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DBMS > LeanXcale vs. Solr vs. Vitess

System Properties Comparison LeanXcale vs. Solr vs. Vitess

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Editorial information provided by DB-Engines
NameLeanXcale  Xexclude from comparisonSolr  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesA widely used distributed, scalable search engine based on Apache LuceneScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelKey-value store
Relational DBMS
Search engineRelational DBMS
Secondary database modelsSpatial DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websitewww.leanxcale.comsolr.apache.orgvitess.io
Technical documentationsolr.apache.org/­resources.htmlvitess.io/­docs
DeveloperLeanXcaleApache Software FoundationThe Linux Foundation, PlanetScale
Initial release201520062013
Current release9.6.0, April 202415.0.2, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaGo
Server operating systemsAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Docker
Linux
macOS
Data schemeyesyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyes infosupports customizable data types and automatic typingyes
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.yes
Secondary indexesyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLyes infothrough Apache DerbySolr Parallel SQL Interfaceyes infowith proprietary extensions
APIs and other access methodsJDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
Java API
RESTful HTTP/JSON API
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesC
Java
Scala
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresJava pluginsyes infoproprietary syntax
Triggersyes infoUser configurable commands triggered on index changesyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integrityyesnoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDoptimistic lockingACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes
User concepts infoAccess controlyesUsers with fine-grained authorization concept infono user groups or roles

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More resources
LeanXcaleSolrVitess
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Massively Scaling MySQL Using Vitess
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