondb/category/getting-startedmilvus.io/­docs/­overview.mdsolr.apache.org/­resources.html
DeveloperExasolBaiduCirconus LLC.Apache Software Foundation
Initial release20002018201720192006
Current release0.9V0.10.20, January 20182.3.4, January 20249.6.1, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonononono
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 and C++C++, GoJava
Server operating systemsLinux
macOS
Unix
LinuxLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyes infotext, numeric, histogramsVector, Numeric and Stringyes infosupports customizable data types and automatic typing
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.nonononoyes
Secondary indexesyesyes infoalso supports composite index and range indexnonoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesnoSQL-like query language (Circonus Analytics Query Language: CAQL)noSolr Parallel SQL Interface
APIs and other access methods.Net
JDBC
ODBC
WebSocket
Java API
RESTful HTTP API
TinkerPop Gremlin
HTTP APIRESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languagesJava
Lua
Python
R
Groovy
Java
Python
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functionsasynchronous Gremlin script jobsyes, in LuanoJava plugins
Triggersyesnononoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseAutomatic, metric affinity per nodeShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseconfigurable replication factor, datacenter awareyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoHadoop integrationvia hugegraph-sparknonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate consistency per node, eventual consistency across nodesBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual Consistency
Foreign keys infoReferential integrityyesyes infoedges in graphnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnonooptimistic locking
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.yesyesnoyesyes
User concepts infoAccess controlAccess rights for users, groups and roles according to SQL-standardUsers, roles and permissionsnoRole based access control and fine grained access rightsyes
More information provided by the system vendor
EXASOLHugeGraphIRONdbMilvusSolr
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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EXASOLHugeGraphIRONdbMilvusSolr
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